From e52d46397576d4e065a51200dcd074e849a12c99 Mon Sep 17 00:00:00 2001 From: "James N." Date: Tue, 17 Feb 2026 12:02:04 -0800 Subject: [PATCH 01/37] update fraud_detection_durable to feb 12 agent-framework --- .../fraud_analysis_workflow.py | 35 +- .../fraud_detection_durable/pyproject.toml | 2 +- .../workflow/fraud_detection_durable/uv.lock | 426 +++++++++++++++++- .../fraud_detection_durable/worker.py | 21 +- 4 files changed, 438 insertions(+), 46 deletions(-) diff --git a/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py b/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py index 7557707da..475c4a66f 100644 --- a/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py +++ b/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py @@ -18,7 +18,7 @@ from pydantic import BaseModel from agent_framework import ( - ChatAgent, + Agent, Executor, handler, WorkflowBuilder, @@ -197,14 +197,14 @@ def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChat allowed_tools = ["get_customer_detail", "get_subscription_detail", "get_data_usage", "search_knowledge_base"] filtered_functions = [func for func in mcp_tool.functions if func.name in allowed_tools] - self._agent = ChatAgent( - chat_client=chat_client, - name="UsagePatternAnalyst", + self._agent = Agent( + client=chat_client, instructions=( "You are a specialist in analyzing customer data usage patterns. " "Look for anomalies like sudden spikes, unusual hours, or patterns inconsistent with history. " "Use the available tools to gather data and provide a risk score (0.0-1.0) and findings." ), + name="UsagePatternAnalyst", tools=filtered_functions, ) @@ -270,14 +270,14 @@ def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChat allowed_tools = ["get_customer_detail", "get_security_logs", "search_knowledge_base"] filtered_functions = [func for func in mcp_tool.functions if func.name in allowed_tools] - self._agent = ChatAgent( - chat_client=chat_client, - name="LocationAnalysisAgent", + self._agent = Agent( + client=chat_client, instructions=( "You are a specialist in analyzing geolocation and security patterns. " "Look for impossible travel, VPN usage, or login anomalies. " "Use the available tools to gather data and provide a risk score (0.0-1.0) and findings." ), + name="LocationAnalysisAgent", tools=filtered_functions, ) @@ -343,14 +343,14 @@ def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChat "get_customer_orders", "search_knowledge_base"] filtered_functions = [func for func in mcp_tool.functions if func.name in allowed_tools] - self._agent = ChatAgent( - chat_client=chat_client, - name="BillingChargeAnalyst", + self._agent = Agent( + client=chat_client, instructions=( "You are a specialist in analyzing billing and charge patterns. " "Look for unusual purchases, subscription changes, or payment anomalies. " "Use the available tools to gather data and provide a risk score (0.0-1.0) and findings." ), + name="BillingChargeAnalyst", tools=filtered_functions, ) @@ -446,14 +446,14 @@ async def handle_results( logger.info(f"[Aggregator] Aggregating results for {alert_id}") # Use LLM to synthesize findings - agent = ChatAgent( - chat_client=self._chat_client, - name="FraudRiskAggregator", + agent = Agent( + client=self._chat_client, instructions=( "You are a senior fraud analyst synthesizing findings from specialist agents. " "Weigh the evidence, calculate an overall risk score, and recommend an action. " "Be thorough but decisive. Return a JSON response with the assessment." ), + name="FraudRiskAggregator", ) prompt = f""" @@ -583,10 +583,7 @@ def create_fraud_analysis_workflow( aggregator = FraudRiskAggregatorExecutor(chat_client) # Build workflow topology - builder = WorkflowBuilder() - - # Set entry point - builder.set_start_executor(alert_router) + builder = WorkflowBuilder(start_executor=alert_router) # Fan-out: Alert Router → 3 Specialists builder.add_edge(alert_router, usage_executor) @@ -648,9 +645,9 @@ async def main(): print(f"{'='*60}\n") # Run workflow - async for event in workflow.run_stream(alert): + async for event in workflow.run(alert, stream=True): print(f"Event: {type(event).__name__}") - if hasattr(event, 'data') and isinstance(event.data, FraudRiskAssessment): + if event.type == "output" and isinstance(event.data, FraudRiskAssessment): assessment = event.data print(f"\n{'='*60}") print("FRAUD RISK ASSESSMENT") diff --git a/agentic_ai/workflow/fraud_detection_durable/pyproject.toml 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23591 }, +] + [[package]] name = "yarl" version = "1.22.0" diff --git a/agentic_ai/workflow/fraud_detection_durable/worker.py b/agentic_ai/workflow/fraud_detection_durable/worker.py index e88880382..29d041f90 100644 --- a/agentic_ai/workflow/fraud_detection_durable/worker.py +++ b/agentic_ai/workflow/fraud_detection_durable/worker.py @@ -52,7 +52,7 @@ from durabletask.task import ActivityContext, OrchestrationContext, Task, when_any, when_all from pydantic import BaseModel, ValidationError -from agent_framework import MCPStreamableHTTPTool, WorkflowOutputEvent +from agent_framework import MCPStreamableHTTPTool, WorkflowEvent from agent_framework.azure import AzureOpenAIChatClient from fraud_analysis_workflow import ( @@ -110,6 +110,7 @@ class ActionResult(BaseModel): _mcp_tool: MCPStreamableHTTPTool | None = None _chat_client: AzureOpenAIChatClient | None = None +_main_loop: asyncio.AbstractEventLoop | None = None async def _ensure_resources(): @@ -170,8 +171,8 @@ async def _run_workflow(): # Run workflow and collect output assessment: FraudRiskAssessment | None = None - async for event in workflow.run_stream(alert): - if isinstance(event, WorkflowOutputEvent): + async for event in workflow.run(alert, stream=True): + if isinstance(event, WorkflowEvent) and event.type == "output": if isinstance(event.data, FraudRiskAssessment): assessment = event.data break @@ -185,8 +186,15 @@ async def _run_workflow(): return assessment_dict - # Run the async workflow synchronously - result = asyncio.run(_run_workflow()) + # Run the async workflow on the main event loop (where MCP tool was initialized) + if _main_loop is None or _main_loop.is_closed(): + raise RuntimeError("Main event loop not available. Was the worker started correctly?") + try: + future = asyncio.run_coroutine_threadsafe(_run_workflow(), _main_loop) + result = future.result(timeout=300) # 5 minute timeout + except Exception as e: + logger.error(f"[Activity] run_fraud_analysis FAILED: {type(e).__name__}: {e}", exc_info=True) + raise logger.info(f"[Activity] run_fraud_analysis completed, risk_score={result.get('overall_risk_score')}") return result @@ -577,6 +585,9 @@ def setup_worker(worker: DurableTaskSchedulerWorker) -> None: async def main(): """Main entry point for the worker process.""" + global _main_loop + _main_loop = asyncio.get_running_loop() + logger.info("="*60) logger.info("Starting Durable Fraud Detection Worker") logger.info("="*60) From 91369f9d14a93ffcd12fba0c3b0a5c8fee0e7522 Mon Sep 17 00:00:00 2001 From: "James N." Date: Wed, 18 Feb 2026 18:41:57 -0800 Subject: [PATCH 02/37] enhance fraud detection durable --- .vite/deps/_metadata.json | 8 + .vite/deps/package.json | 3 + agentic_ai/observability/setup.py | 22 +- .../workflow/fraud_detection/.env.sample | 12 - .../workflow/fraud_detection/ARCHITECTURE.md | 895 --- .../workflow/fraud_detection/DIAGRAMS.md | 420 -- .../fraud_detection/IMPLEMENTATION.md | 434 -- .../workflow/fraud_detection/QUICKSTART.md | 263 - agentic_ai/workflow/fraud_detection/README.md | 407 -- .../workflow/fraud_detection/backend.py | 1176 ---- .../fraud_detection_workflow.py | 1195 ---- .../workflow/fraud_detection/pyproject.toml | 25 - .../workflow/fraud_detection/scenario.md | 29 - .../fraud_detection/start_backend.bat | 17 - .../workflow/fraud_detection/ui/.dockerignore | 30 - .../workflow/fraud_detection/ui/.env.example | 6 - .../workflow/fraud_detection/ui/.eslintrc.cjs | 22 - .../workflow/fraud_detection/ui/.gitignore | 31 - .../fraud_detection/ui/.prettierignore | 7 - .../fraud_detection/ui/.prettierrc.cjs | 14 - .../ui/.vscode/extensions.json | 7 - .../workflow/fraud_detection/ui/Dockerfile | 57 - .../workflow/fraud_detection/ui/README.md | 247 - .../workflow/fraud_detection/ui/index.html | 16 - .../fraud_detection/ui/package-lock.json | 5909 ----------------- .../workflow/fraud_detection/ui/package.json | 30 - .../workflow/fraud_detection/ui/src/App.jsx | 231 - .../src/components/AnalystDecisionPanel.jsx | 179 - .../ui/src/components/ControlPanel.jsx | 128 - .../ui/src/components/CustomNode.jsx | 81 - .../ui/src/components/EventLog.jsx | 121 - .../ui/src/components/WorkflowVisualizer.jsx | 151 - .../ui/src/constants/config.js | 33 - .../ui/src/constants/workflow.js | 53 - .../ui/src/hooks/useWebSocket.js | 113 - .../workflow/fraud_detection/ui/src/index.css | 18 - .../workflow/fraud_detection/ui/src/main.jsx | 18 - .../fraud_detection/ui/src/theme/index.js | 84 - .../fraud_detection/ui/src/utils/api.js | 73 - .../fraud_detection/ui/src/utils/helpers.js | 66 - .../ui/src/utils/uiHelpers.jsx | 189 - .../fraud_detection/ui/vite.config.js | 34 - agentic_ai/workflow/fraud_detection/uv.lock | 3039 --------- .../PRODUCTION_ARCHITECTURE.md | 333 + .../fraud_detection_durable/README.md | 773 ++- .../fraud_detection_durable/backend.py | 145 +- .../fraud_detection_durable/event_producer.py | 395 ++ .../fraud_detection_durable/provision_dts.ps1 | 193 + .../fraud_detection_durable/pyproject.toml | 3 + .../fraud_detection_durable/ui/src/App.jsx | 68 +- .../src/components/AnalystDecisionPanel.jsx | 131 +- .../ui/src/components/EventFeed.jsx | 338 + .../ui/src/constants/config.js | 5 +- .../ui/src/constants/workflow.js | 8 + .../fraud_detection_durable/ui/vite.config.js | 12 + .../workflow/fraud_detection_durable/uv.lock | 8 +- .../fraud_detection_durable/worker.py | 849 ++- mcp/mcp_service.py | 67 +- 58 files changed, 2629 insertions(+), 16592 deletions(-) create mode 100644 .vite/deps/_metadata.json create mode 100644 .vite/deps/package.json delete mode 100644 agentic_ai/workflow/fraud_detection/.env.sample delete mode 100644 agentic_ai/workflow/fraud_detection/ARCHITECTURE.md delete mode 100644 agentic_ai/workflow/fraud_detection/DIAGRAMS.md delete mode 100644 agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md delete mode 100644 agentic_ai/workflow/fraud_detection/QUICKSTART.md delete mode 100644 agentic_ai/workflow/fraud_detection/README.md delete mode 100644 agentic_ai/workflow/fraud_detection/backend.py delete mode 100644 agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py delete mode 100644 agentic_ai/workflow/fraud_detection/pyproject.toml delete mode 100644 agentic_ai/workflow/fraud_detection/scenario.md delete mode 100644 agentic_ai/workflow/fraud_detection/start_backend.bat delete mode 100644 agentic_ai/workflow/fraud_detection/ui/.dockerignore delete mode 100644 agentic_ai/workflow/fraud_detection/ui/.env.example delete mode 100644 agentic_ai/workflow/fraud_detection/ui/.eslintrc.cjs delete mode 100644 agentic_ai/workflow/fraud_detection/ui/.gitignore delete mode 100644 agentic_ai/workflow/fraud_detection/ui/.prettierignore delete mode 100644 agentic_ai/workflow/fraud_detection/ui/.prettierrc.cjs delete mode 100644 agentic_ai/workflow/fraud_detection/ui/.vscode/extensions.json delete mode 100644 agentic_ai/workflow/fraud_detection/ui/Dockerfile delete mode 100644 agentic_ai/workflow/fraud_detection/ui/README.md delete mode 100644 agentic_ai/workflow/fraud_detection/ui/index.html delete mode 100644 agentic_ai/workflow/fraud_detection/ui/package-lock.json delete mode 100644 agentic_ai/workflow/fraud_detection/ui/package.json delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/App.jsx delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/components/ControlPanel.jsx delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/components/CustomNode.jsx delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/components/EventLog.jsx delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/constants/config.js delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/constants/workflow.js delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/index.css delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/main.jsx delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/theme/index.js delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/utils/api.js delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/utils/helpers.js delete mode 100644 agentic_ai/workflow/fraud_detection/ui/src/utils/uiHelpers.jsx delete mode 100644 agentic_ai/workflow/fraud_detection/ui/vite.config.js delete mode 100644 agentic_ai/workflow/fraud_detection/uv.lock create mode 100644 agentic_ai/workflow/fraud_detection_durable/PRODUCTION_ARCHITECTURE.md create mode 100644 agentic_ai/workflow/fraud_detection_durable/event_producer.py create mode 100644 agentic_ai/workflow/fraud_detection_durable/provision_dts.ps1 create mode 100644 agentic_ai/workflow/fraud_detection_durable/ui/src/components/EventFeed.jsx diff --git a/.vite/deps/_metadata.json b/.vite/deps/_metadata.json new file mode 100644 index 000000000..d96c294f3 --- /dev/null +++ b/.vite/deps/_metadata.json @@ -0,0 +1,8 @@ +{ + "hash": "2f2146ce", + "configHash": "c3c8e9de", + "lockfileHash": "e3b0c442", + "browserHash": "d7d98e11", + "optimized": {}, + "chunks": {} +} \ No newline at end of file diff --git a/.vite/deps/package.json b/.vite/deps/package.json new file mode 100644 index 000000000..3dbc1ca59 --- /dev/null +++ b/.vite/deps/package.json @@ -0,0 +1,3 @@ +{ + "type": "module" +} diff --git a/agentic_ai/observability/setup.py b/agentic_ai/observability/setup.py index e2cbe063c..6efe21bc4 100644 --- a/agentic_ai/observability/setup.py +++ b/agentic_ai/observability/setup.py @@ -75,6 +75,19 @@ def setup_observability( # Enable Agent Framework instrumentation enable_instrumentation(enable_sensitive_data=enable_sensitive_data) + # Workaround: agent_framework._tools.py calls + # model_dump_json(ensure_ascii=False), but Pydantic v2 doesn't + # support that kwarg, causing every tool call to fail with + # "Function failed". Monkeypatch BaseModel.model_dump_json to + # silently strip unsupported kwargs so observability doesn't + # break tool execution. Remove once the library is fixed. + import pydantic + _orig_mdj = pydantic.BaseModel.model_dump_json + def _safe_model_dump_json(self, **kwargs): + kwargs.pop("ensure_ascii", None) + return _orig_mdj(self, **kwargs) + pydantic.BaseModel.model_dump_json = _safe_model_dump_json # type: ignore[assignment] + _initialized = True print(f"✅ Application Insights observability enabled (service: {service_name})") logger.info(f"✅ Application Insights observability enabled (service: {service_name})") @@ -84,9 +97,12 @@ def setup_observability( print(f"❌ Observability dependencies not installed: {e}") logger.warning(f"Observability dependencies not installed: {e}") return False - except Exception as e: - print(f"❌ Failed to configure observability: {e}") - logger.warning(f"Failed to configure observability: {e}") + except BaseException as e: + # Catch ALL errors including KeyboardInterrupt from import deadlocks + # in azure-ai-projects telemetry instrumentor (openai SDK version conflicts). + # Observability is never worth crashing the service. + print(f"⚠️ Observability setup failed (non-fatal): {type(e).__name__}: {e}") + logger.warning(f"Observability setup failed (non-fatal): {type(e).__name__}: {e}") return False diff --git a/agentic_ai/workflow/fraud_detection/.env.sample b/agentic_ai/workflow/fraud_detection/.env.sample deleted file mode 100644 index 5d9f9deb8..000000000 --- a/agentic_ai/workflow/fraud_detection/.env.sample +++ /dev/null @@ -1,12 +0,0 @@ -# Azure OpenAI Configuration -AZURE_OPENAI_API_KEY=your-azure-openai-api-key-here -AZURE_OPENAI_CHAT_DEPLOYMENT=your-deployment-name -AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/ -AZURE_OPENAI_API_VERSION=2024-10-01-preview - -# MCP Server Configuration -MCP_SERVER_URI=http://localhost:8000/mcp - -# Application Insights (optional - enables telemetry) -# APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=... -# ENABLE_SENSITIVE_DATA=true diff --git a/agentic_ai/workflow/fraud_detection/ARCHITECTURE.md b/agentic_ai/workflow/fraud_detection/ARCHITECTURE.md deleted file mode 100644 index a7a1c6b9f..000000000 --- a/agentic_ai/workflow/fraud_detection/ARCHITECTURE.md +++ /dev/null @@ -1,895 +0,0 @@ -# Fraud Detection Workflow - End-to-End System Architecture - -## Overview - -The Fraud Detection Workflow is a real-time, multi-agent AI system that analyzes suspicious activities, performs risk assessments, and enables human-in-the-loop decision-making. The system combines Azure OpenAI agents, Model Context Protocol (MCP) tools, and a React-based visualization interface. - ---- - -## High-Level Architecture - -``` -┌─────────────────────────────────────────────────────────────────────────────┐ -│ USER INTERFACE LAYER │ -│ │ -│ ┌────────────────────────────────────────────────────────────────────┐ │ -│ │ React Frontend (Vite) │ │ -│ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │ │ -│ │ │ Workflow │ │ Analyst │ │ Event Log │ │ │ -│ │ │ Control │ │ Decision │ │ (Real-time Stream) │ │ │ -│ │ │ Panel │ │ Panel │ │ │ │ │ -│ │ └──────────────┘ └──────────────┘ └──────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ React Flow - Workflow Visualization │ │ │ -│ │ │ (Interactive DAG with real-time executor state updates) │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └────────────────────────────────────────────────────────────────────┘ │ -│ │ │ -│ HTTP REST API │ WebSocket (Event Stream) │ -└────────────────────────────────────┼────────────────────────────────────────┘ - │ -┌────────────────────────────────────┼────────────────────────────────────────┐ -│ APPLICATION LAYER │ -│ │ │ -│ ┌────────────────────────────────▼────────────────────────────────────┐ │ -│ │ FastAPI Backend Server │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ REST API Endpoints │ │ │ -│ │ │ • POST /api/workflow/start - Start new workflow │ │ │ -│ │ │ • POST /api/workflow/decision - Submit analyst decision │ │ │ -│ │ │ • GET /api/workflow/status - Get workflow status │ │ │ -│ │ │ • GET /api/alerts - Get sample alerts │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ WebSocket Manager │ │ │ -│ │ │ • Real-time event broadcasting to all connected clients │ │ │ -│ │ │ • Connection lifecycle management │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ Workflow Orchestration │ │ │ -│ │ │ • Workflow initialization & execution │ │ │ -│ │ │ • Event streaming & processing │ │ │ -│ │ │ • Human-in-the-loop coordination │ │ │ -│ │ │ • Checkpoint management for resume capability │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └──────────────────────────────────────────────────────────────────────┘ │ -│ │ │ -└────────────────────────────────────┼────────────────────────────────────────┘ - │ -┌────────────────────────────────────┼────────────────────────────────────────┐ -│ AGENT FRAMEWORK LAYER │ -│ │ │ -│ ┌────────────────────────────────▼────────────────────────────────────┐ │ -│ │ Microsoft Agent Framework (agent-framework) │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ Workflow Engine │ │ │ -│ │ │ • DAG-based execution (Directed Acyclic Graph) │ │ │ -│ │ │ • State management & transitions │ │ │ -│ │ │ • Checkpointing & resumability │ │ │ -│ │ │ • Event emission (ExecutorInvoked, ExecutorCompleted, etc.) │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ Executor Nodes (Agents) │ │ │ -│ │ │ │ │ │ -│ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ -│ │ │ │ Alert │────▶│ Usage │────▶│ Risk │ │ │ │ -│ │ │ │ Router │ │ Pattern │ │ Aggregator │ │ │ │ -│ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ -│ │ │ │ │ │ -│ │ │ ┌─────────────┐ ┌─────────────┐ ┌──────┐ │ │ │ -│ │ │ │ Location │────▶│ Analyst │───────────▶│ Auto │ │ │ │ -│ │ │ │ Analysis │ │ Review │ │ Clear│ │ │ │ -│ │ │ └─────────────┘ └─────────────┘ └──────┘ │ │ │ -│ │ │ (Human-in-Loop) │ │ │ │ -│ │ │ ┌─────────────┐ │ │ │ │ -│ │ │ │ Billing │───────────────────────────┬─────────┘ │ │ │ -│ │ │ │ Charge │ │ │ │ │ -│ │ │ └─────────────┘ ▼ │ │ │ -│ │ │ ┌─────────────┐ │ │ │ -│ │ │ │ Fraud │ │ │ │ -│ │ │ │ Action │ │ │ │ -│ │ │ └─────────────┘ │ │ │ -│ │ │ │ │ │ │ -│ │ │ ▼ │ │ │ -│ │ │ ┌─────────────┐ │ │ │ -│ │ │ │ Final │ │ │ │ -│ │ │ │ Notification│ │ │ │ -│ │ │ └─────────────┘ │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ RequestInfoExecutor (Human-in-the-Loop) │ │ │ -│ │ │ • Pauses workflow execution │ │ │ -│ │ │ • Creates checkpoint with current state │ │ │ -│ │ │ • Waits for external decision (analyst input) │ │ │ -│ │ │ • Resumes from checkpoint with decision │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └──────────────────────────────────────────────────────────────────────┘ │ -│ │ │ -└────────────────────────────────────┼────────────────────────────────────────┘ - │ -┌────────────────────────────────────┼────────────────────────────────────────┐ -│ AI & TOOLS LAYER │ -│ │ │ -│ ┌────────────────────────────────▼────────────────────────────────────┐ │ -│ │ Azure OpenAI Service │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ GPT-4 Model (Chat Completion) │ │ │ -│ │ │ • Agent reasoning & decision-making │ │ │ -│ │ │ • Natural language analysis │ │ │ -│ │ │ • Risk assessment generation │ │ │ -│ │ │ • Tool calling (function calling) │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └──────────────────────────────────────────────────────────────────────┘ │ -│ │ │ -│ ┌────────────────────────────────▼────────────────────────────────────┐ │ -│ │ Model Context Protocol (MCP) Server │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ FastMCP Server (HTTP/SSE) │ │ │ -│ │ │ • Exposes tools via standardized MCP protocol │ │ │ -│ │ │ • Handles tool invocation requests │ │ │ -│ │ │ • Returns structured tool responses │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ MCP Tools (Simulated Customer Service System) │ │ │ -│ │ │ │ │ │ -│ │ │ Customer Tools: Subscription Tools: │ │ │ -│ │ │ • get_all_customers() • get_subscription_detail()│ │ │ -│ │ │ • get_customer_detail() • update_subscription() │ │ │ -│ │ │ • get_customer_orders() • get_data_usage() │ │ │ -│ │ │ • unlock_account() │ │ │ -│ │ │ │ │ │ -│ │ │ Billing Tools: Support Tools: │ │ │ -│ │ │ • get_billing_summary() • get_support_tickets() │ │ │ -│ │ │ • pay_invoice() • create_support_ticket() │ │ │ -│ │ │ • get_invoice_payments() │ │ │ -│ │ │ │ │ │ -│ │ │ Knowledge Base: │ │ │ -│ │ │ • search_knowledge_base() - Semantic search on policies │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └──────────────────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────────────────┘ - │ -┌────────────────────────────────────┼────────────────────────────────────────┐ -│ DATA & STORAGE LAYER │ -│ │ │ -│ ┌────────────────────────────────▼────────────────────────────────────┐ │ -│ │ File-Based Storage │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ Checkpoint Storage (./checkpoints/) │ │ │ -│ │ │ • Workflow state snapshots │ │ │ -│ │ │ • Executor states at pause points │ │ │ -│ │ │ • Pending request information │ │ │ -│ │ │ • JSON format, UUID-based filenames │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ Simulated Database (In-Memory) │ │ │ -│ │ │ • Customer records │ │ │ -│ │ │ • Subscription data │ │ │ -│ │ │ • Invoice & payment history │ │ │ -│ │ │ • Support tickets │ │ │ -│ │ │ • Security logs │ │ │ -│ │ │ (Generated with Faker library for demo purposes) │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └──────────────────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────────────────┘ -``` - ---- - -## Component Details - -### 1. Frontend Layer (React + Vite) - -**Technology Stack:** -- React 18+ with Hooks -- Material-UI (MUI) for components -- React Flow for workflow visualization -- WebSocket for real-time updates -- Vite for fast development builds - -**Key Components:** - -#### 1.1 Workflow Control Panel -- Displays available alerts (from backend) -- Alert selection dropdown with severity indicators -- "Start Workflow" button with loading state -- Shows active workflow status - -#### 1.2 Analyst Decision Panel -- Appears when `decision_required` event received -- Shows risk assessment & AI analysis -- Dropdown for decision selection -- Text area for analyst notes -- "Submit Decision" button -- Compact layout optimized for visibility - -#### 1.3 Workflow Graph Visualizer -- Interactive DAG visualization using React Flow -- Real-time executor state updates: - - `idle` - Gray, not yet executed - - `running` - Blue, currently executing - - `completed` - Green, finished successfully -- Shows workflow structure and data flow -- Zoom, pan, and fit-to-screen controls - -#### 1.4 Event Log Panel -- Real-time event stream display -- Color-coded event types -- Timestamps and executor tracking -- Auto-scroll to latest events -- Custom scrollbar with overflow handling -- Deduplication to prevent duplicate events - -**Communication Protocols:** -- REST API for control operations (start workflow, submit decisions) -- WebSocket for event streaming (bi-directional, persistent connection) - ---- - -### 2. Backend Layer (FastAPI) - -**Technology Stack:** -- FastAPI for async REST API -- WebSocket support for real-time events -- Python 3.12+ with async/await -- Pydantic for data validation - -**Key Modules:** - -#### 2.1 REST API Endpoints - -```python -POST /api/workflow/start - • Input: SuspiciousActivityAlert - • Creates workflow instance - • Returns: { status, alert_id, message } - • Spawns async workflow execution task - -GET /api/alerts - • Returns sample alerts for testing - • Includes: alert_id, customer_id, alert_type, severity - -POST /api/workflow/decision - • Input: AnalystDecisionRequest - • Resolves checkpoint for resume - • Continues workflow with analyst's decision - • Returns: { status, message } - -GET /api/workflow/status/{alert_id} - • Returns current workflow state - • Includes: status, current_executor, events, pending_decision -``` - -#### 2.2 WebSocket Manager - -**Connection Lifecycle:** -```python -1. Client connects → Accept WebSocket -2. Add to active_connections list -3. Stream events via broadcast() -4. On disconnect → Remove from list -``` - -**Event Broadcasting:** -- Sends JSON messages to all connected clients -- Handles disconnections gracefully -- No message queuing (real-time only) - -**Event Types:** -- `workflow_initializing` - Setup in progress -- `workflow_started` - Execution beginning -- `executor_invoked` - Agent starting work -- `executor_completed` - Agent finished -- `decision_required` - Human input needed -- `workflow_completed` - All done -- `workflow_error` - Error occurred - -#### 2.3 Workflow Orchestration - -**Initialization Phase:** -```python -async def run_workflow(alert): - 1. Create workflow instance (pre-initialized resources) - 2. Send progress updates during setup - 3. Store workflow reference & instance - 4. Stream events via workflow.run_stream() - 5. Handle RequestInfoEvent → pause for analyst -``` - -**Resume Phase:** -```python -async def continue_workflow(alert_id, responses, checkpoint_id): - 1. Create NEW workflow instance - 2. Load checkpoint state - 3. Resume with workflow.run_stream_from_checkpoint() - 4. Provide analyst responses via responses dict - 5. Continue streaming events -``` - -**Checkpoint Resolution:** -- Retries up to 20 times with exponential backoff -- Checkpoints written AFTER superstep completion -- Maps request_id to checkpoint_id for resume - ---- - -### 3. Agent Framework Layer - -**Technology Stack:** -- Microsoft Agent Framework (`agent-framework`) -- DAG-based workflow engine -- State machine with transitions -- Checkpoint-based resumability - -**Architecture Pattern:** -- **Directed Acyclic Graph (DAG)** for workflow structure -- **Executor Nodes** as individual agents/tasks -- **Edges** define dependencies and data flow -- **State Management** tracks execution progress - -#### 3.1 Workflow Engine - -**Execution Model:** -``` -SuperStep Cycle: -1. Identify ready executors (dependencies met) -2. Execute executors in parallel -3. Collect outputs -4. Update shared state -5. Write checkpoint -6. Repeat until terminal state -``` - -**Event System:** -```python -- ExecutorInvokedEvent → Executor starting -- ExecutorCompletedEvent → Executor finished -- WorkflowStatusEvent → State transition -- RequestInfoEvent → Human input needed -- WorkflowOutputEvent → Final result -``` - -**Checkpointing:** -- Snapshot of entire workflow state -- Includes: executor states, shared state, pending requests -- File-based storage (JSON) -- Required for human-in-the-loop resume - -#### 3.2 Executor Nodes (Agents) - -**Executor Types:** - -1. **AlertRouter** - Entry point, routes alert to analysis -2. **UsagePatternExecutor** - Analyzes data usage patterns -3. **LocationAnalysisExecutor** - Analyzes login locations -4. **BillingChargeExecutor** - Checks billing anomalies -5. **RiskAggregator** - Combines analyses, calculates risk score -6. **ReviewGateway** - Decides: auto-clear or analyst review -7. **AutoClear** - Handles low-risk alerts automatically -8. **RequestInfoExecutor (AnalystReview)** - Human-in-the-loop -9. **FraudAction** - Executes approved action (lock/refund/etc.) -10. **FinalNotification** - Logs completion and notifies - -**Executor Pattern:** -```python -class CustomExecutor(Executor): - async def execute(self, input_data, shared_state): - # Access MCP tools - tool_result = await self.mcp_tool.call_tool(...) - - # LLM reasoning - response = await self.chat_client.chat(...) - - # Update state - return output_data -``` - -#### 3.3 RequestInfoExecutor (Human-in-the-Loop) - -**Mechanism:** -```python -1. Workflow reaches RequestInfoExecutor -2. Executor pauses (yields RequestInfoEvent) -3. Checkpoint created with pending request -4. Backend broadcasts decision_required to UI -5. Analyst provides decision via UI -6. Backend calls continue_workflow() with responses -7. Workflow resumes from checkpoint -8. RequestInfoExecutor returns analyst's response -9. Workflow continues execution -``` - -**Key Features:** -- Multiple pending requests supported -- Request ID mapping to checkpoint iteration -- Timeout handling (if needed) -- Request-response correlation - ---- - -### 4. AI & Tools Layer - -#### 4.1 Azure OpenAI Service - -**Model Configuration:** -```python -Deployment: gpt-4 (or gpt-4-turbo) -Authentication: Azure CLI Credential (DefaultAzureCredential) -Endpoint: Azure OpenAI resource endpoint -Features: - - Chat completion - - Function calling (tool calling) - - JSON mode responses -``` - -**Usage Pattern:** -```python -# Agent makes decision -response = await chat_client.chat( - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_message} - ], - tools=available_tools, - temperature=0.7 -) - -# LLM decides to call a tool -tool_call = response.tool_calls[0] -tool_result = await mcp_tool.call_tool(tool_call.name, args) - -# LLM processes result and continues -``` - -#### 4.2 Model Context Protocol (MCP) Server - -**Purpose:** -- Standardized protocol for tool exposure -- HTTP-based (FastMCP implementation) -- Server-Sent Events (SSE) for streaming -- Used by agents to access customer data - -**MCP Tool Integration:** -```python -# Backend initializes MCP client -mcp_tool = MCPStreamableHTTPTool( - name="contoso_mcp", - url="http://localhost:8000/mcp", - headers={"Content-Type": "application/json"} -) - -# Agent calls tool -result = await mcp_tool.call_tool( - "get_customer_detail", - {"params": {"customer_id": 123}} -) -``` - -**Tool Categories:** - -**Customer Management:** -- `get_all_customers()` - List all customers -- `get_customer_detail(customer_id)` - Full customer profile -- `get_customer_orders(customer_id)` - Order history -- `unlock_account(customer_id)` - Unlock locked account - -**Subscription Management:** -- `get_subscription_detail(subscription_id)` - Detailed subscription info -- `update_subscription(...)` - Modify subscription settings -- `get_data_usage(...)` - Data usage over time period - -**Billing:** -- `get_billing_summary(customer_id)` - Current balance -- `pay_invoice(invoice_id, amount)` - Record payment -- `get_invoice_payments(invoice_id)` - Payment history - -**Support & Security:** -- `get_support_tickets(customer_id)` - Ticket history -- `create_support_ticket(...)` - Create new ticket -- `get_security_logs(customer_id)` - Security events -- `search_knowledge_base(query)` - Semantic search policies - ---- - -### 5. Data & Storage Layer - -#### 5.1 Checkpoint Storage - -**Format:** -```json -{ - "checkpoint_id": "uuid-here", - "workflow_id": "workflow-uuid", - "timestamp": "2025-10-09T10:30:00Z", - "executor_states": { - "executor_id": { - "status": "completed", - "output": {...} - } - }, - "shared_state": { - "alert": {...}, - "risk_assessment": {...}, - "__pending_requests__": { - "request-id": {...} - } - } -} -``` - -**Location:** `./checkpoints/` -**Cleanup:** Deleted after workflow completion - -#### 5.2 Simulated Database - -**Implementation:** -- In-memory Python data structures -- Generated with Faker library -- Realistic customer scenarios - -**Data Models:** -```python -Customer: - - customer_id, name, email, phone - - account_status, loyalty_tier - - created_at, last_login - -Subscription: - - subscription_id, customer_id, product_id - - status, start_date, end_date - - data_cap_gb, speed_tier - - autopay_enabled, roaming_enabled - -Invoice: - - invoice_id, customer_id, subscription_id - - amount, due_date, status - - payments (linked) - -SupportTicket: - - ticket_id, customer_id, subscription_id - - category, priority, status - - subject, description -``` - ---- - -## Data Flow: Complete Request Cycle - -### Scenario: High-Risk Alert with Analyst Review - -``` -┌──────────────────────────────────────────────────────────────────────────┐ -│ PHASE 1: WORKFLOW START │ -└──────────────────────────────────────────────────────────────────────────┘ - -1. User selects "ALERT-001" in UI -2. UI → POST /api/workflow/start { alert_id: "ALERT-001", ... } -3. Backend: - - Creates workflow instance - - Spawns run_workflow(alert) task - - Returns immediately -4. Workflow Engine: - - Broadcasts: workflow_initializing - - Creates workflow graph - - Broadcasts: workflow_started -5. UI updates: Workflow Control shows "Running..." - -┌──────────────────────────────────────────────────────────────────────────┐ -│ PHASE 2: AGENT ANALYSIS (Automatic) │ -└──────────────────────────────────────────────────────────────────────────┘ - -6. AlertRouter executes: - - Broadcasts: executor_invoked (AlertRouter) - - Routes alert to analysis executors - - Broadcasts: executor_completed (AlertRouter) - -7. Analysis Executors (parallel): - A. UsagePatternExecutor: - - Calls MCP: get_data_usage() - - LLM analyzes: "500% spike is suspicious" - - Broadcasts: executor_completed - - B. LocationAnalysisExecutor: - - Calls MCP: get_security_logs() - - LLM analyzes: "USA to Russia in 2 hours - impossible travel" - - Broadcasts: executor_completed - - C. BillingChargeExecutor: - - Calls MCP: get_billing_summary() - - LLM analyzes: "No unusual charges" - - Broadcasts: executor_completed - -8. RiskAggregator executes: - - Combines all analysis results - - Calculates overall_risk_score: 0.88 - - Determines risk_level: "critical" - - Recommends: "lock_account" & "refund_charges" - - Broadcasts: executor_completed - -9. ReviewGateway executes: - - Evaluates: risk_score >= 0.6 → needs review - - Routes to: analyst_review (not auto_clear) - - Broadcasts: executor_completed - -┌──────────────────────────────────────────────────────────────────────────┐ -│ PHASE 3: HUMAN-IN-THE-LOOP (Pause & Wait) │ -└──────────────────────────────────────────────────────────────────────────┘ - -10. AnalystReview (RequestInfoExecutor) executes: - - Yields RequestInfoEvent - - Workflow pauses - - Checkpoint written to disk - -11. Backend processes RequestInfoEvent: - - Resolves checkpoint (retry with backoff) - - Stores pending_decisions[alert_id] - - Broadcasts: decision_required - - WebSocket → UI - -12. UI receives decision_required: - - Displays Analyst Decision Panel - - Shows: risk score, analysis, recommended action - - Workflow graph: "analyst_review" node shows "running" - -13. Analyst makes decision: - - Selects: "Lock Account & Refund" - - Adds notes: "Confirmed fraud - Russia login" - - Clicks "Submit Decision" - -14. UI → POST /api/workflow/decision - { - request_id: "req-123", - approved_action: "both", - analyst_notes: "Confirmed fraud..." - } - -┌──────────────────────────────────────────────────────────────────────────┐ -│ PHASE 4: WORKFLOW RESUME │ -└──────────────────────────────────────────────────────────────────────────┘ - -15. Backend continue_workflow(): - - Creates NEW workflow instance - - Loads checkpoint from disk - - Calls: workflow.run_stream_from_checkpoint( - checkpoint_id, - responses={req_id: AnalystDecision(...)} - ) - -16. Workflow Engine: - - Restores state from checkpoint - - Resumes at analyst_review executor - - RequestInfoExecutor returns analyst's decision - - Broadcasts: executor_completed (analyst_review) - -17. FraudAction executes: - - Input: approved_action = "both" - - Calls MCP: update_subscription(status="locked") - - Calls MCP: pay_invoice(refund_amount) - - Broadcasts: executor_completed - -18. FinalNotification executes: - - Calls MCP: create_support_ticket(...) - - Logs: "Alert ALERT-001 resolved - account locked & refunded" - - Broadcasts: executor_completed - -19. Workflow completes: - - Broadcasts: workflow_completed - - Cleanup checkpoints - - UI updates: All nodes green, workflow done - -┌──────────────────────────────────────────────────────────────────────────┐ -│ PHASE 5: UI UPDATE CYCLE (Throughout) │ -└──────────────────────────────────────────────────────────────────────────┘ - -WebSocket events → UI state updates: - -• executor_invoked → Node color: blue (running) -• executor_completed → Node color: green (completed) -• decision_required → Show Analyst Decision Panel -• workflow_completed → Reset UI, show success - -Event Log displays all events in real-time with timestamps -``` - ---- - -## Key Design Patterns - -### 1. Event-Driven Architecture -- **Pattern:** Observer pattern via WebSocket -- **Benefit:** Real-time UI updates without polling -- **Implementation:** ConnectionManager broadcasts to all clients - -### 2. Checkpoint-Resume Pattern -- **Pattern:** Memento pattern for workflow state -- **Benefit:** Enable human-in-the-loop without blocking -- **Implementation:** FileCheckpointStorage with JSON serialization - -### 3. Agent Pattern -- **Pattern:** Autonomous agents with tools -- **Benefit:** Modular, testable, extensible analysis -- **Implementation:** Executor nodes with LLM + MCP tools - -### 4. DAG-Based Orchestration -- **Pattern:** Workflow as directed acyclic graph -- **Benefit:** Parallel execution, clear dependencies -- **Implementation:** Agent Framework workflow engine - -### 5. Tool Abstraction (MCP) -- **Pattern:** Adapter pattern for external systems -- **Benefit:** Standardized tool interface, swap implementations -- **Implementation:** MCP protocol with HTTP transport - ---- - -## Scalability Considerations - -### Current Design (Single Server) -- **Concurrent Workflows:** Limited by FastAPI worker threads -- **State Storage:** File-based (not distributed) -- **WebSocket:** Single server, no load balancing - -### Production Enhancements - -#### 1. Distributed Workflow Execution -``` -Replace: FileCheckpointStorage -With: Azure Cosmos DB or Redis -Benefit: Multiple backend instances, shared state -``` - -#### 2. Message Queue for Events -``` -Add: Azure Service Bus or RabbitMQ -Pattern: Producer (workflow) → Queue → Consumer (WebSocket broadcaster) -Benefit: Decouple execution from UI updates -``` - -#### 3. Horizontal Scaling -``` -Add: Load balancer + multiple backend instances -Add: Redis for WebSocket connection state -Pattern: Sticky sessions or broadcast via Redis pub/sub -``` - -#### 4. Async Job Processing -``` -Add: Celery or Azure Functions -Pattern: Backend submits workflow jobs to queue -Workers execute workflows, emit events to message bus -``` - ---- - -## Security Considerations - -### Authentication & Authorization -- **Current:** Azure CLI credential (dev only) -- **Production:** Add user authentication (OAuth2, OIDC) -- **RBAC:** Analyst vs. Admin vs. Viewer roles - -### Data Protection -- **Encryption:** TLS for API & WebSocket -- **PII Handling:** Mask sensitive customer data in logs -- **Audit Trail:** Log all analyst decisions with timestamps - -### MCP Tool Security -- **API Keys:** Secure MCP server with authentication -- **Rate Limiting:** Prevent tool abuse -- **Input Validation:** Sanitize all tool parameters - ---- - -## Monitoring & Observability - -### Application Insights Integration -```python -from azure.monitor.opentelemetry import configure_azure_monitor - -configure_azure_monitor( - connection_string=os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING") -) -``` - -**Track:** -- Workflow execution times -- Analyst response times -- Tool call durations -- Error rates & exceptions -- WebSocket connection metrics - -### Logging Strategy -``` -INFO: Workflow lifecycle events -DEBUG: Tool calls & LLM prompts -ERROR: Exceptions with stack traces -WARN: Checkpoint resolution delays -``` - -### Tracing -- OpenTelemetry tracing built into agent-framework -- Correlate events across workflow → backend → MCP → LLM - ---- - -## Deployment Architecture - -### Development -``` -Local Machine: -├─ Frontend: npm run dev (Vite) → localhost:3000 -├─ Backend: uvicorn backend:app → localhost:8001 -└─ MCP Server: uvicorn mcp_service:app → localhost:8000 -``` - -### Production (Azure) - -``` -┌────────────────────────────────────────────────────────────┐ -│ Azure Front Door │ -│ (CDN + Load Balancer) │ -└─────────────────────┬──────────────────────────────────────┘ - │ - ┌─────────────┴─────────────┐ - │ │ - ▼ ▼ -┌───────────────┐ ┌───────────────┐ -│ Static Web │ │ Azure App │ -│ App │ │ Service │ -│ (React build) │ │ (FastAPI) │ -└───────────────┘ └───────┬───────┘ - │ - ┌──────────────┼──────────────┐ - │ │ │ - ▼ ▼ ▼ - ┌─────────────┐ ┌──────────┐ ┌──────────┐ - │ Azure │ │ Azure │ │ MCP │ - │ OpenAI │ │ Cosmos DB│ │ Server │ - └─────────────┘ └──────────┘ └──────────┘ - │ - ▼ - ┌──────────┐ - │ Azure SQL│ - │ Database │ - └──────────┘ -``` - ---- - -## Technology Summary - -| Layer | Technology | Purpose | -|-------|-----------|---------| -| **Frontend** | React 18 | UI framework | -| | Material-UI | Component library | -| | React Flow | Workflow visualization | -| | WebSocket API | Real-time events | -| | Vite | Build tool | -| **Backend** | FastAPI | REST API server | -| | WebSocket | Event streaming | -| | Uvicorn | ASGI server | -| | Pydantic | Data validation | -| **Agent Framework** | agent-framework | Workflow engine | -| | Azure OpenAI | LLM reasoning | -| | MCP Client | Tool invocation | -| **MCP Server** | FastMCP | MCP implementation | -| | Faker | Test data generation | -| **Storage** | File System | Checkpoints (dev) | -| | JSON | Checkpoint format | -| **AI** | Azure OpenAI | GPT-4 model | -| | Azure CLI Credential | Authentication | - ---- - -## Conclusion - -This architecture demonstrates a modern, event-driven AI agent system with: - -✅ **Real-time visualization** - WebSocket streaming for live updates -✅ **Human-in-the-loop** - Checkpoint-based pause/resume -✅ **Modular agents** - Each executor is independent and testable -✅ **Standardized tools** - MCP protocol for tool integration -✅ **Production-ready patterns** - Async/await, error handling, logging -✅ **Scalable design** - Ready for distributed deployment - -The system can handle complex fraud detection scenarios while providing full transparency and control to human analysts. diff --git a/agentic_ai/workflow/fraud_detection/DIAGRAMS.md b/agentic_ai/workflow/fraud_detection/DIAGRAMS.md deleted file mode 100644 index 0fb8daa11..000000000 --- a/agentic_ai/workflow/fraud_detection/DIAGRAMS.md +++ /dev/null @@ -1,420 +0,0 @@ -# Fraud Detection Workflow - Visual Diagrams - -## Complete Workflow Topology - -``` -┌─────────────────────────────────────────────────────────────────────────┐ -│ SUSPICIOUS ACTIVITY ALERT │ -│ │ -│ ┌────────────────────────────────────────────────────────────────┐ │ -│ │ Alert ID: ALERT-001 │ │ -│ │ Customer ID: 1 │ │ -│ │ Type: multi_country_login │ │ -│ │ Description: Login attempts from USA and Russia within 2 hours │ │ -│ │ Severity: high │ │ -│ └────────────────────────────────────────────────────────────────┘ │ -└───────────────────────────────┬──────────────────────────────────────────┘ - ↓ - ┌───────────────────────┐ - │ AlertRouter │ - │ Executor │ - │ │ - │ - Receives alert │ - │ - Routes to 3 │ - │ specialist agents │ - └───────────┬───────────┘ - ↓ - ┌──────────────────────┼──────────────────────┐ - ↓ ↓ ↓ -┌────────────────────┐ ┌────────────────────┐ ┌────────────────────┐ -│ UsagePattern │ │ LocationAnalysis │ │ BillingCharge │ -│ Executor │ │ Executor │ │ Executor │ -│ │ │ │ │ │ -│ MCP Tools: │ │ MCP Tools: │ │ MCP Tools: │ -│ • get_customer_ │ │ • get_customer_ │ │ • get_customer_ │ -│ detail │ │ detail │ │ detail │ -│ • get_subscription │ │ • get_security_ │ │ • get_billing_ │ -│ _detail │ │ logs │ │ summary │ -│ • get_data_usage │ │ • search_ │ │ • get_subscription │ -│ • search_ │ │ knowledge_base │ │ _detail │ -│ knowledge_base │ │ │ │ • get_customer_ │ -│ │ │ │ │ orders │ -│ ↓ │ │ ↓ │ │ ↓ │ -│ Risk Score: 0.7 │ │ Risk Score: 0.85 │ │ Risk Score: 0.4 │ -│ Indicators: │ │ Indicators: │ │ Indicators: │ -│ • Data spike │ │ • Impossible │ │ • Normal billing │ -│ • Off-peak usage │ │ travel │ │ pattern │ -└─────────┬──────────┘ └─────────┬──────────┘ └─────────┬──────────┘ - └──────────────────────┼──────────────────────┘ - ↓ - ┌────────────────────────┐ - │ FraudRiskAggregator │ - │ Executor (LLM Agent) │ - │ │ - │ Analyzes all findings │ - │ Calculates weighted │ - │ risk score │ - │ │ - │ Overall Risk: 0.65 │ - │ Level: high │ - │ Action: lock_account │ - └───────────┬────────────┘ - ↓ - ┌───────────────────────┐ - │ RISK SCORE SWITCH │ - │ │ - │ Score >= 0.6? │ - └───────────┬───────────┘ - ↓ - ┌─────────────────┴─────────────────┐ - ↓ ↓ - (YES - High Risk) (NO - Low Risk) - ↓ ↓ -┌────────────────────────┐ ┌────────────────────────┐ -│ RequestInfoExecutor │ │ AutoClearExecutor │ -│ (Human-in-the-Loop) │ │ │ -│ │ │ Automatically clears │ -│ ┌────────────────────┐ │ │ low-risk alerts │ -│ │ WORKFLOW PAUSED │ │ │ │ -│ │ │ │ │ Creates ActionResult: │ -│ │ Checkpoint saved │ │ │ • action: cleared │ -│ │ │ │ │ • success: true │ -│ │ Waiting for analyst│ │ └───────────┬────────────┘ -│ │ decision... │ │ │ -│ └────────────────────┘ │ │ -│ │ │ -│ ┌────────────────────┐ │ │ -│ │ ANALYST REVIEWS │ │ │ -│ │ │ │ │ -│ │ Inputs: │ │ │ -│ │ • Alert details │ │ │ -│ │ • Risk assessment │ │ │ -│ │ • All findings │ │ │ -│ │ │ │ │ -│ │ Decision: │ │ │ -│ │ • lock_account │ │ │ -│ │ • refund_charges │ │ │ -│ │ • both │ │ │ -│ │ • clear │ │ │ -│ └────────────────────┘ │ │ -│ │ │ -│ Analyst Decision: │ │ -│ • Action: lock_account │ │ -│ • Notes: "Confirmed │ │ -│ fraudulent activity" │ │ -└───────────┬────────────┘ │ - ↓ │ -┌────────────────────────┐ │ -│ FraudActionExecutor │ │ -│ │ │ -│ Executes approved │ │ -│ action: │ │ -│ • Lock account │ │ -│ • Refund charges │ │ -│ • Both │ │ -│ • Clear │ │ -│ │ │ -│ Creates ActionResult: │ │ -│ • action_taken │ │ -│ • success: true │ │ -│ • details │ │ -│ • timestamp │ │ -└───────────┬────────────┘ │ - └──────────────┬──────────────────────┘ - ↓ - ┌────────────────────────┐ - │ FinalNotification │ - │ Executor │ - │ │ - │ • Notify customer │ - │ • Log audit trail │ - │ • Close alert │ - │ │ - │ Workflow Output: │ - │ FinalNotification { │ - │ customer_notified │ - │ audit_logged │ - │ } │ - └────────────────────────┘ - ↓ - ✅ WORKFLOW COMPLETE -``` - -## Message Flow Sequence - -``` -1. SuspiciousActivityAlert - ├─ alert_id: "ALERT-001" - ├─ customer_id: 1 - ├─ alert_type: "multi_country_login" - └─ severity: "high" - -2. Fan-Out (Parallel Execution) - ├─→ UsageAnalysisResult - │ ├─ risk_score: 0.7 - │ └─ risk_indicators: ["data_spike", "off_peak_usage"] - │ - ├─→ LocationAnalysisResult - │ ├─ risk_score: 0.85 - │ └─ risk_indicators: ["impossible_travel", "high_risk_region"] - │ - └─→ BillingAnalysisResult - ├─ risk_score: 0.4 - └─ risk_indicators: ["normal_pattern"] - -3. Fan-In (Aggregation) - → FraudRiskAssessment - ├─ overall_risk_score: 0.65 - ├─ risk_level: "high" - ├─ recommended_action: "lock_account" - └─ reasoning: "Multiple high-risk indicators..." - -4. Switch/Case (Conditional Routing) - IF risk_score >= 0.6: - → RequestInfoEvent - └─ Workflow PAUSES ⏸️ - - ELSE: - → AutoClear - └─ ActionResult(action_taken="cleared") - -5. Human Input (Analyst Decision) - → AnalystDecision - ├─ approved_action: "lock_account" - └─ analyst_notes: "Confirmed fraudulent activity" - - Workflow RESUMES ▶️ - -6. Action Execution - → ActionResult - ├─ action_taken: "lock_account" - ├─ success: true - └─ details: "Account locked. Analyst: analyst_001" - -7. Final Notification - → FinalNotification - ├─ customer_notified: true - ├─ audit_logged: true - └─ resolution: "Account locked due to fraudulent activity" - -8. Workflow Output - ✅ Complete -``` - -## Timing Diagram - -``` -Time → - -0s AlertRouter - ↓ -1s ┌──────────────┬──────────────┬──────────────┐ - UsagePattern Location Billing - Executor Executor Executor - ↓ ↓ ↓ - - [Parallel MCP Tool Calls] - -12s ↓ ↓ ↓ - Risk: 0.7 Risk: 0.85 Risk: 0.4 - └──────────────┴──────────────┘ - -13s FraudRiskAggregator (LLM) - ↓ - -18s Risk Assessment: 0.65 (HIGH) - ↓ - -19s Switch/Case Routing - ↓ - - [HIGH RISK PATH] - ↓ - -20s RequestInfoExecutor - ⏸️ WORKFLOW PAUSED - - [WAITING FOR ANALYST...] - -5m Analyst Reviews Case - ↓ - -5m30s AnalystDecision: lock_account - ▶️ WORKFLOW RESUMED - ↓ - -5m31s FraudActionExecutor - ↓ - -5m32s FinalNotificationExecutor - ↓ - -5m33s ✅ COMPLETE - -Total Active Time: ~33 seconds -Total Wall Time: ~5 minutes 33 seconds (includes analyst review) -``` - -## State Transitions - -``` -┌─────────────────┐ -│ INITIALIZED │ -│ │ -│ • Workflow built│ -│ • Executors │ -│ ready │ -└────────┬────────┘ - ↓ - run_stream() - ↓ -┌─────────────────┐ -│ RUNNING │ -│ │ -│ • Processing │ -│ alert │ -│ • Superstep 1 │ -└────────┬────────┘ - ↓ - 3 specialists complete - ↓ -┌─────────────────┐ -│ AGGREGATING │ -│ │ -│ • Fan-in wait │ -│ • LLM analysis │ -│ • Superstep 2 │ -└────────┬────────┘ - ↓ - Risk score >= 0.6 - ↓ -┌─────────────────┐ -│ WAITING_INPUT │ -│ │ -│ • Checkpoint │ -│ saved │ -│ • RequestInfo │ -│ event emitted │ -└────────┬────────┘ - ↓ - Analyst decision received - ↓ -┌─────────────────┐ -│ EXECUTING │ -│ │ -│ • Fraud action │ -│ • Superstep 3 │ -└────────┬────────┘ - ↓ -┌─────────────────┐ -│ NOTIFYING │ -│ │ -│ • Customer │ -│ • Audit log │ -│ • Superstep 4 │ -└────────┬────────┘ - ↓ -┌─────────────────┐ -│ COMPLETED │ -│ │ -│ • Final output │ -│ • Idle status │ -└─────────────────┘ -``` - -## Checkpoint Structure - -``` -WorkflowCheckpoint { - checkpoint_id: "cp_123456789" - workflow_id: "fraud_detection_001" - timestamp: "2025-10-07T10:30:45" - iteration_count: 3 - - pending_messages: { - "analyst_review": [ - FraudRiskAssessment { - alert_id: "ALERT-001" - overall_risk_score: 0.65 - recommended_action: "lock_account" - ... - } - ] - } - - shared_state: { - "current_alert_id": "ALERT-001" - "customer_id": 1 - } - - executor_states: { - "usage_pattern_executor": { - "analysis_complete": true - "risk_score": 0.7 - } - "location_analysis_executor": { - "analysis_complete": true - "risk_score": 0.85 - } - "billing_charge_executor": { - "analysis_complete": true - "risk_score": 0.4 - } - "fraud_risk_aggregator": { - "assessment_complete": true - "overall_risk": 0.65 - } - } -} -``` - -## Error Handling Flow - -``` -┌─────────────────┐ -│ Normal Flow │ -└────────┬────────┘ - ↓ - Try Execute - ↓ - ┌────┴────┐ - │ │ -Success Exception - │ │ - ↓ ↓ -Continue ┌──────────────┐ - │ Error Handler│ - └──────┬───────┘ - ↓ - ┌─────────────┐ - │ Log Error │ - └─────┬───────┘ - ↓ - ┌─────────────┐ - │ Checkpoint │ - │ Save │ - └─────┬───────┘ - ↓ - ┌─────────────┐ - │ Notify │ - │ Admin │ - └─────┬───────┘ - ↓ - ┌─────────────┐ - │ Workflow │ - │ FAILED │ - └─────────────┘ -``` - ---- - -## Legend - -- `┌──┐` Executor/Component -- `↓` Message flow -- `→` Data flow -- `⏸️` Workflow paused -- `▶️` Workflow resumed -- `✅` Success/Complete -- `[...]` Parallel execution -- `{...}` Data structure diff --git a/agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md b/agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md deleted file mode 100644 index fe2ae2649..000000000 --- a/agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md +++ /dev/null @@ -1,434 +0,0 @@ -# Fraud Detection Workflow Implementation Summary - -## 📋 Overview - -Successfully implemented the Contoso Fraud Detection & Escalation Workflow as specified in `scenario.md`. This is a production-ready console application demonstrating advanced Agent Framework workflow patterns. - -## ✅ What Was Implemented - -### 1. **Complete Workflow Topology** -- ✅ AlertRouterExecutor (entry point) -- ✅ Three specialist agent executors with MCP tools: - - UsagePatternExecutor - - LocationAnalysisExecutor - - BillingChargeExecutor -- ✅ FraudRiskAggregatorExecutor (LLM-based fan-in) -- ✅ Switch/case routing based on risk score -- ✅ ReviewGatewayExecutor for human-in-the-loop (uses ctx.request_info API) -- ✅ AutoClearExecutor for low-risk cases -- ✅ FraudActionExecutor for action execution -- ✅ FinalNotificationExecutor for completion - -### 2. **Key Patterns Demonstrated** - -#### Fan-Out Pattern -```python -# One alert → three parallel analyses -builder.add_edge(alert_router, usage_executor) -builder.add_edge(alert_router, location_executor) -builder.add_edge(alert_router, billing_executor) -``` - -#### Fan-In Pattern -```python -# Three analyses → one aggregator (waits for all) -builder.add_fan_in_edge( - [usage_executor, location_executor, billing_executor], - aggregator -) -``` - -#### Switch/Case Routing -```python -# Route based on risk score -builder.add_switch_case_edge( - aggregator, - cases=[ - (lambda a: a.overall_risk_score >= 0.6, analyst_review), # High risk - (lambda a: a.overall_risk_score < 0.6, auto_clear), # Low risk - ], -) -``` - -#### Human-in-the-Loop -```python -# ReviewGatewayExecutor uses ctx.request_info() to pause workflow -class ReviewGatewayExecutor(Executor): - async def handle_assessment(self, ctx, assessment: FraudRiskAssessment): - request = AnalystReviewRequest(...) - await ctx.request_info(request, AnalystDecision) - - @response_handler - async def handle_analyst_response(self, ctx, decision: AnalystDecision): - # Process the analyst decision when workflow resumes - await ctx.send_message(decision) -``` - -### 3. **MCP Tool Integration** - -Each specialist agent has **filtered access** to domain-specific MCP tools: - -**UsagePatternExecutor**: -- `get_customer_detail` -- `get_subscription_detail` -- `get_data_usage` -- `search_knowledge_base` - -**LocationAnalysisExecutor**: -- `get_customer_detail` -- `get_security_logs` -- `search_knowledge_base` - -**BillingChargeExecutor**: -- `get_customer_detail` -- `get_billing_summary` -- `get_subscription_detail` -- `get_customer_orders` -- `search_knowledge_base` - -### 4. **Type-Safe Message Flow** - -All messages use Pydantic models for validation: - -```python -SuspiciousActivityAlert → [3 specialist executors] - ↓ -[UsageAnalysisResult, LocationAnalysisResult, BillingAnalysisResult] - ↓ -FraudRiskAssessment → (Switch/Case) - ↓ -AnalystDecision OR ActionResult - ↓ -FinalNotification -``` - -### 5. **LLM-Based Risk Aggregation** - -The `FraudRiskAggregatorExecutor` uses an LLM agent to: -- Synthesize findings from three specialist agents -- Calculate weighted overall risk score -- Determine risk level (low/medium/high/critical) -- Recommend action (clear/lock_account/refund_charges/both) -- Provide reasoning referencing specific findings - -### 6. **Checkpointing Support** - -```python -# Workflow state saved at each superstep -# Using UTF8FileCheckpointStorage wrapper for Windows compatibility -checkpoint_storage = UTF8FileCheckpointStorage("./checkpoints") -workflow = create_fraud_detection_workflow( - ..., - checkpoint_storage=checkpoint_storage -) - -# Resume from checkpoint after analyst decision -async for event in workflow.run_stream(checkpoint_id=checkpoint_id): - process_event(event) - -# Send responses and continue workflow -async for event in workflow.send_responses_streaming(responses): - process_event(event) -``` - -## 📁 Files Created - -``` -agentic_ai/workflow/examples/fraud_detection/ -├── fraud_detection_workflow.py (890 lines - main implementation) -├── README.md (comprehensive documentation) -├── .env.sample (environment configuration template) -└── IMPLEMENTATION.md (this file) -``` - -## 🎯 Key Features - -### Event-Driven Architecture -- Triggered by suspicious activity alerts from monitoring systems -- Supports multiple alert types: multi-country logins, data spikes, unusual charges - -### Parallel Processing -- Three specialist agents analyze simultaneously -- Utilizes `asyncio.gather` for concurrent execution -- Reduces total analysis time by ~70% - -### Intelligent Risk Scoring -- LLM-based aggregation considers all specialist findings -- Weighted scoring based on alert severity and patterns -- Context-aware recommendations - -### False Positive Prevention -- Human analyst review for high-risk cases -- Prevents legitimate customers from being harmed -- Audit trail for all decisions - -### Production-Ready -- Comprehensive error handling -- Detailed logging at each step -- Type-safe message passing -- Graceful degradation on failures - -## 🚀 Running the Example - -### 1. Prerequisites - -```bash -# Set environment variables -export AZURE_OPENAI_API_KEY="your-key" -export AZURE_OPENAI_CHAT_DEPLOYMENT="your-deployment" -export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/" -export MCP_SERVER_URI="http://localhost:8000" -``` - -### 2. Start MCP Server - -```bash -cd mcp -python mcp_service.py -``` - -### 3. Run Fraud Detection Workflow - -```bash -cd agentic_ai/workflow/fraud_detection -python fraud_detection_workflow.py -``` - -## 📊 Sample Output - -``` -================================================================================ -Contoso Fraud Detection & Escalation Workflow -================================================================================ -[Setup] Connecting to MCP server at http://localhost:8000 -[Setup] Connected to MCP server, loaded 20 tools -[Workflow] Fraud detection workflow built successfully - -Processing Alert: ALERT-001 (Multi-country login) -[AlertRouter] Routing to 3 analysis executors -[UsagePatternExecutor] Analyzing... risk_score=0.7 -[LocationAnalysisExecutor] Analyzing... risk_score=0.85 -[BillingChargeExecutor] Analyzing... risk_score=0.4 -[FraudRiskAggregator] Overall risk_score=0.65, action=lock_account - -ANALYST REVIEW REQUIRED -[Analyst] Decision: lock_account -[FraudAction] Account locked successfully -[FinalNotification] Customer notified, audit logged - -Workflow completed successfully! -``` - -## 🎓 Learning Objectives Achieved - -### ✅ Fan-Out Pattern -Learned how to route one message to multiple executors for parallel processing. - -### ✅ Fan-In Pattern -Learned how to wait for multiple inputs before proceeding (aggregation). - -### ✅ Switch/Case Routing -Learned conditional routing based on message content and business logic. - -### ✅ Human-in-the-Loop -Learned workflow pause/resume with external input using `ctx.request_info()` and `@response_handler` decorator. - -### ✅ Checkpointing -Learned persistent workflow state for long-running processes. - -### ✅ MCP Tool Integration -Learned how to integrate specialist agents with filtered tool access. - -### ✅ LLM Agent Executors -Learned how to create AI-powered executors for decision-making. - -### ✅ Type-Safe Messaging -Learned Pydantic model usage for message validation. - -## 🔧 Customization Examples - -### Adjust Risk Threshold - -```python -# More sensitive (catches more cases) -(lambda a: a.overall_risk_score >= 0.5, analyst_review) - -# Less sensitive (fewer false positives) -(lambda a: a.overall_risk_score >= 0.7, analyst_review) -``` - -### Add Fourth Specialist - -```python -class NetworkAnalysisExecutor(Executor): - """Analyzes network patterns for VPN/proxy usage.""" - # Implementation... - -builder.add_executor(network_executor) -builder.add_edge(alert_router, network_executor) -builder.add_fan_in_edge( - [usage, location, billing, network_executor], - aggregator -) -``` - -### Custom Alert Types - -```python -SuspiciousActivityAlert( - alert_id="ALERT-004", - customer_id=4, - alert_type="account_takeover", - description="Password changed followed by subscription upgrade", - timestamp=datetime.now().isoformat(), - severity="critical", -) -``` - -## 🏗️ Architecture Decisions - -### Why Fan-Out? -- **Parallelism**: All three analyses run simultaneously -- **Specialization**: Each agent focuses on one domain -- **Scalability**: Easy to add more specialist agents - -### Why LLM Aggregator? -- **Context-aware**: Considers nuances in specialist findings -- **Flexible reasoning**: Can handle complex scenarios -- **Explainable**: Provides reasoning for decisions - -### Why Human-in-the-Loop? -- **False positive prevention**: Critical for customer trust -- **Compliance**: Many fraud systems require human oversight -- **Learning**: Analyst decisions improve the system over time - -### Why Checkpointing? -- **Reliability**: Resume after failures or restarts -- **Async workflows**: Can wait hours/days for analyst -- **Audit trail**: Complete history of workflow execution - -## 🔍 Code Highlights - -### Specialist Agent with MCP Tools - -```python -class UsagePatternExecutor(Executor): - def __init__(self, mcp_tool, chat_client, model): - # Filter to domain-specific tools - allowed_tools = [ - "get_customer_detail", - "get_subscription_detail", - "get_data_usage", - "search_knowledge_base", - ] - - filtered_functions = [ - func for func in mcp_tool.functions - if func.name in allowed_tools - ] - - self._agent = ChatAgent( - name="UsagePatternAnalyst", - instructions="Analyze usage patterns...", - tools=filtered_functions, - ) -``` - -### LLM-Based Aggregation - -```python -class FraudRiskAggregatorExecutor(Executor): - async def handle_analysis_results( - self, ctx, results: list[AnalysisResult] - ): - # Synthesize all findings - prompt = f"Aggregate these analyses: {results}" - response = await self._agent.run_stream(prompt) - - # Parse and create assessment - assessment = FraudRiskAssessment(...) - await ctx.send_message(assessment) -``` - -### Workflow Pause/Resume - -```python -# Workflow automatically pauses when ctx.request_info() is called -async for event in workflow.run_stream(alert): - if isinstance(event, RequestInfoEvent): - # Workflow is now paused, waiting for analyst decision - request_id = event.request_id - - # ... Get analyst decision from UI ... - - # Resume workflow with the response - responses = {request_id: analyst_decision} - async for event in workflow.send_responses_streaming(responses): - # @response_handler processes the decision - process_event(event) -``` - -## 📈 Performance Characteristics - -### Sequential Processing (Hypothetical) -``` -Usage Analysis: ~10 seconds -Location Analysis: ~8 seconds -Billing Analysis: ~12 seconds -Aggregation: ~5 seconds -Total: ~35 seconds -``` - -### Parallel Processing (Actual) -``` -[Usage, Location, Billing]: ~12 seconds (parallel) -Aggregation: ~5 seconds -Total: ~17 seconds (51% faster!) -``` - -## 🚦 Next Steps - -### Short-term Enhancements -1. Add persistent checkpoint storage (Redis/Cosmos DB) -2. Implement real-time UI for analyst review -3. Add metrics and OpenTelemetry tracing -4. Improve response parsing (use structured output API) - -### Medium-term Enhancements -1. Multi-alert processing (parallel workflows) -2. Escalation chains for critical cases -3. Machine learning model integration -4. A/B testing for risk thresholds - -### Long-term Enhancements -1. Integration with actual fraud systems -2. Real-time monitoring dashboard -3. Automated retraining from analyst feedback -4. Geographic distribution for global scale - -## 🎉 Success Metrics - -✅ **Complete implementation** of all 9 executors from scenario -✅ **All workflow patterns** demonstrated (fan-out, fan-in, switch, HITL) -✅ **Type-safe messaging** with Pydantic models -✅ **MCP tool integration** with filtered access -✅ **LLM-based aggregation** for intelligent risk scoring -✅ **Checkpointing** for pause/resume capability -✅ **Comprehensive documentation** with examples -✅ **Production-ready** error handling and logging - -## 📚 Related Documentation - -- CHECK LINK [Workflow README](README.md) - Architecture overview -- DEAD LINK State Management Guide ../../../agents/agent_framework/STATE_MANAGEMENT.md - Persistence patterns -- [Human-in-the-Loop Examples](../human-in-the-loop.md) - HITL patterns - -- DEAD LINK Control Flow Examples ../control-flow/ - Switch/case routing -- DEAD LINK Parallelism Examples ../parallelism/ - Fan-out patterns - - ---- - -**Implementation Date**: October 7, 2025 -**Agent Framework Version**: Latest -**Status**: ✅ Complete and Ready for Testing diff --git a/agentic_ai/workflow/fraud_detection/QUICKSTART.md b/agentic_ai/workflow/fraud_detection/QUICKSTART.md deleted file mode 100644 index 213ab2e11..000000000 --- a/agentic_ai/workflow/fraud_detection/QUICKSTART.md +++ /dev/null @@ -1,263 +0,0 @@ -# Fraud Detection Workflow - Quick Start Guide - -## 🚀 5-Minute Setup - -### 1. Configure Environment - -```bash -# Copy sample env file -cd agentic_ai/workflow/fraud_detection -cp .env.sample .env - -# Edit .env with your Azure OpenAI credentials -nano .env -``` - -### 2. Start MCP Server - -```bash -# Terminal 1 -cd mcp -python mcp_service.py -``` - -### 3. Run Workflow - -```bash -# Terminal 2 -cd agentic_ai/workflow/fraud_detection -python fraud_detection_workflow.py -``` - -## 📋 Key Commands - -### Test with Different Alerts - -Edit `fraud_detection_workflow.py` and modify the `alerts` list: - -```python -# High-risk multi-country login -SuspiciousActivityAlert( - alert_id="ALERT-001", - customer_id=1, - alert_type="multi_country_login", - severity="high" -) - -# Medium-risk data spike -SuspiciousActivityAlert( - alert_id="ALERT-002", - customer_id=2, - alert_type="data_spike", - severity="medium" -) -``` - -### View Logs - -```bash -# Real-time log viewing -tail -f workflow.log - -# Search for specific events -grep "FraudRiskAggregator" workflow.log -``` - -## 🎯 Common Tasks - -### Change Risk Threshold - -**File**: `fraud_detection_workflow.py` -**Line**: ~810 - -```python -# Current: 0.6 (60% risk) -(lambda assessment: assessment.overall_risk_score >= 0.6, analyst_review) - -# Make more sensitive (catches more): -(lambda assessment: assessment.overall_risk_score >= 0.5, analyst_review) - -# Make less sensitive (fewer alerts): -(lambda assessment: assessment.overall_risk_score >= 0.7, analyst_review) -``` - -### Simulate Analyst Decisions - -**File**: `fraud_detection_workflow.py` -**Line**: ~890 - -```python -analyst_decision = AnalystDecision( - alert_id=alerts[0].alert_id, - approved_action="lock_account", # Change: "clear", "refund_charges", "both" - analyst_notes="Your notes here", - analyst_id="analyst_001", -) -``` - -### Test with Custom Customer - -```python -# Add to MCP database first via mcp/data/create_db.py -# Then use in alert: -SuspiciousActivityAlert( - alert_id="ALERT-999", - customer_id=999, # Your custom customer - alert_type="custom_test", - description="Testing with custom customer", - timestamp=datetime.now().isoformat(), - severity="medium", -) -``` - -## 🐛 Troubleshooting - -### Error: "Cannot connect to MCP server" -```bash -# Check if MCP server is running -ps aux | grep mcp_service - -# Restart MCP server -cd mcp -python mcp_service.py -``` - -### Error: "Azure OpenAI authentication failed" -```bash -# Verify environment variables -echo $AZURE_OPENAI_API_KEY -echo $AZURE_OPENAI_ENDPOINT - -# Re-export if needed -export AZURE_OPENAI_API_KEY="your-key" -``` - -### Workflow Hangs at Analyst Review -```bash -# Check logs for RequestInfoEvent -grep "ANALYST REVIEW" workflow.log - -# Verify analyst decision is being sent -# Check around line 890 in fraud_detection_workflow.py -``` - -### MCP Tools Not Found -```bash -# Verify MCP connection -curl http://localhost:8000/list_tools - -# Check filtered tools in logs -grep "filtered tools" workflow.log -``` - -## 📊 Understanding Output - -### Successful Flow (Low Risk) -``` -[AlertRouter] → [3 Analysts] → [Aggregator] -→ [AutoClear] → [Notification] ✅ -``` - -### Successful Flow (High Risk) -``` -[AlertRouter] → [3 Analysts] → [Aggregator] -→ [AnalystReview] ⏸️ → [FraudAction] → [Notification] ✅ -``` - -### Risk Score Interpretation -- **0.0 - 0.3**: Low risk (auto-clear) -- **0.3 - 0.6**: Medium risk (auto-clear by default) -- **0.6 - 0.8**: High risk (requires analyst review) -- **0.8 - 1.0**: Critical risk (requires analyst review) - -## 🔧 Quick Customizations - -### Add Custom Risk Indicator - -**Location**: `UsagePatternExecutor.handle_alert()` - -```python -# Add to prompt: -""" -Additional checks: -- Look for X pattern -- Check Y threshold -- Flag Z behavior -""" -``` - -### Change Analyst Instructions - -**Location**: `ReviewGatewayExecutor` class in `fraud_detection_workflow.py` - -```python -# The AnalystReviewRequest dataclass contains the request details -@dataclass -class AnalystReviewRequest: - alert_id: str - customer_id: int - risk_assessment: FraudRiskAssessment - instructions: str = "Review the risk assessment and decide on action" -``` - -### Modify Action Logic - -**Location**: `FraudActionExecutor.handle_decision()` - -```python -if "lock" in decision.approved_action: - # Add custom lock logic - custom_lock_account(decision) -``` - -## 📖 Learning Path - -### Beginner -1. ✅ Run the example as-is -2. ✅ Change risk threshold -3. ✅ Modify alert severity -4. ✅ Review logs to understand flow - -### Intermediate -1. ✅ Add custom risk indicators -2. ✅ Modify analyst instructions -3. ✅ Create custom alert types -4. ✅ Add fourth specialist agent - -### Advanced -1. ✅ Implement persistent checkpoint storage -2. ✅ Add real-time UI for analyst review -3. ✅ Integrate with external systems -4. ✅ Add OpenTelemetry tracing - -## 🎓 Key Concepts Checklist - -- [ ] Understand fan-out pattern (1 → N) -- [ ] Understand fan-in pattern (N → 1) -- [ ] Understand switch/case routing -- [ ] Understand human-in-the-loop -- [ ] Understand checkpointing -- [ ] Understand MCP tool filtering -- [ ] Understand LLM agent executors -- [ ] Understand type-safe messaging - -## 📞 Getting Help - -### Check Documentation -- [Full README](README.md) -- [Implementation Details](IMPLEMENTATION.md) -- [Workflow Architecture](../README.md) - -### Review Examples -- [Human-in-the-Loop](../human-in-the-loop.md) - -- DEAD LINK Control Flow ../control-flow/ - -- DEAD LINK Parallelism ../parallelism/ - -### Common Issues -See [Troubleshooting](#-troubleshooting) section above - ---- - -**Pro Tip**: Start with the default configuration and make one change at a time to understand each component's impact! diff --git a/agentic_ai/workflow/fraud_detection/README.md b/agentic_ai/workflow/fraud_detection/README.md deleted file mode 100644 index 9fa788cb0..000000000 --- a/agentic_ai/workflow/fraud_detection/README.md +++ /dev/null @@ -1,407 +0,0 @@ -# Contoso Fraud Detection & Escalation Workflow - -This example demonstrates a comprehensive fraud detection system using the Agent Framework's workflow capabilities. - -## Architecture Overview - -``` -┌─────────────────────────────────────────────────────────────────────┐ -│ Suspicious Activity Alert │ -│ (monitoring system trigger) │ -└────────────────────────────┬────────────────────────────────────────┘ - ↓ - ┌────────────────┐ - │ AlertRouter │ - └────────┬───────┘ - ↓ - ┌─────────────────┼─────────────────┐ - ↓ ↓ ↓ - ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ - │ Usage │ │ Location │ │ Billing │ - │ Pattern │ │ Analysis │ │ Charge │ - │ Executor │ │ Executor │ │ Executor │ - │ (MCP tools) │ │ (MCP tools) │ │ (MCP tools) │ - └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ - └─────────────────┼─────────────────┘ - ↓ - ┌────────────────┐ - │ Fraud Risk │ - │ Aggregator │ - │ (LLM Agent) │ - └────────┬───────┘ - ↓ - (Risk Score Switch) - ↓ - ┌────────────────┴────────────────┐ - ↓ ↓ - (High Risk ≥0.6) (Low Risk <0.6) - ↓ ↓ - ┌──────────────┐ ┌──────────────┐ - │ Review │ │ Auto Clear │ - │ Gateway │ │ Executor │ - │(Human Input) │ └──────┬───────┘ - └──────┬───────┘ │ - ↓ │ - ┌──────────────┐ │ - │ Fraud │ │ - │ Action │ │ - │ Executor │ │ - └──────┬───────┘ │ - └──────────────┬─────────────────┘ - ↓ - ┌────────────────┐ - │ Final │ - │ Notification │ - │ Executor │ - └────────────────┘ -``` - -## Key Features - -### 1. **Event-Driven Processing** -- Triggered by suspicious activity alerts from monitoring systems -- Supports multiple alert types: multi-country logins, data spikes, unusual charges - -### 2. **Fan-Out Pattern** -- Single alert routed to three specialist agents simultaneously -- Each agent uses filtered MCP tools for domain-specific analysis: - - **UsagePatternExecutor**: Analyzes data usage patterns - - **LocationAnalysisExecutor**: Checks geolocation anomalies - - **BillingChargeExecutor**: Reviews billing and charges - -### 3. **Fan-In Aggregation** -- **FraudRiskAggregatorExecutor** waits for all three analyses -- LLM-based agent synthesizes findings into single risk assessment -- Produces overall risk score (0.0-1.0) and recommended action - -### 4. **Switch/Case Routing** -- Routes based on risk score threshold: - - **High risk (≥0.6)**: Human analyst review required - - **Low risk (<0.6)**: Auto-clear - -### 5. **Human-in-the-Loop** -- Uses `ctx.request_info()` API for analyst review within `ReviewGatewayExecutor` -- Workflow pauses and creates checkpoint automatically -- Analyst provides decision (lock account, refund charges, clear, both) -- `@response_handler` decorator processes the response when workflow resumes - -### 6. **Checkpointing** -- Workflow state saved at each superstep -- Can pause indefinitely while waiting for analyst -- Resume from exact point after analyst decision - -### 7. **MCP Tool Integration** -- Each specialist agent has filtered access to relevant MCP tools -- Tools used: - - Usage: `get_customer_detail`, `get_subscription_detail`, `get_data_usage`, `search_knowledge_base` - - Location: `get_customer_detail`, `get_security_logs`, `search_knowledge_base` - - Billing: `get_customer_detail`, `get_billing_summary`, `get_subscription_detail`, `get_customer_orders`, `search_knowledge_base` - -## Message Flow - -1. **SuspiciousActivityAlert** → AlertRouter -2. **SuspiciousActivityAlert** → [UsagePattern, Location, Billing] (fan-out) -3. **[UsageAnalysisResult, LocationAnalysisResult, BillingAnalysisResult]** → Aggregator (fan-in) -4. **FraudRiskAssessment** → Switch (risk score check) -5. **FraudRiskAssessment** → ReviewGateway OR AutoClear -6. **AnalystDecision** → FraudAction (if high risk, after human review) -7. **ActionResult** → FinalNotification -8. **FinalNotification** → Workflow output - -## Setup - -### Prerequisites - -1. **Azure OpenAI Configuration**: - ```bash - export AZURE_OPENAI_API_KEY="your-key" - export AZURE_OPENAI_CHAT_DEPLOYMENT="your-deployment" - export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/" - export AZURE_OPENAI_API_VERSION="2024-10-01-preview" - ``` - -2. **MCP Server**: - ```bash - export MCP_SERVER_URI="http://localhost:8000/mcp" - ``` - -3. **Start MCP Server** (in separate terminal): - ```bash - cd mcp - python mcp_service.py - ``` - -### Running the Workflow with command line - -```bash -cd agentic_ai/workflow/fraud_detection -python fraud_detection_workflow.py -``` - -## Sample Alerts - -The example includes three sample alerts: - -### Alert 1: Multi-Country Login (High Severity) -```python -alert_id: "ALERT-001" -customer_id: 1 -alert_type: "multi_country_login" -description: "Login attempts from USA and Russia within 2 hours" -severity: "high" -``` - -### Alert 2: Data Spike (Medium Severity) -```python -alert_id: "ALERT-002" -customer_id: 2 -alert_type: "data_spike" -description: "Data usage increased by 500% in last 24 hours" -severity: "medium" -``` - -### Alert 3: Unusual Charges (High Severity) -```python -alert_id: "ALERT-003" -customer_id: 3 -alert_type: "unusual_charges" -description: "Three large purchases totaling $5,000 in 10 minutes" -severity: "high" -``` - -## Expected Output - -``` -================================================================================ -Contoso Fraud Detection & Escalation Workflow -================================================================================ -[Setup] Connecting to MCP server at http://localhost:8000 -[Setup] Connected to MCP server, loaded 20 tools -[Workflow] Fraud detection workflow built successfully - -================================================================================ -Processing Alert: ALERT-001 -================================================================================ -[Workflow] Starting fraud detection workflow... -[AlertRouter] Processing alert ALERT-001 for customer 1 -[AlertRouter] Alert routed to 3 analysis executors -[UsagePatternExecutor] Analyzing alert ALERT-001 -[LocationAnalysisExecutor] Analyzing alert ALERT-001 -[BillingChargeExecutor] Analyzing alert ALERT-001 -[UsagePatternExecutor] Sent analysis result (risk_score=0.7) -[LocationAnalysisExecutor] Sent analysis result (risk_score=0.85) -[BillingChargeExecutor] Sent analysis result (risk_score=0.4) -[FraudRiskAggregator] Aggregating 3 analysis results -[FraudRiskAggregator] Sent assessment (risk_score=0.65, action=lock_account) - -================================================================================ -ANALYST REVIEW REQUIRED -================================================================================ -Request: {'type': 'fraud_analyst_review', ...} - -Simulating analyst decision... -[Analyst] Decision: lock_account -[FraudAction] Executing action: lock_account for alert ALERT-001 -[FraudAction] Action executed for alert ALERT-001 -[FinalNotification] Alert ALERT-001 completed and logged - -================================================================================ -Workflow completed successfully! -================================================================================ -``` - -## Customization - -### Adjust Risk Threshold - -Change the risk threshold in the switch/case edge: - -```python -# Current: 0.6 threshold -(lambda assessment: assessment.overall_risk_score >= 0.6, analyst_review) - -# Example: 0.5 threshold (more sensitive) -(lambda assessment: assessment.overall_risk_score >= 0.5, analyst_review) -``` - -### Add Custom Analysis - -Create a new executor: - -```python -class CustomAnalysisExecutor(Executor): - async def handle_alert( - self, ctx: WorkflowContext[CustomAnalysisResult], alert: SuspiciousActivityAlert - ) -> None: - # Your analysis logic - pass -``` - -Add to workflow: - -```python -builder.add_executor(custom_executor) -builder.add_edge(alert_router, custom_executor) -builder.add_fan_in_edge([usage, location, billing, custom_executor], aggregator) -``` - -### Modify Agent Instructions - -Adjust agent instructions in executor constructors: - -```python -instructions=( - "Your custom instructions here..." -) -``` - -## Key Concepts Demonstrated - -1. **Fan-Out Pattern**: One message → multiple executors -2. **Fan-In Pattern**: Multiple messages → one executor (waits for all) -3. **Switch/Case Routing**: Conditional routing based on message content -4. **Human-in-the-Loop**: Workflow pause/resume with `ctx.request_info()` and `@response_handler` -5. **Checkpointing**: Persistent workflow state across restarts -6. **MCP Tool Integration**: Domain-specific tool filtering -7. **LLM Agent Executors**: AI-powered decision making -8. **Type-Safe Messaging**: Pydantic models for all messages -9. **Event Streaming**: Real-time workflow event monitoring - - -## Real-Time Workflow Visualizer UI - -### Overview - -A modern React + FastAPI dashboard for real-time workflow visualization and analyst interaction. - -**Features:** -- 🎯 Interactive workflow graph (React Flow) -- 📊 Real-time executor status updates -- 💬 Live event stream with WebSocket -- 🎨 Professional Material-UI design -- 👤 Human-in-the-loop analyst panel - -### Architecture - -``` -┌─────────────────────┐ ┌──────────────────────┐ -│ React Frontend │◄────────┤ FastAPI Backend │ -│ (Port 3000) │ WebSocket│ (Port 8001) │ -│ │◄────────┤ │ -│ - React Flow │ HTTP │ - Workflow Engine │ -│ - Material-UI │ │ - Event Streaming │ -│ - WebSocket Client │ │ - Azure OpenAI │ -└─────────────────────┘ └──────────────────────┘ - │ - ▼ - ┌──────────────────┐ - │ MCP Server │ - │ (Port 8000) │ - └──────────────────┘ -``` - -### Quick Start - -**1. Install Frontend Dependencies:** -```bash -cd ui -npm install -``` - -**2. Start All Services (3 terminals):** - -Terminal 1 - MCP Server: -```bash -cd mcp -uv run mcp_service.py -``` - -Terminal 2 - FastAPI Backend: -```bash -cd agentic_ai/workflow/fraud_detection -uv run --prerelease allow backend.py -``` - -Terminal 3 - React Frontend: -```bash -cd agentic_ai/workflow/fraud_detection/ui -npm run dev -``` - -**3. Open Browser:** -``` -http://localhost:3000 -``` - -### Using the UI - -1. **Select Alert**: Choose from 3 sample alerts (ALERT-001, ALERT-002, ALERT-003) -2. **Start Workflow**: Click button to begin processing -3. **Watch Live Updates**: Nodes change color as executors run - - 🔵 Blue = Running - - 🟢 Green = Completed - - ⚪ Gray = Idle -4. **Analyst Review**: When high-risk fraud detected, review panel appears -5. **Submit Decision**: Choose action and add notes -6. **Monitor Events**: Right panel shows complete event stream - -### API Endpoints - -**REST API:** -- `GET /api/alerts` - List sample alerts -- `POST /api/workflow/start` - Start workflow execution -- `POST /api/workflow/decision` - Submit analyst decision -- `GET /api/workflow/status/{alert_id}` - Get current status - -**WebSocket:** -- `WS /ws` - Real-time event stream - -### Technology Stack - -**Frontend:** - -- React 19.2 + Vite 7.2 -- React Flow 11.11 (workflow visualization) -- Material-UI 7.3 (components) -- WebSocket (real-time updates) - -**Backend:** - -- FastAPI (async web framework) -- Agent Framework (workflow engine) -- Pydantic (data validation) -- WebSocket (real-time events) - -### Troubleshooting - -**WebSocket Connection Failed:** - -- Ensure backend is running on port 8001 -- Check browser console for errors - -**Workflow Not Starting:** - -- Verify MCP server is running (port 8000) -- Check Azure OpenAI credentials in .env -- Check backend logs - -**UI Not Updating:** - -- Check WebSocket connection in DevTools -- Verify events are being sent from backend - -**Checkpoint Encoding Error (Windows):** - -- If you see `'charmap' codec can't encode character` errors, the backend uses `UTF8FileCheckpointStorage` wrapper to handle Unicode characters in LLM output -- Ensure you're using the latest backend.py which includes this fix - -**Analyst Decision Not Resuming Workflow:** - -- Check that the checkpoint was created successfully (no encoding errors in logs) -- Verify the `request_id` matches between the decision and the pending request -- Check backend logs for `continue_workflow` messages - - -## License - -Copyright (c) Microsoft. All rights reserved. diff --git a/agentic_ai/workflow/fraud_detection/backend.py b/agentic_ai/workflow/fraud_detection/backend.py deleted file mode 100644 index 2f64da70e..000000000 --- a/agentic_ai/workflow/fraud_detection/backend.py +++ /dev/null @@ -1,1176 +0,0 @@ -""" -FastAPI backend for Fraud Detection Workflow Visualization. - -Provides REST API endpoints and WebSocket for real-time workflow event streaming. -""" - -import asyncio -import logging -import os -import sys -from datetime import datetime -from pathlib import Path -from typing import Any - -from dotenv import load_dotenv - -# Load environment first so observability can read connection string -load_dotenv() - -# ------------------------------------------------------------------ -# Observability (must be before any agent imports) -# ------------------------------------------------------------------ -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) # agentic_ai/ - -try: - from observability import setup_observability - _observability_enabled = setup_observability( - service_name="contoso-fraud-detection", - enable_live_metrics=True, - enable_sensitive_data=os.getenv("ENABLE_SENSITIVE_DATA", "false").lower() in ("1", "true", "yes"), - ) -except ImportError: - _observability_enabled = False - -# ------------------------------------------------------------------ -from fastapi import FastAPI, WebSocket, WebSocketDisconnect, Request, status -from fastapi.middleware.cors import CORSMiddleware -from fastapi.exceptions import RequestValidationError -from fastapi.responses import JSONResponse -from pydantic import BaseModel - -from fraud_detection_workflow import ( - AnalystDecision, - SuspiciousActivityAlert, - create_fraud_detection_workflow, -) -from agent_framework import ( - FileCheckpointStorage, - MCPStreamableHTTPTool, - ExecutorInvokedEvent, - ExecutorCompletedEvent, - WorkflowOutputEvent, - WorkflowStatusEvent, - RequestInfoEvent, - SuperStepCompletedEvent, - WorkflowCheckpoint, - get_checkpoint_summary, -) -from agent_framework.azure import AzureOpenAIChatClient -from azure.identity import AzureCliCredential -import json -from dataclasses import asdict - - -# ============================================================================ -# UTF-8 Checkpoint Storage Wrapper (fixes Windows encoding issues) -# ============================================================================ - -class UTF8FileCheckpointStorage: - """ - Wrapper around FileCheckpointStorage that ensures UTF-8 encoding. - - This fixes the Windows 'charmap' codec error when LLM output contains - Unicode characters (like combining diacritical marks) that can't be - encoded with the default cp1252 encoding. - """ - - def __init__(self, storage_path: str | Path): - """Initialize the file storage with UTF-8 encoding.""" - self.storage_path = Path(storage_path) - self.storage_path.mkdir(parents=True, exist_ok=True) - logger.info(f"Initialized UTF-8 file checkpoint storage at {self.storage_path}") - - async def save_checkpoint(self, checkpoint: WorkflowCheckpoint) -> str: - """Save a checkpoint with UTF-8 encoding and return its ID.""" - file_path = self.storage_path / f"{checkpoint.checkpoint_id}.json" - checkpoint_dict = asdict(checkpoint) - - def _write_atomic() -> None: - tmp_path = file_path.with_suffix(".json.tmp") - with open(tmp_path, "w", encoding="utf-8") as f: - json.dump(checkpoint_dict, f, indent=2, ensure_ascii=False) - os.replace(tmp_path, file_path) - - await asyncio.to_thread(_write_atomic) - logger.info(f"Saved checkpoint {checkpoint.checkpoint_id} to {file_path}") - return checkpoint.checkpoint_id - - async def load_checkpoint(self, checkpoint_id: str) -> WorkflowCheckpoint | None: - """Load a checkpoint by ID with UTF-8 encoding.""" - file_path = self.storage_path / f"{checkpoint_id}.json" - - if not file_path.exists(): - return None - - def _read() -> dict[str, Any]: - with open(file_path, encoding="utf-8") as f: - return json.load(f) - - checkpoint_dict = await asyncio.to_thread(_read) - checkpoint = WorkflowCheckpoint(**checkpoint_dict) - logger.info(f"Loaded checkpoint {checkpoint_id} from {file_path}") - return checkpoint - - async def list_checkpoint_ids(self, workflow_id: str | None = None) -> list[str]: - """List checkpoint IDs with UTF-8 encoding.""" - def _list_ids() -> list[str]: - checkpoint_ids: list[str] = [] - for file_path in self.storage_path.glob("*.json"): - try: - with open(file_path, encoding="utf-8") as f: - data = json.load(f) - if workflow_id is None or data.get("workflow_id") == workflow_id: - checkpoint_ids.append(data.get("checkpoint_id", file_path.stem)) - except Exception as e: - logger.warning(f"Failed to read checkpoint file {file_path}: {e}") - return checkpoint_ids - - return await asyncio.to_thread(_list_ids) - - async def list_checkpoints(self, workflow_id: str | None = None) -> list[WorkflowCheckpoint]: - """List checkpoint objects with UTF-8 encoding.""" - def _list_checkpoints() -> list[WorkflowCheckpoint]: - checkpoints: list[WorkflowCheckpoint] = [] - for file_path in self.storage_path.glob("*.json"): - try: - with open(file_path, encoding="utf-8") as f: - data = json.load(f) - if workflow_id is None or data.get("workflow_id") == workflow_id: - checkpoints.append(WorkflowCheckpoint.from_dict(data)) - except Exception as e: - logger.warning(f"Failed to read checkpoint file {file_path}: {e}") - return checkpoints - - return await asyncio.to_thread(_list_checkpoints) - - async def delete_checkpoint(self, checkpoint_id: str) -> bool: - """Delete a checkpoint by ID.""" - file_path = self.storage_path / f"{checkpoint_id}.json" - - def _delete() -> bool: - if file_path.exists(): - file_path.unlink() - logger.info(f"Deleted checkpoint {checkpoint_id} from {file_path}") - return True - return False - - return await asyncio.to_thread(_delete) - -# Configure logging -logging.basicConfig(level=logging.INFO, force=True) -logger = logging.getLogger(__name__) -if _observability_enabled: - logger.info("✅ Application Insights observability enabled for fraud detection workflow") - -# Keep agent_framework at INFO level -logging.getLogger("agent_framework").setLevel(logging.INFO) - -# FastAPI app -app = FastAPI(title="Fraud Detection Workflow API") - -# CORS middleware -app.add_middleware( - CORSMiddleware, - allow_origins=["http://localhost:3000", "http://localhost:5173"], # React dev servers - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) - - -# ============================================================================ -# Exception Handlers -# ============================================================================ - - -@app.exception_handler(RequestValidationError) -async def validation_exception_handler(request: Request, exc: RequestValidationError): - """Log validation errors for debugging.""" - try: - body = await request.body() - body_str = body.decode('utf-8') - except Exception: - body_str = "" - - logger.error(f"Validation error on {request.url}: {exc.errors()}") - logger.error(f"Request body: {body_str}") - return JSONResponse( - status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, - content={"detail": exc.errors(), "body": body_str}, - ) - - -# ============================================================================ -# Request/Response Models -# ============================================================================ - - -class StartWorkflowRequest(BaseModel): - """Request to start a workflow with a specific alert.""" - - alert_id: str - customer_id: int - alert_type: str - description: str - severity: str - - -class AnalystDecisionRequest(BaseModel): - """Analyst decision submitted from UI.""" - - request_id: str - alert_id: str | None = None # Optional - tracked via request_id - customer_id: int | str | None = None # Optional - tracked via request_id - approved_action: str - analyst_notes: str - analyst_id: str = "analyst_ui" - - -class WorkflowStatus(BaseModel): - """Current workflow status.""" - - status: str - current_executor: str | None = None - pending_requests: list[dict[str, Any]] = [] - - -# ============================================================================ -# Global State -# ============================================================================ - -# Store active workflows and their states -active_workflows: dict[str, Any] = {} # Stores workflow state AND the workflow instance -workflow_events: dict[str, list[dict[str, Any]]] = {} -pending_decisions: dict[str, dict[str, Any]] = {} -pending_request_events: dict[str, RequestInfoEvent] = {} - -# WebSocket connections -active_connections: list[WebSocket] = [] - -# Pre-initialized resources (created once on startup) -mcp_tool: MCPStreamableHTTPTool | None = None -chat_client: AzureOpenAIChatClient | None = None -checkpoint_storage: UTF8FileCheckpointStorage | None = None - - -# ============================================================================ -# WebSocket Connection Manager -# ============================================================================ - - -class ConnectionManager: - """Manages WebSocket connections for real-time event streaming.""" - - def __init__(self): - self.active_connections: list[WebSocket] = [] - - async def connect(self, websocket: WebSocket): - await websocket.accept() - self.active_connections.append(websocket) - logger.info(f"WebSocket connected. Total connections: {len(self.active_connections)}") - - def disconnect(self, websocket: WebSocket): - self.active_connections.remove(websocket) - logger.info(f"WebSocket disconnected. Total connections: {len(self.active_connections)}") - - async def broadcast(self, message: dict): - """Broadcast message to all connected clients.""" - msg_type = message.get('type', message.get('event_type', 'unknown')) - logger.info(f"[BROADCAST] Sending message type={msg_type} to {len(self.active_connections)} connections") - - if not self.active_connections: - logger.warning(f"No active WebSocket connections to broadcast to. Message type: {msg_type}") - return - - disconnected = [] - for connection in self.active_connections: - try: - await connection.send_json(message) - logger.info(f"[BROADCAST] Successfully sent {msg_type}") - except Exception as e: - logger.error(f"Error sending to WebSocket: {e}") - disconnected.append(connection) - - # Remove disconnected clients - for conn in disconnected: - self.disconnect(conn) - - -manager = ConnectionManager() - - -# ============================================================================ -# Sample Alerts -# ============================================================================ - -SAMPLE_ALERTS = { - "ALERT-001": SuspiciousActivityAlert( - alert_id="ALERT-001", - customer_id=1, - alert_type="multi_country_login", - description="Login attempts from USA and Russia within 2 hours", - timestamp=datetime.now().isoformat(), - severity="high", - ), - "ALERT-002": SuspiciousActivityAlert( - alert_id="ALERT-002", - customer_id=2, - alert_type="data_spike", - description="Data usage increased by 500% in last 24 hours", - timestamp=datetime.now().isoformat(), - severity="medium", - ), - "ALERT-003": SuspiciousActivityAlert( - alert_id="ALERT-003", - customer_id=3, - alert_type="unusual_charges", - description="Three large purchases totaling $5,000 in 10 minutes", - timestamp=datetime.now().isoformat(), - severity="high", - ), - "ALERT-004": SuspiciousActivityAlert( - alert_id="ALERT-004", - customer_id=4, - alert_type="routine_check", - description="Routine security check - password changed from usual device", - timestamp=datetime.now().isoformat(), - severity="low", - ), -} - - -def _serialize_analyst_request(event: RequestInfoEvent) -> dict[str, Any]: - """Convert a RequestInfoEvent into a UI-friendly payload.""" - from dataclasses import is_dataclass, asdict - - request_data = getattr(event, "data", None) - assessment = getattr(request_data, "assessment", None) - - def serialize_assessment(data: Any) -> dict[str, Any]: - if data is None: - return {} - # Handle dataclasses first (FraudRiskAssessment is a dataclass) - if is_dataclass(data): - return asdict(data) - # Handle Pydantic models - if hasattr(data, "model_dump"): - return data.model_dump() - if hasattr(data, "dict"): - return data.dict() - return {} - - assessment_payload = serialize_assessment(assessment) - - return { - "request_id": event.request_id, - "alert_id": assessment_payload.get("alert_id") or getattr(request_data, "alert_id", None), - "customer_id": assessment_payload.get("customer_id"), - "risk_score": assessment_payload.get("overall_risk_score", 0.0), - "risk_level": assessment_payload.get("risk_level"), - "recommended_action": assessment_payload.get("recommended_action"), - "reasoning": assessment_payload.get("reasoning", ""), - "analysis_summaries": assessment_payload.get("analysis_summaries", []), - "prompt": getattr(request_data, "prompt", ""), - } - - -async def cleanup_checkpoints(workflow_id: str | None): - """Clean up checkpoint files for a completed workflow.""" - if not checkpoint_storage or not workflow_id: - return - - try: - import pathlib - checkpoint_dir = pathlib.Path("./checkpoints") - if not checkpoint_dir.exists(): - return - - # List all checkpoints for this workflow - checkpoints = await checkpoint_storage.list_checkpoints(workflow_id=workflow_id) - deleted_count = 0 - - for checkpoint in checkpoints: - try: - checkpoint_file = checkpoint_dir / f"{checkpoint.checkpoint_id}.json" - if checkpoint_file.exists(): - checkpoint_file.unlink() - deleted_count += 1 - except Exception as e: - logger.warning(f"Failed to delete checkpoint {checkpoint.checkpoint_id}: {e}") - - if deleted_count > 0: - logger.info(f"Cleaned up {deleted_count} checkpoint file(s) for workflow {workflow_id}") - except Exception as e: - logger.error(f"Error cleaning up checkpoints: {e}") - - -async def _resolve_checkpoint_for_request(alert_id: str, request_id: str) -> tuple[str | None, int | None]: - """Locate the checkpoint that contains the pending request.""" - - if checkpoint_storage is None: - return None, None - - workflow_state = active_workflows.get(alert_id) - if not workflow_state: - return None, None - - workflow_id = workflow_state.get("workflow_id") - if not workflow_id: - return None, None - - async def _lookup() -> tuple[str | None, int | None]: - try: - checkpoints = await checkpoint_storage.list_checkpoints(workflow_id=workflow_id) - except Exception as exc: # pragma: no cover - defensive logging - logger.warning( - "Failed to list checkpoints for alert %s (workflow_id=%s): %s", - alert_id, - workflow_id, - exc, - ) - return None, None - - checkpoints_sorted = sorted( - checkpoints, - key=lambda cp: getattr(cp, "timestamp", "") or "", - reverse=True, - ) - - for checkpoint in checkpoints_sorted: - try: - # Use the new get_checkpoint_summary API instead of RequestInfoExecutor - summary = get_checkpoint_summary(checkpoint) - pending_events = summary.pending_request_info_events - except Exception as exc: # pragma: no cover - defensive logging - logger.debug( - "Unable to inspect checkpoint %s for alert %s: %s", - getattr(checkpoint, "checkpoint_id", ""), - alert_id, - exc, - ) - continue - - for pending in pending_events: - if pending.request_id == request_id: - return checkpoint.checkpoint_id, checkpoint.iteration_count - - return None, None - - # Retry multiple times in case the checkpoint is written just after the event - # The checkpoint is written AFTER the superstep completes, so we need to wait - max_attempts = 20 # Increased from 10 - for attempt in range(max_attempts): - checkpoint_id, iteration = await _lookup() - if checkpoint_id: - logger.info( - "Found checkpoint %s for alert %s request %s after %d attempt(s)", - checkpoint_id, - alert_id, - request_id, - attempt + 1, - ) - return checkpoint_id, iteration - if attempt < max_attempts - 1: - # Exponential backoff: 0.5s, 1s, 1.5s, 2s, 2.5s, etc. (max ~10 seconds total) - await asyncio.sleep(0.5 * (attempt + 1)) - if attempt % 5 == 4: # Log every 5 attempts - logger.debug( - "Still waiting for checkpoint for alert %s request %s (attempt %d/%d)", - alert_id, - request_id, - attempt + 1, - max_attempts, - ) - - logger.warning( - "Checkpoint not found for alert %s request %s (workflow_id=%s) after %s attempts (~%.1f seconds)", - alert_id, - request_id, - workflow_state.get("workflow_id"), - max_attempts, - sum(0.5 * (i + 1) for i in range(max_attempts)), - ) - return None, None - - -# ============================================================================ -# API Endpoints -# ============================================================================ - - -@app.get("/") -async def root(): - """Root endpoint.""" - return {"message": "Fraud Detection Workflow API", "status": "running"} - - -@app.get("/api/alerts") -async def get_sample_alerts(): - """Get list of sample alerts.""" - return { - "alerts": [ - { - "alert_id": alert.alert_id, - "customer_id": alert.customer_id, - "alert_type": alert.alert_type, - "description": alert.description, - "severity": alert.severity, - } - for alert in SAMPLE_ALERTS.values() - ] - } - - -@app.post("/api/workflow/start") -async def start_workflow(request: StartWorkflowRequest): - """Start a new workflow with the specified alert.""" - alert_id = request.alert_id - - # Check if alert exists in samples or create new one - if alert_id in SAMPLE_ALERTS: - alert = SAMPLE_ALERTS[alert_id] - else: - alert = SuspiciousActivityAlert( - alert_id=alert_id, - customer_id=request.customer_id, - alert_type=request.alert_type, - description=request.description, - timestamp=datetime.now().isoformat(), - severity=request.severity, - ) - - # Initialize workflow events storage - workflow_events[alert_id] = [] - pending_decisions[alert_id] = {} - pending_request_events.pop(alert_id, None) - - # Start workflow in background - asyncio.create_task(run_workflow(alert)) - - return {"status": "started", "alert_id": alert_id, "message": "Workflow started successfully"} - - -@app.post("/api/workflow/decision") -async def submit_decision(decision: AnalystDecisionRequest): - """Submit analyst decision for pending review.""" - logger.info(f"Received decision request: {decision}") - request_id = decision.request_id - - # Find the alert_id for this request_id - alert_id = None - for aid, pending_info in pending_decisions.items(): - if pending_info.get("request_id") == request_id: - alert_id = aid - break - - if not alert_id: - logger.error(f"No pending decision found for request_id {request_id}") - logger.error(f"Available pending decisions: {list(pending_decisions.keys())}") - return {"status": "error", "message": f"No pending decision found for request {request_id}"} - - if alert_id not in active_workflows: - return {"status": "error", "message": f"No active workflow for alert {alert_id}"} - - pending_info = pending_decisions.get(alert_id) - pending_event = pending_request_events.get(alert_id) - if not pending_info: - return {"status": "error", "message": f"No pending analyst review for alert {alert_id}"} - - checkpoint_id = pending_info.get("checkpoint_id") - if not checkpoint_id: - checkpoint_id, iteration = await _resolve_checkpoint_for_request(alert_id, request_id) - if checkpoint_id: - pending_info["checkpoint_id"] = checkpoint_id - pending_info["checkpoint_iteration"] = iteration - workflow_state = active_workflows.get(alert_id) - if workflow_state is not None: - workflow_state["pending_checkpoint_id"] = checkpoint_id - workflow_state["last_checkpoint_id"] = checkpoint_id - else: - checkpoint_id = None - - checkpoint_id = pending_info.get("checkpoint_id") - - if not checkpoint_id: - return { - "status": "error", - "message": ( - "No checkpoint available for this decision yet. Please wait a moment and try again, or restart the workflow." - ), - } - - # Normalize customer_id to int - customer_id = decision.customer_id or pending_info.get("customer_id") or 0 - if isinstance(customer_id, str): - try: - customer_id = int(customer_id) - except (ValueError, TypeError): - customer_id = 0 - - # Create AnalystDecision object - analyst_decision = AnalystDecision( - alert_id=alert_id, - customer_id=customer_id, - approved_action=decision.approved_action, - analyst_notes=decision.analyst_notes, - analyst_id=decision.analyst_id, - ) - - responses = {request_id: analyst_decision} - - # Continue workflow execution - logger.info(f"About to continue workflow for alert {alert_id} with checkpoint {checkpoint_id}") - logger.info(f"Request ID for response: {request_id}") - logger.info(f"Analyst decision: {analyst_decision}") - logger.info(f"Responses dict keys: {list(responses.keys())}") - task = asyncio.create_task(continue_workflow(alert_id, responses, checkpoint_id=checkpoint_id)) - - # Add error handler to catch exceptions in the background task - def task_done_callback(t): - try: - t.result() - except Exception as e: - logger.error(f"Error in continue_workflow task: {e}", exc_info=True) - - task.add_done_callback(task_done_callback) - - return {"status": "submitted", "message": "Decision submitted successfully"} - - -@app.get("/api/workflow/status/{alert_id}") -async def get_workflow_status(alert_id: str): - """Get current workflow status.""" - if alert_id not in active_workflows: - return {"status": "not_found", "message": f"No workflow found for alert {alert_id}"} - - workflow_state = active_workflows[alert_id] - return { - "status": workflow_state.get("status", "unknown"), - "current_executor": workflow_state.get("current_executor"), - "events": workflow_events.get(alert_id, []), - "pending_decision": pending_decisions.get(alert_id), - "pending_checkpoint_id": workflow_state.get("pending_checkpoint_id"), - "last_checkpoint_id": workflow_state.get("last_checkpoint_id"), - "workflow_id": workflow_state.get("workflow_id"), - } - - -# ============================================================================ -# WebSocket Endpoint -# ============================================================================ - - -@app.websocket("/ws") -async def websocket_endpoint(websocket: WebSocket): - """WebSocket endpoint for real-time event streaming.""" - await manager.connect(websocket) - try: - while True: - # Keep connection alive and receive any messages from client - data = await websocket.receive_text() - logger.info(f"Received from client: {data}") - except WebSocketDisconnect: - manager.disconnect(websocket) - - -# ============================================================================ -# Workflow Execution -# ============================================================================ - - -async def run_workflow(alert: SuspiciousActivityAlert): - """Run the fraud detection workflow and stream events.""" - alert_id = alert.alert_id - - try: - # Broadcast workflow initialization started - await manager.broadcast( - { - "type": "workflow_initializing", - "alert_id": alert_id, - "message": "Initializing workflow...", - "timestamp": datetime.now().isoformat(), - } - ) - - logger.info(f"Creating workflow for alert {alert_id}...") - - # Send periodic updates to keep connection alive during initialization - async def send_progress_updates(): - messages = [ - "Creating workflow graph...", - "Initializing executors...", - "Setting up agent tools...", - "Workflow ready!" - ] - for i, msg in enumerate(messages): - await asyncio.sleep(2) # Wait 2 seconds between updates - await manager.broadcast({ - "type": "workflow_initializing", - "alert_id": alert_id, - "message": msg, - "progress": (i + 1) / len(messages) * 100, - "timestamp": datetime.now().isoformat(), - }) - - # Create workflow using pre-initialized resources (this takes time!) - progress_task = asyncio.create_task(send_progress_updates()) - - try: - workflow = await create_fraud_detection_workflow( - mcp_tool=mcp_tool, - chat_client=chat_client, - model=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4"), - checkpoint_storage=checkpoint_storage, - ) - progress_task.cancel() # Stop progress updates - except asyncio.CancelledError: - pass - - logger.info(f"✓ Workflow created for alert {alert_id}") - - # Broadcast workflow ready - await manager.broadcast( - { - "type": "workflow_started", - "alert_id": alert_id, - "message": "Workflow ready, starting execution...", - "timestamp": datetime.now().isoformat(), - } - ) - - # Store workflow reference AND instance - active_workflows[alert_id] = { - "status": "running", - "workflow_id": workflow.id, - "workflow_instance": workflow, # Store the workflow instance for reuse - "current_executor": None, - "pending_checkpoint_id": None, - "last_checkpoint_id": None, - } - - # Small delay to ensure WebSocket connection is fully established - await asyncio.sleep(0.1) - - # Track if we've seen a RequestInfoEvent (workflow is awaiting decision) - seen_request_info = False - request_info_event_data = None - - # Run workflow and stream events - async for event in workflow.run_stream(alert): - logger.info(f"[DEBUG] Received event: {type(event).__name__}") - await process_event(alert_id, event) - - # Check for human-in-the-loop request - if isinstance(event, RequestInfoEvent): - logger.info(f"[DEBUG] RequestInfoEvent detected! request_id={event.request_id}, source={event.source_executor_id}") - request_payload = _serialize_analyst_request(event) - logger.info(f"[DEBUG] Serialized request payload keys: {list(request_payload.keys())}") - - timestamp = datetime.now().isoformat() - - # Store the pending decision immediately - checkpoint will be resolved later - pending_decisions[alert_id] = { - **request_payload, - "timestamp": timestamp, - "source_executor_id": event.source_executor_id, - "checkpoint_id": None, # Will be resolved after superstep completes - "checkpoint_iteration": None, - } - pending_request_events[alert_id] = event - active_workflows[alert_id]["status"] = "awaiting_decision" - - # Broadcast decision_required immediately - don't wait for checkpoint - await manager.broadcast( - { - "type": "decision_required", - "alert_id": alert_id, - "request_id": event.request_id, - "data": request_payload, - "checkpoint_id": None, # Will be resolved after superstep completes - "checkpoint_iteration": None, - "timestamp": timestamp, - } - ) - - logger.info(f"[DEBUG] Broadcast decision_required for {alert_id}") - logger.info(f"Workflow {alert_id} awaiting analyst decision") - - # Mark that we've seen the request - continue processing to get checkpoint - seen_request_info = True - request_info_event_data = event - # DON'T return here - continue to let the superstep complete and checkpoint be created - - # After a superstep completes following a RequestInfoEvent, resolve the checkpoint - elif seen_request_info and isinstance(event, SuperStepCompletedEvent): - logger.info(f"[DEBUG] SuperStepCompleted after RequestInfoEvent - resolving checkpoint") - checkpoint_id, checkpoint_iteration = await _resolve_checkpoint_for_request( - alert_id, - request_info_event_data.request_id, - ) - logger.info(f"[DEBUG] Resolved checkpoint: id={checkpoint_id}, iteration={checkpoint_iteration}") - - # Update stored values with resolved checkpoint - if checkpoint_id: - pending_decisions[alert_id]["checkpoint_id"] = checkpoint_id - pending_decisions[alert_id]["checkpoint_iteration"] = checkpoint_iteration - active_workflows[alert_id]["pending_checkpoint_id"] = checkpoint_id - active_workflows[alert_id]["last_checkpoint_id"] = checkpoint_id - else: - logger.warning( - "No checkpoint recorded for alert %s request %s; resume will be unavailable until one is created.", - alert_id, - request_info_event_data.request_id, - ) - - # Now we can exit - workflow is paused awaiting decision - return - - # If we exit the loop naturally, mark as completed - if alert_id in active_workflows: - active_workflows[alert_id]["status"] = "completed" - active_workflows[alert_id]["current_executor"] = None - active_workflows[alert_id]["pending_checkpoint_id"] = None - pending_decisions.pop(alert_id, None) - pending_request_events.pop(alert_id, None) - - await manager.broadcast( - { - "type": "workflow_completed", - "alert_id": alert_id, - "timestamp": datetime.now().isoformat(), - } - ) - - logger.info(f"✓ Workflow completed for alert {alert_id}") - - # Clean up checkpoint files for this completed workflow - if alert_id in active_workflows: - await cleanup_checkpoints(active_workflows[alert_id].get("workflow_id")) - - except Exception as e: - logger.error(f"Error in workflow execution: {e}", exc_info=True) - await manager.broadcast( - { - "type": "workflow_error", - "alert_id": alert_id, - "error": str(e), - "timestamp": datetime.now().isoformat(), - } - ) - - -async def continue_workflow(alert_id: str, responses: dict[str, Any], checkpoint_id: str | None = None): - """Continue workflow execution after analyst decision.""" - logger.info(f"=== continue_workflow called for alert {alert_id} ===") - logger.info(f"Checkpoint ID: {checkpoint_id}") - logger.info(f"Responses: {responses}") - logger.info(f"Active workflows: {list(active_workflows.keys())}") - - if alert_id not in active_workflows: - logger.warning("Attempted to continue unknown workflow for alert %s", alert_id) - return - - workflow_state = active_workflows[alert_id] - logger.info(f"Workflow state: {workflow_state}") - pending_info = pending_decisions.get(alert_id) - effective_checkpoint_id = checkpoint_id or workflow_state.get("pending_checkpoint_id") - if not effective_checkpoint_id and pending_info: - effective_checkpoint_id = pending_info.get("checkpoint_id") - - if not effective_checkpoint_id: - logger.error("No checkpoint available to resume workflow for alert %s", alert_id) - await manager.broadcast( - { - "type": "workflow_error", - "alert_id": alert_id, - "error": "Missing checkpoint for analyst decision", - "timestamp": datetime.now().isoformat(), - } - ) - return - - # Clear pending state before resuming - pending_decisions.pop(alert_id, None) - pending_request_events.pop(alert_id, None) - workflow_state["pending_checkpoint_id"] = None - workflow_state["last_checkpoint_id"] = effective_checkpoint_id - - try: - # Create a NEW workflow instance for resuming (like the reference example) - # The checkpoint contains all the state needed to restore everything - logger.info(f"Creating new workflow instance for resume") - workflow = await create_fraud_detection_workflow( - mcp_tool=mcp_tool, - chat_client=chat_client, - model=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4"), - checkpoint_storage=checkpoint_storage, - ) - - workflow_state["workflow_instance"] = workflow - workflow_state.setdefault("workflow_id", workflow.id) - workflow_state["status"] = "running" - - # Debug: Check what's in the checkpoint using get_checkpoint_summary - checkpoint = await checkpoint_storage.load_checkpoint(effective_checkpoint_id) - if checkpoint: - summary = get_checkpoint_summary(checkpoint) - logger.info(f"Checkpoint status: {summary.status}") - logger.info(f"Pending request events: {len(summary.pending_request_info_events)}") - for pending in summary.pending_request_info_events: - logger.info(f" - Request ID: {pending.request_id}, Source: {pending.source_executor_id}") - - logger.info(f"Starting workflow resume with responses keys: {list(responses.keys())}") - logger.info(f"Checkpoint ID: {effective_checkpoint_id}") - - # Immediately broadcast review_gateway completion since the framework may not emit it - # The executor using ctx.request_info was waiting, and when we provide the response, it completes - # but this completion event is not always emitted in the event stream during resume - await manager.broadcast({ - "alert_id": alert_id, - "type": "ExecutorCompletedEvent", - "event_type": "executor_completed", - "executor_id": "review_gateway", - "timestamp": datetime.now().isoformat(), - }) - logger.info("Broadcast review_gateway completion event") - - # Track request info executors that completed during resume - completed_request_executors = set() - - # First, restore from checkpoint - logger.info(f"Restoring workflow from checkpoint {effective_checkpoint_id}") - async for event in workflow.run_stream( - checkpoint_id=effective_checkpoint_id, - checkpoint_storage=checkpoint_storage, - ): - logger.info(f"Restore event received: {type(event).__name__}") - await process_event(alert_id, event) - - # Now send the responses to continue the workflow - logger.info(f"Sending responses to continue workflow: {list(responses.keys())}") - async for event in workflow.send_responses_streaming(responses): - logger.info(f"Event received: {type(event).__name__}") - - # Track when review_gateway completes so we can re-broadcast at the end - if isinstance(event, ExecutorCompletedEvent) and event.executor_id == "review_gateway": - completed_request_executors.add(event.executor_id) - - await process_event(alert_id, event) - - if isinstance(event, RequestInfoEvent): - # Skip RequestInfoEvents that we already provided responses for - if event.request_id in responses: - logger.info(f"Skipping already-responded RequestInfoEvent {event.request_id}") - continue - request_payload = _serialize_analyst_request(event) - checkpoint_id_next, checkpoint_iteration = await _resolve_checkpoint_for_request( - alert_id, - event.request_id, - ) - - timestamp = datetime.now().isoformat() - pending_decisions[alert_id] = { - **request_payload, - "timestamp": timestamp, - "source_executor_id": event.source_executor_id, - "checkpoint_id": checkpoint_id_next, - "checkpoint_iteration": checkpoint_iteration, - } - pending_request_events[alert_id] = event - workflow_state["status"] = "awaiting_decision" - workflow_state["pending_checkpoint_id"] = checkpoint_id_next - if checkpoint_id_next: - workflow_state["last_checkpoint_id"] = checkpoint_id_next - else: - logger.warning( - "No checkpoint found after resuming workflow for alert %s request %s", - alert_id, - event.request_id, - ) - - await manager.broadcast( - { - "type": "decision_required", - "alert_id": alert_id, - "request_id": event.request_id, - "data": request_payload, - "checkpoint_id": checkpoint_id_next, - "checkpoint_iteration": checkpoint_iteration, - "timestamp": timestamp, - } - ) - - logger.info(f"Workflow {alert_id} awaiting additional analyst decision") - return - - # Workflow completed - logger.info(f"Workflow loop finished for alert {alert_id}") - logger.info(f"Pending decisions remaining: {list(pending_decisions.keys())}") - - # If workflow finished without another decision request, mark as completed - if alert_id not in pending_decisions: - workflow_state["status"] = "completed" - logger.info(f"Workflow {alert_id} completed successfully") - - # Small delay to ensure all events are sent to UI before completion message - await asyncio.sleep(0.5) - - # Broadcast completion to UI - await manager.broadcast( - { - "type": "workflow_completed", - "alert_id": alert_id, - "message": "Workflow completed successfully", - "timestamp": datetime.now().isoformat(), - } - ) - - # Clean up checkpoint files for this completed workflow - await cleanup_checkpoints(workflow_state.get("workflow_id")) - workflow_state["status"] = "awaiting_decision" - else: - workflow_state["status"] = "completed" - workflow_state["current_executor"] = None - - await manager.broadcast( - { - "type": "workflow_completed", - "alert_id": alert_id, - "timestamp": datetime.now().isoformat(), - } - ) - - logger.info(f"Workflow {alert_id} completed after resuming checkpoint {effective_checkpoint_id}") - - except Exception as e: - logger.error(f"Error continuing workflow: {e}", exc_info=True) - workflow_state["status"] = "error" - await manager.broadcast( - { - "type": "workflow_error", - "alert_id": alert_id, - "error": str(e), - "timestamp": datetime.now().isoformat(), - } - ) - - -async def process_event(alert_id: str, event: Any): - """Process and broadcast workflow event.""" - event_data = { - "alert_id": alert_id, - "type": event.__class__.__name__, - "timestamp": datetime.now().isoformat(), - } - - if isinstance(event, ExecutorInvokedEvent): - event_data.update( - { - "event_type": "executor_invoked", - "executor_id": event.executor_id, - } - ) - active_workflows[alert_id]["current_executor"] = event.executor_id - - elif isinstance(event, ExecutorCompletedEvent): - event_data.update( - { - "event_type": "executor_completed", - "executor_id": event.executor_id, - } - ) - logger.info(f"Broadcasting executor_completed for {event.executor_id}") - - elif isinstance(event, WorkflowStatusEvent): - event_data.update( - { - "event_type": "status_change", - "status": str(event.state) if hasattr(event, "state") else "unknown", - } - ) - - elif isinstance(event, WorkflowOutputEvent): - event_data.update( - { - "event_type": "workflow_output", - "output": str(event.data) if hasattr(event, "data") else None, - } - ) - - # Store event - if alert_id not in workflow_events: - workflow_events[alert_id] = [] - workflow_events[alert_id].append(event_data) - - # Broadcast to all connected clients - await manager.broadcast(event_data) - - -# ============================================================================ -# Startup/Shutdown Events -# ============================================================================ - - -@app.on_event("startup") -async def startup_event(): - """Initialize resources on startup.""" - global mcp_tool, chat_client, checkpoint_storage - - logger.info("Initializing backend resources...") - - try: - # Initialize MCP tool - mcp_server_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") - mcp_tool = MCPStreamableHTTPTool( - name="contoso_mcp", - url=mcp_server_uri, - headers={"Content-Type": "application/json"}, - timeout=30, - request_timeout=30, - ) - await mcp_tool.__aenter__() - logger.info(f"✓ MCP tool initialized at {mcp_server_uri}") - - # Initialize Azure OpenAI client - chat_client = AzureOpenAIChatClient(credential=AzureCliCredential(), deployment_name=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT")) - logger.info("✓ Azure OpenAI client initialized") - - # Initialize checkpoint storage with UTF-8 encoding wrapper - import pathlib - checkpoint_dir = pathlib.Path("./checkpoints") - checkpoint_dir.mkdir(parents=True, exist_ok=True) - checkpoint_storage = UTF8FileCheckpointStorage(str(checkpoint_dir)) - logger.info(f"✓ Checkpoint storage initialized at {checkpoint_dir.absolute()}") - - logger.info("Backend ready! 🚀") - - except Exception as e: - logger.error(f"Failed to initialize backend resources: {e}", exc_info=True) - raise - - -@app.on_event("shutdown") -async def shutdown_event(): - """Cleanup resources on shutdown.""" - global mcp_tool - - logger.info("Shutting down backend...") - - if mcp_tool: - try: - await mcp_tool.__aexit__(None, None, None) - logger.info("✓ MCP tool cleaned up") - except Exception as e: - logger.error(f"Error cleaning up MCP tool: {e}") - - -# ============================================================================ -# Main Entry Point -# ============================================================================ - -if __name__ == "__main__": - import uvicorn - - uvicorn.run(app, host="0.0.0.0", port=8001) diff --git a/agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py b/agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py deleted file mode 100644 index da36abf1e..000000000 --- a/agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py +++ /dev/null @@ -1,1195 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. - -""" -Contoso Fraud Detection & Escalation Workflow - -Business Context: -Contoso's automated systems flag suspicious account activity, but certain cases require -a fraud analyst to review before taking action (e.g., locking account, reversing charges). - -Flow: -1. Event Trigger: Backend monitoring system sends "Suspicious Activity Alert" -2. AlertRouterExecutor → routes alert to multiple analysis executors: - - UsagePatternExecutor → checks recent usage against historical baseline (uses MCP tools) - - LocationAnalysisExecutor → checks geolocation anomalies (uses MCP tools) - - BillingChargeExecutor → checks for unusual purchases or charges (uses MCP tools) -3. Fan-In to FraudRiskAggregatorExecutor (LLM-based agent): - - Produces FraudRiskScore and recommended action (lock account, refund charges, ignore) -4. SwitchCaseEdgeRunner: - - If risk score ≥ threshold → route to ReviewGatewayExecutor for human fraud analyst review - - Else → route to AutoClearExecutor -5. ReviewGatewayExecutor → calls ctx.request_info() to request analyst decision, workflow pauses -6. Checkpoint saved — workflow awaits analyst response -7. Analyst decides (approve lock/refund or clear) -8. Workflow resumes: - - ReviewGatewayExecutor's @response_handler processes analyst decision - - FraudActionExecutor → performs chosen action (e.g., lock account, reverse charges) -9. FinalNotificationExecutor → informs customer and logs audit trail - -Demonstrates: -- Event-driven workflow triggered by backend system -- Fan-out pattern to multiple specialist agents with MCP tools -- Fan-in aggregation to produce single risk assessment -- LLM-based risk scoring -- Human-in-the-loop via ctx.request_info() and @response_handler -- Checkpointing for workflow pause/resume -""" - -import asyncio -import logging -import os -from dataclasses import dataclass, field -from datetime import datetime -from typing import Any -from dotenv import load_dotenv - -load_dotenv() - -from agent_framework import ( - Case, - ChatAgent, - Default, - Executor, - ExecutorCompletedEvent, - ExecutorInvokedEvent, - FileCheckpointStorage, - MCPStreamableHTTPTool, - RequestInfoEvent, - WorkflowBuilder, - WorkflowContext, - WorkflowOutputEvent, - WorkflowStatusEvent, - handler, - response_handler, -) -from agent_framework.azure import AzureOpenAIChatClient -from pydantic import BaseModel, Field - -# Configure logging -logging.basicConfig(force=True, level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") -logger = logging.getLogger(__name__) - - -# ============================================================================ -# Message Models -# NOTE: Using dataclasses instead of Pydantic for checkpoint compatibility -# The framework's checkpoint serialization doesn't support Pydantic v2's -# model_dump()/model_dump_json() methods yet (it looks for to_dict/to_json). -# ============================================================================ - - -@dataclass -class SuspiciousActivityAlert: - """Initial alert from monitoring system.""" - - alert_id: str - customer_id: int - alert_type: str # "multi_country_login", "data_spike", "unusual_charges" - description: str - timestamp: str - severity: str # "low", "medium", "high" - - -@dataclass -class UsageAnalysisResult: - """Result from usage pattern analysis.""" - - alert_id: str - customer_id: int - analysis_type: str = "usage_pattern" - findings: str = "" - risk_indicators: list[str] = field(default_factory=list) - risk_score: float = 0.5 # 0-1 risk score - - -@dataclass -class LocationAnalysisResult: - """Result from location analysis.""" - - alert_id: str - customer_id: int - analysis_type: str = "location" - findings: str = "" - risk_indicators: list[str] = field(default_factory=list) - risk_score: float = 0.5 # 0-1 risk score - - -@dataclass -class BillingAnalysisResult: - """Result from billing charge analysis.""" - - alert_id: str - customer_id: int - analysis_type: str = "billing" - findings: str = "" - risk_indicators: list[str] = field(default_factory=list) - risk_score: float = 0.5 # 0-1 risk score - - -@dataclass -class FraudRiskAssessment: - """Aggregated fraud risk assessment.""" - - alert_id: str - customer_id: int - overall_risk_score: float # 0-1 risk score - risk_level: str # "low", "medium", "high", "critical" - recommended_action: str # "clear", "lock_account", "refund_charges", "both" - reasoning: str - analysis_summaries: list[str] = field(default_factory=list) - - -@dataclass -class AnalystReviewRequest: - """Request for analyst review - used with ctx.request_info().""" - - assessment: FraudRiskAssessment | None = None - prompt: str = "" - - -@dataclass -class AnalystDecision: - """Decision from human fraud analyst.""" - - alert_id: str - customer_id: int - approved_action: str # "clear", "lock_account", "refund_charges", "both" - analyst_notes: str - analyst_id: str - - -@dataclass -class ActionResult: - """Result of fraud action execution.""" - - alert_id: str - customer_id: int - action_taken: str - success: bool - details: str - timestamp: str - - -@dataclass -class FinalNotification: - """Final notification to customer and audit log.""" - - alert_id: str - customer_id: int - resolution: str - customer_notified: bool - audit_logged: bool - - -# ============================================================================ -# Executors -# ============================================================================ - - -class AlertRouterExecutor(Executor): - """Routes suspicious activity alerts to multiple analysis executors (fan-out).""" - - def __init__(self, id: str = "alert_router") -> None: - super().__init__(id=id) - - @handler - async def handle_alert( - self, - alert: SuspiciousActivityAlert, - ctx: WorkflowContext[SuspiciousActivityAlert], - ) -> None: - try: - logger.info(f"[AlertRouter] Processing alert {alert.alert_id} for customer {alert.customer_id}") - logger.info(f"[AlertRouter] Alert type: {alert.alert_type}, Severity: {alert.severity}") - - print(f"\n[DEBUG] AlertRouter: Fan-out alert to all connected analysts") - print(f"[DEBUG] Alert data: {alert}") - print(f"[DEBUG] Context type: {type(ctx)}") - - # Fan-out: Send the same alert to all three analysis executors - # The add_fan_out_edges in the workflow builder will automatically route this - # message to all connected downstream executors - result = await ctx.send_message(alert) - print(f"[DEBUG] send_message result: {result}") - - logger.info(f"[AlertRouter] Alert {alert.alert_id} sent for fan-out routing") - print(f"[DEBUG] AlertRouter: Message sent (fan-out edges will handle distribution)") - except Exception as e: - print(f"[ERROR] Exception in AlertRouter: {e}") - import traceback - traceback.print_exc() - raise - - -class UsagePatternExecutor(Executor): - """Analyzes usage patterns using MCP tools.""" - - def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient, model: str, id: str = "usage_pattern_executor") -> None: - - super().__init__(id=id) - self._agent: ChatAgent | None = None - self._mcp_tool = mcp_tool - self._chat_client = chat_client - self._model = model - self._allowed_tools = { - "get_customer_detail", - "get_subscription_detail", - "get_data_usage", - "search_knowledge_base", - } - - async def _ensure_agent(self) -> None: - if self._agent is not None: - return - - logger.info("[UsagePatternExecutor] Initializing agent...") - filtered_functions = [func for func in self._mcp_tool.functions if func.name in self._allowed_tools] - self._agent = ChatAgent( - name="UsagePatternAnalyst", - chat_client=self._chat_client, - model=self._model, - instructions=( - "You are a usage pattern fraud analyst for Contoso. " - "Analyze customer data usage patterns to detect anomalies that may indicate fraud. " - "Use the provided tools to retrieve customer details, subscription info, and data usage history. " - "Look for: sudden spikes in data usage, usage patterns inconsistent with subscription tier, " - "usage from unusual times/locations. " - "Provide a risk score (0.0-1.0) and specific risk indicators." - ), - tools=filtered_functions, - ) - await self._agent.__aenter__() - logger.info("[UsagePatternExecutor] Agent initialized with filtered MCP tools") - - @handler - async def handle_alert( - self, alert: SuspiciousActivityAlert, ctx: WorkflowContext[UsageAnalysisResult] - ) -> None: - logger.info(f"[UsagePatternExecutor] Analyzing alert {alert.alert_id}") - - await self._ensure_agent() - assert self._agent is not None - - # Create analysis prompt - prompt = f"""Analyze potential fraud for customer ID {alert.customer_id}. - -Alert Details: -- Type: {alert.alert_type} -- Description: {alert.description} -- Severity: {alert.severity} - -Tasks: -1. Retrieve customer details and subscription information -2. Get recent data usage history (last 30 days if available) -3. Identify any unusual patterns or anomalies -4. Provide risk indicators and a risk score (0.0-1.0) - -Respond in this format: -FINDINGS: [Your detailed findings] -RISK_INDICATORS: [List specific red flags] -RISK_SCORE: [0.0-1.0] -""" - - # Run agent to analyze - thread = self._agent.get_new_thread() - response_parts = [] - async for chunk in self._agent.run_stream(prompt, thread=thread): - if hasattr(chunk, "text") and chunk.text: - response_parts.append(chunk.text) - - response = "".join(response_parts) - logger.info(f"[UsagePatternExecutor] Analysis complete: {response[:200]}...") - - # Parse response (simple parsing - in production use structured output) - risk_score = 0.5 # Default - risk_indicators = [] - findings = response - - if "RISK_SCORE:" in response: - try: - score_line = [line for line in response.split("\n") if "RISK_SCORE:" in line][0] - risk_score = float(score_line.split("RISK_SCORE:")[1].strip()) - except (IndexError, ValueError): - pass - - if "RISK_INDICATORS:" in response: - try: - indicators_line = [line for line in response.split("\n") if "RISK_INDICATORS:" in line][0] - indicators_text = indicators_line.split("RISK_INDICATORS:")[1].strip() - risk_indicators = [ind.strip() for ind in indicators_text.split(",")] - except IndexError: - pass - - # Send result to aggregator - result = UsageAnalysisResult( - alert_id=alert.alert_id, - customer_id=alert.customer_id, - findings=findings, - risk_indicators=risk_indicators, - risk_score=risk_score, - ) - - await ctx.send_message(result) - logger.info(f"[UsagePatternExecutor] Sent analysis result (risk_score={risk_score})") - - -class LocationAnalysisExecutor(Executor): - """Analyzes location anomalies using MCP tools.""" - - def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient, model: str, id: str = "location_analysis_executor") -> None: - super().__init__(id=id) - print("[LocationAnalysisExecutor] Initializing...") - self._agent: ChatAgent | None = None - self._mcp_tool = mcp_tool - self._chat_client = chat_client - self._model = model - self._allowed_tools = { - "get_customer_detail", - "get_security_logs", - "search_knowledge_base", - } - - async def _ensure_agent(self) -> None: - if self._agent is not None: - return - - logger.info("[LocationAnalysisExecutor] Initializing agent...") - filtered_functions = [func for func in self._mcp_tool.functions if func.name in self._allowed_tools] - self._agent = ChatAgent( - name="LocationAnalyst", - chat_client=self._chat_client, - model=self._model, - instructions=( - "You are a geolocation fraud analyst for Contoso. " - "Analyze customer security logs and authentication patterns to detect location-based fraud. " - "Use the provided tools to retrieve customer details and security logs. " - "Look for: logins from multiple countries in short timeframes, impossible travel scenarios, " - "access from high-risk regions, VPN/proxy usage patterns. " - "Provide a risk score (0.0-1.0) and specific risk indicators." - ), - tools=filtered_functions, - ) - await self._agent.__aenter__() - logger.info("[LocationAnalysisExecutor] Agent initialized") - - @handler - async def handle_alert( - self, alert: SuspiciousActivityAlert, ctx: WorkflowContext[LocationAnalysisResult] - ) -> None: - logger.info(f"[LocationAnalysisExecutor] Analyzing alert {alert.alert_id}") - - await self._ensure_agent() - assert self._agent is not None - - prompt = f"""Analyze location-based fraud indicators for customer ID {alert.customer_id}. - -Alert Details: -- Type: {alert.alert_type} -- Description: {alert.description} - -Tasks: -1. Retrieve customer details -2. Get security logs (authentication events, login locations) -3. Identify suspicious location patterns -4. Provide risk indicators and a risk score (0.0-1.0) - -Respond in this format: -FINDINGS: [Your detailed findings] -RISK_INDICATORS: [List specific red flags] -RISK_SCORE: [0.0-1.0] -""" - - thread = self._agent.get_new_thread() - response_parts = [] - async for chunk in self._agent.run_stream(prompt, thread=thread): - if hasattr(chunk, "text") and chunk.text: - response_parts.append(chunk.text) - - response = "".join(response_parts) - logger.info(f"[LocationAnalysisExecutor] Analysis complete") - - # Parse response - risk_score = 0.5 - risk_indicators = [] - findings = response - - if "RISK_SCORE:" in response: - try: - score_line = [line for line in response.split("\n") if "RISK_SCORE:" in line][0] - risk_score = float(score_line.split("RISK_SCORE:")[1].strip()) - except (IndexError, ValueError): - pass - - if "RISK_INDICATORS:" in response: - try: - indicators_line = [line for line in response.split("\n") if "RISK_INDICATORS:" in line][0] - indicators_text = indicators_line.split("RISK_INDICATORS:")[1].strip() - risk_indicators = [ind.strip() for ind in indicators_text.split(",")] - except IndexError: - pass - - result = LocationAnalysisResult( - alert_id=alert.alert_id, - customer_id=alert.customer_id, - findings=findings, - risk_indicators=risk_indicators, - risk_score=risk_score, - ) - - await ctx.send_message(result) - logger.info(f"[LocationAnalysisExecutor] Sent analysis result (risk_score={risk_score})") - - -class BillingChargeExecutor(Executor): - """Analyzes billing and charge anomalies using MCP tools.""" - - def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient, model: str, id: str = "billing_charge_executor") -> None: - super().__init__(id=id) - self._agent: ChatAgent | None = None - self._mcp_tool = mcp_tool - self._chat_client = chat_client - self._model = model - self._allowed_tools = { - "get_customer_detail", - "get_billing_summary", - "get_subscription_detail", - "get_customer_orders", - "search_knowledge_base", - } - - async def _ensure_agent(self) -> None: - if self._agent is not None: - return - - logger.info("[BillingChargeExecutor] Initializing agent...") - filtered_functions = [func for func in self._mcp_tool.functions if func.name in self._allowed_tools] - self._agent = ChatAgent( - name="BillingAnalyst", - chat_client=self._chat_client, - model=self._model, - instructions=( - "You are a billing fraud analyst for Contoso. " - "Analyze customer billing patterns and charges to detect fraudulent activity. " - "Use the provided tools to retrieve customer details, billing summaries, and order history. " - "Look for: unusual charge amounts, unexpected subscription changes, " - "orders inconsistent with customer profile, rapid succession of charges. " - "Provide a risk score (0.0-1.0) and specific risk indicators." - ), - tools=filtered_functions, - ) - await self._agent.__aenter__() - logger.info("[BillingChargeExecutor] Agent initialized") - - @handler - async def handle_alert( - self, alert: SuspiciousActivityAlert, ctx: WorkflowContext[BillingAnalysisResult] - ) -> None: - logger.info(f"[BillingChargeExecutor] Analyzing alert {alert.alert_id}") - - await self._ensure_agent() - assert self._agent is not None - - prompt = f"""Analyze billing fraud indicators for customer ID {alert.customer_id}. - -Alert Details: -- Type: {alert.alert_type} -- Description: {alert.description} - -Tasks: -1. Retrieve customer details and subscription information -2. Get billing summary and recent orders -3. Identify unusual billing patterns or charges -4. Provide risk indicators and a risk score (0.0-1.0) - -Respond in this format: -FINDINGS: [Your detailed findings] -RISK_INDICATORS: [List specific red flags] -RISK_SCORE: [0.0-1.0] -""" - - thread = self._agent.get_new_thread() - response_parts = [] - async for chunk in self._agent.run_stream(prompt, thread=thread): - if hasattr(chunk, "text") and chunk.text: - response_parts.append(chunk.text) - - response = "".join(response_parts) - logger.info(f"[BillingChargeExecutor] Analysis complete") - - # Parse response - risk_score = 0.5 - risk_indicators = [] - findings = response - - if "RISK_SCORE:" in response: - try: - score_line = [line for line in response.split("\n") if "RISK_SCORE:" in line][0] - risk_score = float(score_line.split("RISK_SCORE:")[1].strip()) - except (IndexError, ValueError): - pass - - if "RISK_INDICATORS:" in response: - try: - indicators_line = [line for line in response.split("\n") if "RISK_INDICATORS:" in line][0] - indicators_text = indicators_line.split("RISK_INDICATORS:")[1].strip() - risk_indicators = [ind.strip() for ind in indicators_text.split(",")] - except IndexError: - pass - - result = BillingAnalysisResult( - alert_id=alert.alert_id, - customer_id=alert.customer_id, - findings=findings, - risk_indicators=risk_indicators, - risk_score=risk_score, - ) - - await ctx.send_message(result) - logger.info(f"[BillingChargeExecutor] Sent analysis result (risk_score={risk_score})") - - -class FraudRiskAggregatorExecutor(Executor): - """ - LLM-based agent that aggregates analysis results and produces final fraud risk assessment. - This is a fan-in executor that waits for all three analysis results. - """ - - def __init__(self, chat_client: AzureOpenAIChatClient, model: str, id: str = "fraud_risk_aggregator") -> None: - super().__init__(id=id) - self._agent: ChatAgent | None = None - self._chat_client = chat_client - self._model = model - async def _ensure_agent(self) -> None: - if self._agent is not None: - return - - logger.info("[FraudRiskAggregator] Initializing agent...") - self._agent = ChatAgent( - name="FraudRiskAggregator", - chat_client=self._chat_client, - model=self._model, - instructions=( - "You are the lead fraud risk aggregator for Contoso. " - "You receive analysis results from three specialist agents: " - "usage pattern analyst, location analyst, and billing analyst. " - "Your job is to synthesize their findings into a single fraud risk assessment. " - "Calculate an overall risk score (0.0-1.0) based on the three input scores. " - "Determine risk level (low, medium, high, critical) and recommend action: " - "- clear: No fraud detected, close alert " - "- lock_account: Lock customer account to prevent further activity " - "- refund_charges: Reverse fraudulent charges " - "- both: Lock account AND refund charges " - "Provide clear reasoning for your decision." - ), - ) - await self._agent.__aenter__() - logger.info("[FraudRiskAggregator] Agent initialized") - - @handler - async def handle_analysis_results( - self, - results: list[UsageAnalysisResult | LocationAnalysisResult | BillingAnalysisResult], - ctx: WorkflowContext[FraudRiskAssessment], - ) -> None: - logger.info(f"[FraudRiskAggregator] Aggregating {len(results)} analysis results") - - await self._ensure_agent() - assert self._agent is not None - - # Build comprehensive prompt with all analysis results - alert_id = results[0].alert_id - customer_id = results[0].customer_id - - summaries = [] - for result in results: - summary = f""" -Analysis Type: {result.analysis_type} -Risk Score: {result.risk_score} -Risk Indicators: {', '.join(result.risk_indicators) if result.risk_indicators else 'None'} -Findings: {result.findings} -""" - summaries.append(summary) - - prompt = f"""Aggregate fraud risk assessment for Alert ID: {alert_id}, Customer ID: {customer_id} - -You have received analysis results from three specialists: - -{chr(10).join(summaries)} - -Tasks: -1. Calculate an overall risk score (weighted average or your best judgment) -2. Determine risk level: low (<0.3), medium (0.3-0.6), high (0.6-0.8), critical (>0.8) -3. Recommend action: clear, lock_account, refund_charges, or both -4. Provide reasoning that references specific findings from the analysts - -Respond in this format: -OVERALL_RISK_SCORE: [0.0-1.0] -RISK_LEVEL: [low/medium/high/critical] -RECOMMENDED_ACTION: [clear/lock_account/refund_charges/both] -REASONING: [Your comprehensive reasoning] -""" - - thread = self._agent.get_new_thread() - response_parts = [] - async for chunk in self._agent.run_stream(prompt, thread=thread): - if hasattr(chunk, "text") and chunk.text: - response_parts.append(chunk.text) - - response = "".join(response_parts) - logger.info(f"[FraudRiskAggregator] Assessment complete") - - # Parse response - overall_risk_score = 0.5 - risk_level = "medium" - recommended_action = "clear" - reasoning = response - - try: - if "OVERALL_RISK_SCORE:" in response: - score_line = [line for line in response.split("\n") if "OVERALL_RISK_SCORE:" in line][0] - overall_risk_score = float(score_line.split("OVERALL_RISK_SCORE:")[1].strip()) - - if "RISK_LEVEL:" in response: - level_line = [line for line in response.split("\n") if "RISK_LEVEL:" in line][0] - risk_level = level_line.split("RISK_LEVEL:")[1].strip().lower() - - if "RECOMMENDED_ACTION:" in response: - action_line = [line for line in response.split("\n") if "RECOMMENDED_ACTION:" in line][0] - recommended_action = action_line.split("RECOMMENDED_ACTION:")[1].strip().lower() - except (IndexError, ValueError) as e: - logger.warning(f"[FraudRiskAggregator] Error parsing response: {e}") - - # Create assessment - assessment = FraudRiskAssessment( - alert_id=alert_id, - customer_id=customer_id, - overall_risk_score=overall_risk_score, - risk_level=risk_level, - recommended_action=recommended_action, - reasoning=reasoning, - analysis_summaries=[f"{r.analysis_type}: {r.risk_score}" for r in results], - ) - - await ctx.send_message(assessment) - logger.info( - f"[FraudRiskAggregator] Sent assessment (risk_score={overall_risk_score}, action={recommended_action})" - ) - - -class ReviewGatewayExecutor(Executor): - """ - Gateway that handles high-risk assessments with human-in-the-loop review. - - Uses the new request_info API: - 1. Receives high-risk assessment - 2. Calls ctx.request_info() to pause workflow and request analyst input - 3. @response_handler receives the analyst's decision - 4. Forwards decision to FraudActionExecutor - """ - - def __init__(self, fraud_action_id: str, id: str = "review_gateway") -> None: - super().__init__(id=id) - self._fraud_action_id = fraud_action_id - # Store the assessment for use in the response handler - self._pending_assessment: FraudRiskAssessment | None = None - - @handler - async def handle_assessment( - self, assessment: FraudRiskAssessment, ctx: WorkflowContext[AnalystDecision] - ) -> None: - logger.info(f"[ReviewGateway] Routing high-risk assessment {assessment.alert_id} to analyst") - - # Store assessment for response handler - self._pending_assessment = assessment - - # Create analyst review request - request = AnalystReviewRequest( - assessment=assessment, - prompt=f"Review fraud case for alert {assessment.alert_id}. Risk score: {assessment.overall_risk_score:.2f}. Recommended action: {assessment.recommended_action}", - ) - - # Request info from external analyst - workflow will pause here - # The response will be handled by the @response_handler below - await ctx.request_info(request, AnalystDecision) - logger.info(f"[ReviewGateway] Waiting for analyst decision on {assessment.alert_id}") - - @response_handler - async def handle_analyst_response( - self, - original_request: AnalystReviewRequest, - response: AnalystDecision, - ctx: WorkflowContext[AnalystDecision], - ) -> None: - """Handle the analyst's decision and forward to fraud action executor.""" - logger.info(f"[ReviewGateway] Received analyst decision: {response.approved_action}") - - # Ensure decision has customer_id from original assessment - if original_request.assessment and getattr(response, "customer_id", None) in (None, 0): - from dataclasses import replace - response = replace( - response, - customer_id=original_request.assessment.customer_id, - alert_id=original_request.assessment.alert_id, - ) - - # Forward the analyst decision to fraud action executor - await ctx.send_message(response, target_id=self._fraud_action_id) - logger.info(f"[ReviewGateway] Forwarded decision to fraud action executor") - - -class AutoClearExecutor(Executor): - """Automatically clears low-risk alerts.""" - - def __init__(self, id: str = "auto_clear_executor") -> None: - super().__init__(id=id) - - @handler - async def handle_assessment( - self, assessment: FraudRiskAssessment, ctx: WorkflowContext[ActionResult] - ) -> None: - logger.info(f"[AutoClear] Auto-clearing alert {assessment.alert_id} (low risk)") - - result = ActionResult( - alert_id=assessment.alert_id, - customer_id=assessment.customer_id, - action_taken="cleared", - success=True, - details=f"Alert auto-cleared. Risk score: {assessment.overall_risk_score}", - timestamp=datetime.now().isoformat(), - ) - - await ctx.send_message(result) - logger.info(f"[AutoClear] Alert {assessment.alert_id} cleared") - - -class FraudActionExecutor(Executor): - """Executes fraud mitigation actions based on analyst decision.""" - - def __init__(self, id: str = "fraud_action_executor") -> None: - super().__init__(id=id) - - @handler - async def handle_decision(self, decision: AnalystDecision, ctx: WorkflowContext[ActionResult]) -> None: - logger.info(f"[FraudAction] Executing action: {decision.approved_action} for alert {decision.alert_id}") - - # Add delay to make workflow progression visible in UI - await asyncio.sleep(2) - - # Simulate action execution - action_details = [] - if "lock" in decision.approved_action: - action_details.append("Account locked") - if "refund" in decision.approved_action: - action_details.append("Charges reversed") - if decision.approved_action == "clear": - action_details.append("Alert cleared, no action taken") - - result = ActionResult( - alert_id=decision.alert_id, - customer_id=decision.customer_id, - action_taken=decision.approved_action, - success=True, - details=f"{', '.join(action_details)}. Analyst: {decision.analyst_id}. Notes: {decision.analyst_notes}", - timestamp=datetime.now().isoformat(), - ) - - await ctx.send_message(result) - logger.info(f"[FraudAction] Action executed for alert {decision.alert_id}") - - -class FinalNotificationExecutor(Executor): - """Sends final notification and logs audit trail.""" - - def __init__(self, id: str = "final_notification_executor") -> None: - super().__init__(id=id) - - @handler - async def handle_result( - self, result: ActionResult, ctx: WorkflowContext[None, FinalNotification] - ) -> None: - logger.info(f"[FinalNotification] Processing result for alert {result.alert_id}") - - # Add delay to make workflow progression visible in UI - await asyncio.sleep(2) - - # Simulate notification and audit logging - notification = FinalNotification( - alert_id=result.alert_id, - customer_id=result.customer_id, - resolution=result.details, - customer_notified=True, - audit_logged=True, - ) - - # Yield as workflow output - await ctx.yield_output(notification) - - logger.info(f"[FinalNotification] Alert {result.alert_id} completed and logged") - - -# ============================================================================ -# Workflow Builder -# ============================================================================ - - -async def create_fraud_detection_workflow( - mcp_tool: MCPStreamableHTTPTool, - chat_client: AzureOpenAIChatClient, - model: str, - checkpoint_storage: Any | None = None, -) -> Any: - """ - Build the fraud detection workflow. - - Topology (updated for new request_info API): - AlertRouter → [UsagePattern, Location, Billing] → FraudRiskAggregator - ↓ - (Switch based on risk score) - ↓ - ┌───────────────────────┴──────────────────────┐ - ↓ ↓ - (High Risk) (Low Risk) - ReviewGateway AutoClearExecutor - (uses ctx.request_info ↓ - for human-in-the-loop) ↓ - ↓ ↓ - FraudActionExecutor ────────────────────→ FinalNotificationExecutor - - Note: ReviewGateway now uses ctx.request_info() and @response_handler - instead of the deprecated RequestInfoExecutor. - """ - - # Create executors - alert_router = AlertRouterExecutor() - usage_executor = UsagePatternExecutor(mcp_tool, chat_client, model) - location_executor = LocationAnalysisExecutor(mcp_tool, chat_client, model) - billing_executor = BillingChargeExecutor(mcp_tool, chat_client, model) - aggregator = FraudRiskAggregatorExecutor(chat_client, model) - auto_clear = AutoClearExecutor() - fraud_action = FraudActionExecutor() - final_notification = FinalNotificationExecutor() - - # Create human-in-the-loop executor (now uses ctx.request_info internally) - review_gateway = ReviewGatewayExecutor( - fraud_action_id=fraud_action.id, - ) - - # Build workflow - builder = WorkflowBuilder() - - # Fan-out edges: AlertRouter → 3 analysts (parallel branches) - builder.add_fan_out_edges(alert_router, [usage_executor, location_executor, billing_executor]) - - # Fan-in edge: 3 analysts → Aggregator (waits for all 3) - builder.add_fan_in_edges([usage_executor, location_executor, billing_executor], aggregator) - - # Switch/case edges: Aggregator → High risk OR Low risk - builder.add_switch_case_edge_group( - aggregator, - [ - # High risk → Review Gateway (will request human input via ctx.request_info) - Case(condition=lambda assessment: assessment.overall_risk_score >= 0.6, target=review_gateway), - # Low risk → Auto clear - Default(target=auto_clear), - ], - ) - - # Review gateway → Fraud action (after analyst decision via @response_handler) - builder.add_edge(review_gateway, fraud_action) - - # Both paths → Final notification - builder.add_edge(auto_clear, final_notification) - builder.add_edge(fraud_action, final_notification) - - # Set start executor - builder.set_start_executor(alert_router) - - if checkpoint_storage: - builder = builder.with_checkpointing(checkpoint_storage=checkpoint_storage) - - workflow = builder.build() - - print("\n" + "=" * 80) - print(f"[WORKFLOW STRUCTURE]") - print(f"Total executors: {len(workflow.executors)}") - print(f"Executor IDs: {list(workflow.executors.keys())}") - print("=" * 80 + "\n") - - logger.info("[Workflow] Fraud detection workflow built successfully") - - return workflow - - -# ============================================================================ -# Main Console Application -# ============================================================================ - - -async def main() -> None: - """Main console application.""" - # Load environment variables from .env file - - logger.info("=" * 80) - logger.info("Contoso Fraud Detection & Escalation Workflow") - logger.info("=" * 80) - - # Load configuration - azure_openai_key = os.getenv("AZURE_OPENAI_API_KEY") - azure_deployment = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT") - azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") - mcp_server_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") - if not all([azure_openai_key, azure_deployment, azure_endpoint]): - logger.error("Missing required environment variables. Please set:") - logger.error(" AZURE_OPENAI_API_KEY") - logger.error(" AZURE_OPENAI_CHAT_DEPLOYMENT") - logger.error(" AZURE_OPENAI_ENDPOINT") - logger.error(" MCP_SERVER_URI (optional, defaults to http://localhost:8000)") - return - - # Create Azure OpenAI client - chat_client = AzureOpenAIChatClient( - api_key=azure_openai_key, - deployment_name=azure_deployment, - endpoint=azure_endpoint, - ) - - # Create MCP tool - logger.info(f"[Setup] Connecting to MCP server at {mcp_server_uri}") - mcp_tool = MCPStreamableHTTPTool( - name="contoso_mcp", - url=mcp_server_uri, - headers={"Content-Type": "application/json"}, - timeout=30, - request_timeout=30, - ) - - # Connect to MCP server - await mcp_tool.__aenter__() - logger.info(f"[Setup] Connected to MCP server, loaded {len(mcp_tool.functions)} tools") - - # Create checkpoint storage - # checkpoint_storage=None - checkpoint_storage = FileCheckpointStorage("./checkpoints/fraud_detection") - - # Build workflow - workflow = await create_fraud_detection_workflow( - mcp_tool=mcp_tool, - chat_client=chat_client, - model=azure_deployment, - checkpoint_storage=checkpoint_storage, - ) - - # Sample suspicious activity alerts - alerts = [ - SuspiciousActivityAlert( - alert_id="ALERT-001", - customer_id=1, - alert_type="multi_country_login", - description="Login attempts from USA and Russia within 2 hours", - timestamp=datetime.now().isoformat(), - severity="high", - ), - SuspiciousActivityAlert( - alert_id="ALERT-002", - customer_id=2, - alert_type="data_spike", - description="Data usage increased by 500% in last 24 hours", - timestamp=datetime.now().isoformat(), - severity="medium", - ), - SuspiciousActivityAlert( - alert_id="ALERT-003", - customer_id=3, - alert_type="unusual_charges", - description="Three large purchases totaling $5,000 in 10 minutes", - timestamp=datetime.now().isoformat(), - severity="high", - ), - ] - - # Process first alert - logger.info("\n" + "=" * 80) - logger.info(f"Processing Alert: {alerts[0].alert_id}") - logger.info("=" * 80) - - try: - # Run workflow with streaming - logger.info("[Workflow] Starting fraud detection workflow...") - - event_count = 0 - final_output: FinalNotification | None = None - last_checkpoint_id: str | None = None - - async def consume_stream(stream: Any) -> tuple[list[RequestInfoEvent], FinalNotification | None, str | None]: - nonlocal event_count - pending_requests: list[RequestInfoEvent] = [] - output: FinalNotification | None = None - checkpoint_id: str | None = None - - async for event in stream: - event_count += 1 - logger.info(f"[Workflow] Event #{event_count}: {event.__class__.__name__}") - print(f"\n[Event Type: {event.__class__.__name__}]") - - if isinstance(event, ExecutorInvokedEvent): - print(f" ▶ Executor '{event.executor_id}' invoked") - if getattr(event, "message", None): - print(f" 📥 Input: {event.message}") - elif isinstance(event, ExecutorCompletedEvent): - print(f" ✓ Executor '{event.executor_id}' completed") - if getattr(event, "data", None): - print(" 📤 Data:") - if isinstance(event.data, dict): - for key, value in event.data.items(): - print(f" • {key}: {value}") - elif hasattr(event.data, "__dict__"): - for key, value in event.data.__dict__.items(): - if not key.startswith("_"): - print(f" • {key}: {value}") - else: - print(f" {event.data}") - elif isinstance(event, RequestInfoEvent) and isinstance(event.data, AnalystReviewRequest): - pending_requests.append(event) - logger.info("\n" + "=" * 80) - logger.info("ANALYST REVIEW REQUIRED") - logger.info("=" * 80) - request = event.data - logger.info(f"Request ID: {event.request_id}") - risk_level = request.assessment.risk_level if request.assessment else "N/A" - logger.info(f"Assessment Risk Level: {risk_level}") - print(" ⚠ Analyst review required") - if request.assessment: - print( - f" • Alert: {request.assessment.alert_id} | Risk Score: {request.assessment.overall_risk_score:.2f}" - ) - print(f" • Recommended Action: {request.assessment.recommended_action}") - if request.prompt: - print(f" • Prompt: {request.prompt}") - - # Capture the checkpoint ID from the workflow context - if hasattr(workflow, '_runner_context') and hasattr(workflow._runner_context, '_last_checkpoint_id'): - checkpoint_id = workflow._runner_context._last_checkpoint_id - elif isinstance(event, WorkflowOutputEvent): - if isinstance(event.data, FinalNotification): - output = event.data - print(f"\n{'=' * 80}") - print(" 🎯 WORKFLOW COMPLETED") - print(f"{'=' * 80}") - if output: - print(" Final Output:") - if hasattr(output, "__dict__"): - for key, value in output.__dict__.items(): - if not key.startswith("_"): - print(f" • {key}: {value}") - else: - print(f" {output}") - elif isinstance(event, WorkflowStatusEvent): - print(f" ℹ Status: {event.state.name}") - - logger.info(f"[Event] {event}") - - return pending_requests, output, checkpoint_id - - pending_requests, final_output, last_checkpoint_id = await consume_stream(workflow.run_stream(alerts[0])) - - while final_output is None and pending_requests: - logger.info("\n" + "=" * 80) - logger.info("CHECKPOINT SAVED - Workflow paused for analyst input") - logger.info("=" * 80) - - if last_checkpoint_id: - logger.info(f"Checkpoint ID: {last_checkpoint_id}") - - # Collect analyst decisions from command line - responses: dict[str, AnalystDecision] = {} - for request_event in pending_requests: - assessment = request_event.data.assessment if request_event.data else None - alert_id = assessment.alert_id if assessment else alerts[0].alert_id - customer_id = assessment.customer_id if assessment else alerts[0].customer_id - - print("\n" + "=" * 80) - print(f"ANALYST DECISION REQUIRED") - print("=" * 80) - print(f"Request ID: {request_event.request_id}") - if assessment: - print(f"Alert ID: {assessment.alert_id}") - print(f"Customer ID: {assessment.customer_id}") - print(f"Risk Score: {assessment.overall_risk_score:.2f}") - print(f"Risk Level: {assessment.risk_level}") - print(f"Recommended Action: {assessment.recommended_action}") - print(f"\nReasoning:\n{assessment.reasoning[:500]}...") - - print("\n" + "-" * 80) - print("Available actions:") - print(" 1. clear - No fraud detected, close alert") - print(" 2. lock_account - Lock customer account") - print(" 3. refund_charges - Reverse fraudulent charges") - print(" 4. both - Lock account AND refund charges") - print("-" * 80) - - # Get analyst decision from command line - while True: - action_choice = input("\nEnter action (1-4): ").strip() - action_map = { - "1": "clear", - "2": "lock_account", - "3": "refund_charges", - "4": "both" - } - if action_choice in action_map: - approved_action = action_map[action_choice] - break - print("Invalid choice. Please enter 1, 2, 3, or 4.") - - analyst_notes = input("Enter analyst notes: ").strip() - if not analyst_notes: - analyst_notes = f"Analyst decision: {approved_action}" - - analyst_id = input("Enter analyst ID (default: analyst_cli): ").strip() - if not analyst_id: - analyst_id = "analyst_cli" - - decision = AnalystDecision( - alert_id=alert_id, - customer_id=customer_id, - approved_action=approved_action, - analyst_notes=analyst_notes, - analyst_id=analyst_id, - ) - responses[request_event.request_id] = decision - logger.info(f"[Analyst] Decision for {request_event.request_id}: {decision.approved_action}") - - # Resume workflow with analyst responses - logger.info("\n" + "=" * 80) - logger.info("RESUMING WORKFLOW with analyst decisions") - logger.info("=" * 80) - - pending_requests, final_output, last_checkpoint_id = await consume_stream(workflow.send_responses_streaming(responses)) - - if final_output is None and not pending_requests: - logger.warning( - "Workflow reached an idle state without producing a final notification or pending analyst review." - ) - - logger.info("\n" + "=" * 80) - logger.info("Workflow completed successfully!" if final_output else "Workflow ended without final output.") - if final_output: - logger.info( - "Final notification: alert_id=%s customer_id=%s resolution=%s", - final_output.alert_id, - final_output.customer_id, - final_output.resolution, - ) - logger.info("=" * 80) - - except Exception as e: - logger.error(f"[Error] Workflow failed: {e}", exc_info=True) - - finally: - # Cleanup - await mcp_tool.__aexit__(None, None, None) - logger.info("[Cleanup] MCP connection closed") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/agentic_ai/workflow/fraud_detection/pyproject.toml b/agentic_ai/workflow/fraud_detection/pyproject.toml deleted file mode 100644 index c91139aa2..000000000 --- a/agentic_ai/workflow/fraud_detection/pyproject.toml +++ /dev/null @@ -1,25 +0,0 @@ -[project] -name = "applications" -version = "0.1.0" -description = "Add your description here" -readme = "README.md" -requires-python = ">=3.12" -dependencies = [ - "fastapi==0.115.12", - "agent-framework==1.0.0b260130", - "azure-monitor-opentelemetry>=1.8.5", - "fastmcp==2.7.1", - "flasgger==0.9.7.1", - "flask==3.0.3", - "httpx==0.28.1", - "msal==1.31.0", - "openai>=1.90.0,<1.110.0", - "pydantic==2.11.4", - "requests==2.32.4", - "tenacity==8.5.0", - "uvicorn>=0.25.0", - "websockets>=15.0.1", -] - -[tool.uv] -prerelease = "allow" \ No newline at end of file diff --git a/agentic_ai/workflow/fraud_detection/scenario.md b/agentic_ai/workflow/fraud_detection/scenario.md deleted file mode 100644 index 70abc4f6b..000000000 --- a/agentic_ai/workflow/fraud_detection/scenario.md +++ /dev/null @@ -1,29 +0,0 @@ - -## **Contoso Fraud Detection & Escalation Workflow** - -**Business Context:** -Contoso’s automated systems flag suspicious account activity, but certain cases require a fraud analyst to review before taking action (e.g., locking account, reversing charges). - -**Flow:** -1. **Event Trigger:** Backend monitoring system sends “Suspicious Activity Alert” (login from multiple countries, large data usage spike). -2. **AlertRouterExecutor** → routes alert to multiple analysis executors: - - `UsagePatternExecutor` → checks recent usage against historical baseline. - - `LocationAnalysisExecutor` → checks geolocation anomalies. - - `BillingChargeExecutor` → checks for unusual purchases or charges. -3. **Fan-In** to `FraudRiskAggregatorExecutor`: - - Produces `FraudRiskScore` and recommended action (lock account, refund charges, ignore). -4. **SwitchCaseEdgeRunner**: - - If risk score ≥ threshold → route to `ReviewGatewayExecutor` for human fraud analyst review. - - Else → route to `AutoClearExecutor`. -5. **ReviewGatewayExecutor** → uses `ctx.request_info()` to request analyst review with full context. -6. **Workflow pauses** — checkpoint saved automatically. -7. **Analyst decides** (approve lock/refund or clear). -8. Workflow resumes: - - `FraudActionExecutor` → performs chosen action (e.g., lock account, reverse charges). -9. **FinalNotificationExecutor** → informs customer and logs audit trail. - -**Highlight:** -- Demonstrates **event-driven workflow** triggered by backend system. -- Multiple data sources fan-out and aggregate to produce a single risk assessment. -- Human analyst gating ensures **false positives** don’t harm legitimate customers. -- **Checkpointing** means the workflow can pause indefinitely until analyst responds. diff --git a/agentic_ai/workflow/fraud_detection/start_backend.bat b/agentic_ai/workflow/fraud_detection/start_backend.bat deleted file mode 100644 index 7acd12251..000000000 --- a/agentic_ai/workflow/fraud_detection/start_backend.bat +++ /dev/null @@ -1,17 +0,0 @@ -@echo off -REM Startup script for Fraud Detection Workflow Backend -REM Sets UTF-8 encoding to prevent Unicode errors on Windows - -echo Starting Fraud Detection Workflow Backend... - -REM Set UTF-8 encoding for Python -set PYTHONIOENCODING=utf-8 -set PYTHONUTF8=1 - -REM Change to the script directory -cd /d "%~dp0" - -REM Run with uv -uv run --prerelease allow backend.py - -pause diff --git a/agentic_ai/workflow/fraud_detection/ui/.dockerignore b/agentic_ai/workflow/fraud_detection/ui/.dockerignore deleted file mode 100644 index 778a2ee8b..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.dockerignore +++ /dev/null @@ -1,30 +0,0 @@ -# Development dependencies and build artifacts -node_modules/ -dist/ -build/ - -# Logs -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* - -# Environment files -.env -.env.local -.env.*.local - -# IDE -.vscode/ -.idea/ - -# OS -.DS_Store -Thumbs.db - -# Test coverage -coverage/ - -# Temporary files -*.tmp -*.temp diff --git a/agentic_ai/workflow/fraud_detection/ui/.env.example b/agentic_ai/workflow/fraud_detection/ui/.env.example deleted file mode 100644 index 02c3939e0..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.env.example +++ /dev/null @@ -1,6 +0,0 @@ -# API Configuration -VITE_API_BASE_URL=http://localhost:8001 -VITE_WS_URL=ws://localhost:8001/ws - -# App Configuration -VITE_APP_TITLE=Fraud Detection Workflow Visualizer diff --git a/agentic_ai/workflow/fraud_detection/ui/.eslintrc.cjs b/agentic_ai/workflow/fraud_detection/ui/.eslintrc.cjs deleted file mode 100644 index 7617f77ca..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.eslintrc.cjs +++ /dev/null @@ -1,22 +0,0 @@ -module.exports = { - root: true, - env: { browser: true, es2020: true }, - extends: [ - 'eslint:recommended', - 'plugin:react/recommended', - 'plugin:react/jsx-runtime', - 'plugin:react-hooks/recommended', - ], - ignorePatterns: ['dist', '.eslintrc.cjs'], - parserOptions: { ecmaVersion: 'latest', sourceType: 'module' }, - settings: { react: { version: '19.0' } }, - plugins: ['react-refresh'], - rules: { - 'react/jsx-no-target-blank': 'off', - 'react-refresh/only-export-components': [ - 'warn', - { allowConstantExport: true }, - ], - 'react/prop-types': 'warn', - }, -} diff --git a/agentic_ai/workflow/fraud_detection/ui/.gitignore b/agentic_ai/workflow/fraud_detection/ui/.gitignore deleted file mode 100644 index aa674a799..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.gitignore +++ /dev/null @@ -1,31 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? - -# Environment variables -.env -.env.local -.env.production.local -.env.development.local -.env.test.local diff --git a/agentic_ai/workflow/fraud_detection/ui/.prettierignore b/agentic_ai/workflow/fraud_detection/ui/.prettierignore deleted file mode 100644 index 8c444c5a1..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.prettierignore +++ /dev/null @@ -1,7 +0,0 @@ -node_modules -dist -build -.env -.env.local -*.log -coverage diff --git a/agentic_ai/workflow/fraud_detection/ui/.prettierrc.cjs b/agentic_ai/workflow/fraud_detection/ui/.prettierrc.cjs deleted file mode 100644 index 9f6715a36..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.prettierrc.cjs +++ /dev/null @@ -1,14 +0,0 @@ -// Prettier Configuration -module.exports = { - semi: true, - trailingComma: 'es5', - singleQuote: true, - printWidth: 100, - tabWidth: 2, - useTabs: false, - arrowParens: 'always', - endOfLine: 'lf', - jsxSingleQuote: false, - bracketSpacing: true, - jsxBracketSameLine: false, -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/.vscode/extensions.json b/agentic_ai/workflow/fraud_detection/ui/.vscode/extensions.json deleted file mode 100644 index 82a944c17..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "dbaeumer.vscode-eslint", - "esbenp.prettier-vscode", - "ms-vscode.vscode-typescript-next" - ] -} diff --git a/agentic_ai/workflow/fraud_detection/ui/Dockerfile b/agentic_ai/workflow/fraud_detection/ui/Dockerfile deleted file mode 100644 index f613068f8..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/Dockerfile +++ /dev/null @@ -1,57 +0,0 @@ -# Build stage -FROM node:22-alpine AS builder - -# Set working directory -WORKDIR /app - -# Add metadata -LABEL maintainer="OpenAI Workshop Team" -LABEL description="Fraud Detection UI - React + Vite Application" - -# Copy package files -COPY package*.json ./ - -# Install dependencies with clean install for reproducible builds -RUN npm ci --only=production=false && \ - npm cache clean --force - -# Copy application source -COPY . . - -# Build the application -RUN npm run build - -# Production stage -FROM node:22-alpine AS production - -# Install dumb-init for proper signal handling -RUN apk add --no-cache dumb-init - -# Set working directory -WORKDIR /app - -# Create a non-root user -RUN addgroup -g 1001 -S nodejs && \ - adduser -S nodejs -u 1001 - -# Install serve globally -RUN npm install -g serve - -# Copy built assets from builder stage -COPY --from=builder --chown=nodejs:nodejs /app/dist ./dist - -# Switch to non-root user -USER nodejs - -# Expose port -EXPOSE 3000 - -# Health check -HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \ - CMD wget --no-verbose --tries=1 --spider http://localhost:3000/ || exit 1 - -# Use dumb-init to handle signals properly -ENTRYPOINT ["dumb-init", "--"] - -# Serve the application -CMD ["serve", "-s", "dist", "-l", "3000"] diff --git a/agentic_ai/workflow/fraud_detection/ui/README.md b/agentic_ai/workflow/fraud_detection/ui/README.md deleted file mode 100644 index 9a785b969..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/README.md +++ /dev/null @@ -1,247 +0,0 @@ -# Fraud Detection Workflow Visualizer - -A modern React application for visualizing and managing fraud detection workflows in real-time. Built with Vite, React, Material-UI, and React Flow. - -## Features - -- 🔄 Real-time workflow visualization -- 🎯 Interactive workflow controls -- 📊 Live event logging -- 👤 Human-in-the-loop decision making -- 🔌 WebSocket-based real-time updates -- 🎨 Material-UI based responsive design -- 📈 React Flow powered workflow graphs - -## Tech Stack - -- **Framework**: React 19.2 -- **Build Tool**: Vite 7.2 -- **UI Library**: Material-UI (MUI) 7.3 -- **Graph Visualization**: React Flow 11.11 -- **State Management**: React Hooks -- **Real-time Communication**: WebSocket - -## Prerequisites - -- Node.js 18+ or newer -- npm 9+ or newer - -## Getting Started - -### Installation - -1. Clone the repository -2. Navigate to the project directory: - - ```bash - cd agentic_ai/workflow/fraud_detection/ui - ``` - -3. Install dependencies: - - ```bash - npm install - ``` - -4. Copy the environment file: - - ```bash - cp .env.example .env - ``` - -5. Configure your environment variables in `.env` if needed - -### Development - -Start the development server: - -```bash -npm run dev -``` - -The application will open at `http://localhost:3000` - -### Build - -Build for production: - -```bash -npm run build -``` - -Preview the production build: - -```bash -npm run preview -``` - -### Docker - -Build and run the application in a Docker container: - -```bash -# Build the Docker image -docker build -t fraud-detection-ui:latest . - -# Run the container -docker run -p 3000:3000 fraud-detection-ui:latest - -# Run with custom backend URL -docker run -p 3000:3000 \ - -e VITE_API_BASE_URL=http://your-backend:8001 \ - -e VITE_WS_URL=ws://your-backend:8001/ws \ - fraud-detection-ui:latest -``` - -The Docker image uses a multi-stage build with: - -- **Build stage**: Node.js 22 Alpine with all dependencies -- **Production stage**: Node.js 22 Alpine serving with `serve` -- **Security**: Runs as non-root user (nodejs:1001) -- **Port**: Exposes port 3000 -- **Health check**: Built-in health monitoring - -### Linting - -Run ESLint: - -```bash -npm run lint -``` - -Fix linting issues automatically: - -```bash -npm run lint:fix -``` - -## Project Structure - -```text -ui/ -├── src/ -│ ├── components/ # React components -│ │ ├── AnalystDecisionPanel.jsx -│ │ ├── ControlPanel.jsx -│ │ ├── CustomNode.jsx -│ │ ├── EventLog.jsx -│ │ └── WorkflowVisualizer.jsx -│ ├── hooks/ # Custom React hooks -│ │ └── useWebSocket.js -│ ├── utils/ # Utility functions -│ │ ├── api.js -│ │ ├── helpers.js -│ │ └── uiHelpers.jsx -│ ├── constants/ # Application constants -│ │ ├── config.js -│ │ └── workflow.js -│ ├── theme/ # MUI theme configuration -│ │ └── index.js -│ ├── App.jsx # Main application component -│ ├── main.jsx # Application entry point -│ └── index.css # Global styles -├── public/ # Static assets -├── .env # Environment variables -├── .env.example # Environment variables example -├── .eslintrc.cjs # ESLint configuration -├── .gitignore # Git ignore rules -├── index.html # HTML template -├── jsconfig.json # JavaScript configuration -├── package.json # Project dependencies -├── vite.config.js # Vite configuration -└── README.md # This file -``` - -## Path Aliases - -The project uses path aliases for cleaner imports: - -- `@/` → `src/` -- `@components/` → `src/components/` -- `@hooks/` → `src/hooks/` -- `@utils/` → `src/utils/` -- `@theme/` → `src/theme/` -- `@constants/` → `src/constants/` - -Example: - -```javascript -import WorkflowVisualizer from '@components/WorkflowVisualizer'; -import { useWebSocket } from '@hooks/useWebSocket'; -import theme from '@theme'; -``` - -## Environment Variables - -| Variable | Description | Default | -|----------|-------------|---------| -| `VITE_API_BASE_URL` | Backend API URL | `http://localhost:8001` | -| `VITE_WS_URL` | WebSocket URL | `ws://localhost:8001/ws` | -| `VITE_APP_TITLE` | Application title | `Fraud Detection Workflow Visualizer` | - -## API Integration - -The application connects to a backend API running on `http://localhost:8001` by default. The following endpoints are used: - -- `GET /api/alerts` - Fetch available alerts -- `POST /api/workflow/start` - Start a workflow -- `POST /api/workflow/decision` - Submit analyst decision -- `WS /ws` - WebSocket connection for real-time updates - -## Components - -### Main Components - -- **App.jsx** - Main application component, orchestrates all child components -- **WorkflowVisualizer** - Displays the workflow graph with real-time state updates -- **ControlPanel** - Alert selection and workflow control interface -- **AnalystDecisionPanel** - Human-in-the-loop decision making interface -- **EventLog** - Real-time event logging display -- **CustomNode** - Custom node component for workflow graph - -### Hooks - -- **useWebSocket** - Manages WebSocket connections with automatic reconnection - -## Development Guidelines - -### Code Style - -- Use functional components with hooks -- Follow ESLint rules -- Use path aliases for imports -- Add JSDoc comments for functions -- Keep components small and focused - -### Adding New Components - -1. Create component in `src/components/` -2. Export from the component file -3. Import using path alias: `import Component from '@components/Component'` - -### Adding New Constants - -1. Add constants to appropriate file in `src/constants/` -2. Export named exports -3. Import where needed: `import { CONSTANT } from '@constants/config'` - -## Troubleshooting - -### WebSocket Connection Issues - -- Ensure backend server is running -- Check `VITE_WS_URL` in `.env` -- Check browser console for connection errors - -### Build Issues - -- Clear node_modules and reinstall: `rm -rf node_modules && npm install` -- Clear Vite cache: `rm -rf node_modules/.vite` - -## License - -See the main project LICENSE file. - -## Contributing - -Please read the main project CONTRIBUTING guidelines. diff --git a/agentic_ai/workflow/fraud_detection/ui/index.html b/agentic_ai/workflow/fraud_detection/ui/index.html deleted file mode 100644 index fcdac8966..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/index.html +++ /dev/null @@ -1,16 +0,0 @@ - - - - - - Fraud Detection Workflow Visualizer - - - -
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"optional": true - }, - "immer": { - "optional": true - }, - "react": { - "optional": true - } - } - } - } -} diff --git a/agentic_ai/workflow/fraud_detection/ui/package.json b/agentic_ai/workflow/fraud_detection/ui/package.json deleted file mode 100644 index 77e904866..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/package.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "name": "fraud-detection-ui", - "version": "1.0.0", - "private": true, - "type": "module", - "scripts": { - "dev": "vite", - "build": "vite build", - "preview": "vite preview", - "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0", - "lint:fix": "eslint . --ext js,jsx --fix" - }, - "dependencies": { - "@emotion/react": "^11.14.0", - "@emotion/styled": "^11.14.1", - "@mui/icons-material": "^7.3.6", - "@mui/material": "^7.3.6", - "react": "^19.2.1", - "react-dom": "^19.2.1", - "reactflow": "^11.11.4" - }, - "devDependencies": { - "@vitejs/plugin-react": "^5.1.2", - "eslint": "^9.39.1", - "eslint-plugin-react": "^7.37.5", - "eslint-plugin-react-hooks": "^7.0.1", - "eslint-plugin-react-refresh": "^0.4.24", - "vite": "^7.2.7" - } -} diff --git a/agentic_ai/workflow/fraud_detection/ui/src/App.jsx b/agentic_ai/workflow/fraud_detection/ui/src/App.jsx deleted file mode 100644 index 67f692064..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/App.jsx +++ /dev/null @@ -1,231 +0,0 @@ -import React, { useState, useCallback, useEffect } from 'react'; -import { - Box, - ThemeProvider, - createTheme, - CssBaseline, - AppBar, - Toolbar, - Typography, - Container, - Paper, - Grid, -} from '@mui/material'; -import SecurityIcon from '@mui/icons-material/Security'; -import WorkflowVisualizer from './components/WorkflowVisualizer'; -import ControlPanel from './components/ControlPanel'; -import AnalystDecisionPanel from './components/AnalystDecisionPanel'; -import EventLog from './components/EventLog'; -import { useWebSocket } from './hooks/useWebSocket'; - -const theme = createTheme({ - palette: { - mode: 'light', - primary: { - main: '#1976d2', - }, - secondary: { - main: '#dc004e', - }, - success: { - main: '#4caf50', - }, - warning: { - main: '#ff9800', - }, - error: { - main: '#f44336', - }, - }, - typography: { - fontFamily: '"Roboto", "Helvetica", "Arial", sans-serif', - }, -}); - -function App() { - const [alerts, setAlerts] = useState([]); - const [selectedAlert, setSelectedAlert] = useState(null); - const [workflowRunning, setWorkflowRunning] = useState(false); - const [events, setEvents] = useState([]); - const [pendingDecision, setPendingDecision] = useState(null); - const [executorStates, setExecutorStates] = useState({}); - - // WebSocket hook for real-time updates - const { lastMessage, sendMessage } = useWebSocket('ws://localhost:8001/ws'); - - // Load sample alerts on mount - useEffect(() => { - fetch('/api/alerts') - .then((res) => res.json()) - .then((data) => setAlerts(data.alerts)) - .catch((err) => console.error('Error loading alerts:', err)); - }, []); - - // Handle WebSocket messages - useEffect(() => { - if (!lastMessage) return; - - try { - const event = lastMessage; - - // Add to event log - prevent duplicates by checking timestamp + type + executor_id - setEvents((prev) => { - const eventKey = `${event.timestamp}-${event.type || event.event_type}-${event.executor_id || ''}`; - const isDuplicate = prev.some( - (e) => `${e.timestamp}-${e.type || e.event_type}-${e.executor_id || ''}` === eventKey - ); - return isDuplicate ? prev : [...prev, event]; - }); - - // Handle workflow initialization - if (event.type === 'workflow_initializing') { - // Keep workflow running flag true, just show initialization message - } - - // Handle workflow started - if (event.type === 'workflow_started') { - // Workflow is now running - } - - // Update executor states based on event type - if (event.event_type === 'executor_invoked') { - setExecutorStates((prev) => ({ - ...prev, - [event.executor_id]: 'running', - })); - } else if (event.event_type === 'executor_completed') { - setExecutorStates((prev) => ({ - ...prev, - [event.executor_id]: 'completed', - })); - } - - // Handle decision required - if (event.type === 'decision_required') { - setPendingDecision(event); - setWorkflowRunning(false); - } - - // Handle workflow completion - if (event.type === 'workflow_completed' || event.type === 'workflow_error') { - setWorkflowRunning(false); - // Keep all executor states as-is (they should already be 'completed') - } - } catch (error) { - console.error('Error handling WebSocket message:', error); - } - }, [lastMessage]); - - const handleStartWorkflow = useCallback(async (alert) => { - console.log('Starting workflow for alert:', alert); - setSelectedAlert(alert); - setWorkflowRunning(true); - setEvents([]); - setExecutorStates({}); - setPendingDecision(null); - - try { - const response = await fetch('/api/workflow/start', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify(alert), - }); - - const data = await response.json(); - console.log('Workflow started:', data); - } catch (error) { - console.error('Error starting workflow:', error); - setWorkflowRunning(false); - } - }, []); - - const handleSubmitDecision = useCallback(async (decision) => { - console.log('Submitting decision:', decision); - - try { - const response = await fetch('/api/workflow/decision', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify(decision), - }); - - const data = await response.json(); - console.log('Decision submitted:', data); - - setPendingDecision(null); - setWorkflowRunning(true); - } catch (error) { - console.error('Error submitting decision:', error); - } - }, []); - - return ( - - - - {/* App Bar */} - - - - - Fraud Detection Workflow Visualizer - - - Real-time Multi-Agent Workflow Monitoring - - - - - {/* Main Content */} - - - {/* Left Column - Controls and Decision Panel */} - - - - {pendingDecision && ( - - )} - - - {/* Center Column - Workflow Visualization */} - - - - Workflow Graph - - {selectedAlert - ? `Alert: ${selectedAlert.alert_id} - ${selectedAlert.description}` - : 'Select an alert to start'} - - - - - - - - - {/* Right Column - Event Log */} - - - - - - - - ); -} - -export default App; \ No newline at end of file diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx deleted file mode 100644 index d2cea18f6..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx +++ /dev/null @@ -1,179 +0,0 @@ -import { useState } from 'react'; -import { - Paper, - Box, - Typography, - Button, - TextField, - Select, - MenuItem, - FormControl, - InputLabel, - Chip, - Alert, - Divider, -} from '@mui/material'; -import GavelIcon from '@mui/icons-material/Gavel'; -import SendIcon from '@mui/icons-material/Send'; -import { ACTION_OPTIONS } from '../constants/workflow'; -import { getRiskLevel, getRiskColor } from '../utils/uiHelpers'; - -/** - * Panel for analyst to make decisions on fraud alerts - * @param {Object} props - Component props - * @param {Object} props.decision - Decision request object - * @param {Function} props.onSubmit - Callback to submit decision - */ -function AnalystDecisionPanel({ decision, onSubmit }) { - const [selectedAction, setSelectedAction] = useState( - decision.data?.recommended_action || 'clear' - ); - const [notes, setNotes] = useState(''); - - const handleSubmit = () => { - onSubmit({ - request_id: decision.request_id, - alert_id: decision.data.alert_id, - customer_id: decision.data.customer_id, - approved_action: selectedAction, - analyst_notes: notes || 'Analyst decision from UI', - analyst_id: 'analyst_ui', - }); - }; - - return ( - - - - Analyst Review Required - - - - - Human Decision Needed - - - - - - {/* Risk Assessment */} - - - Risk Assessment - - - Risk Score: - - - - Alert ID: - - - - - {/* Reasoning */} - {decision.data?.reasoning && ( - - - AI Analysis - - - - {decision.data.reasoning.length > 500 - ? decision.data.reasoning.substring(0, 500) + '...' - : decision.data.reasoning} - - - - )} - - {/* Recommended Action */} - - - Recommended Action - - opt.value === decision.data?.recommended_action) - ?.label || 'Unknown' - } - color={ - ACTION_OPTIONS.find((opt) => opt.value === decision.data?.recommended_action) - ?.color || 'default' - } - size="small" - sx={{ height: 20, fontSize: '0.7rem' }} - /> - - - - - {/* Decision Form */} - - Your Decision - - - - setNotes(e.target.value)} - placeholder="Add notes..." - sx={{ '& .MuiInputBase-input': { fontSize: '0.875rem' } }} - /> - - - - ); -} - -export default AnalystDecisionPanel; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/ControlPanel.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/ControlPanel.jsx deleted file mode 100644 index acacc11f1..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/ControlPanel.jsx +++ /dev/null @@ -1,128 +0,0 @@ -import { useState } from 'react'; -import { - Paper, - Box, - Typography, - Button, - Select, - MenuItem, - FormControl, - InputLabel, - Chip, - CircularProgress, -} from '@mui/material'; -import PlayArrowIcon from '@mui/icons-material/PlayArrow'; -import WarningIcon from '@mui/icons-material/Warning'; -import ErrorIcon from '@mui/icons-material/Error'; -import InfoIcon from '@mui/icons-material/Info'; -import { getSeverityIcon, getSeverityColor } from '../utils/uiHelpers'; - -/** - * Control panel component for selecting alerts and starting workflows - * @param {Object} props - Component props - * @param {Array} props.alerts - Array of alert objects - * @param {Function} props.onStartWorkflow - Callback to start workflow - * @param {boolean} props.workflowRunning - Whether workflow is currently running - * @param {Object} props.selectedAlert - Currently selected alert object - */ -function ControlPanel({ alerts = [], onStartWorkflow, workflowRunning = false, selectedAlert }) { - const [selectedAlertId, setSelectedAlertId] = useState(''); - - const handleStartClick = () => { - const alert = alerts.find((a) => a.alert_id === selectedAlertId); - if (alert) { - onStartWorkflow(alert); - } - }; - - return ( - - - Workflow Control - - - - Select Alert - - - - {selectedAlertId && !workflowRunning && ( - - - Description: - - - {alerts.find((a) => a.alert_id === selectedAlertId)?.description} - - - a.alert_id === selectedAlertId)?.customer_id}`} - size="small" - variant="outlined" - sx={{ height: 18, fontSize: '0.7rem' }} - /> - a.alert_id === selectedAlertId)?.alert_type} - size="small" - variant="outlined" - sx={{ height: 18, fontSize: '0.7rem' }} - /> - - - )} - - - - {selectedAlert && workflowRunning && ( - - - Active Workflow - - - Processing {selectedAlert.alert_id} - - - )} - - ); -} - -export default ControlPanel; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/CustomNode.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/CustomNode.jsx deleted file mode 100644 index a39a9c0be..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/CustomNode.jsx +++ /dev/null @@ -1,81 +0,0 @@ -import { Handle, Position } from 'reactflow'; -import { Box, Typography, Paper, Chip } from '@mui/material'; -import CheckCircleIcon from '@mui/icons-material/CheckCircle'; -import HourglassEmptyIcon from '@mui/icons-material/HourglassEmpty'; -import CircleIcon from '@mui/icons-material/Circle'; -import { getNodeStatusColor, getStatusLabel } from '../utils/uiHelpers'; - -/** - * Gets the appropriate icon for node status - * @param {string} status - Node status - * @returns {JSX.Element} Icon component - */ -const getStatusIcon = (status) => { - switch (status) { - case 'running': - return ; - case 'completed': - return ; - default: - return ; - } -}; - -/** - * Custom node component for React Flow workflow visualization - * @param {Object} props - Component props - * @param {Object} props.data - Node data object - */ -function CustomNode({ data = {} }) { - const statusColor = getNodeStatusColor(data.status); - const isActive = data.status === 'running'; - - return ( - - - - - - - {data.label} - - - - - {data.description && ( - - {data.description} - - )} - - - - - ); -} - -export default CustomNode; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/EventLog.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/EventLog.jsx deleted file mode 100644 index f27343e96..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/EventLog.jsx +++ /dev/null @@ -1,121 +0,0 @@ -import { useRef, useEffect, Fragment } from 'react'; -import { - Paper, - Box, - Typography, - List, - ListItem, - ListItemIcon, - ListItemText, - Chip, - Divider, -} from '@mui/material'; -import PlayArrowIcon from '@mui/icons-material/PlayArrow'; -import CheckCircleIcon from '@mui/icons-material/CheckCircle'; -import InfoIcon from '@mui/icons-material/Info'; -import GavelIcon from '@mui/icons-material/Gavel'; -import ErrorIcon from '@mui/icons-material/Error'; -import { getEventIcon, getEventColor, getEventTitle } from '../utils/uiHelpers'; -import { formatTime } from '../utils/helpers'; - -/** - * Event log component for displaying workflow events in real-time - * @param {Object} props - Component props - * @param {Array} props.events - Array of event objects - */ -function EventLog({ events = [] }) { - const listRef = useRef(null); - - // Auto-scroll to bottom when new events arrive - useEffect(() => { - if (listRef.current) { - listRef.current.scrollTop = listRef.current.scrollHeight; - } - }, [events]); - - const icons = { PlayArrowIcon, CheckCircleIcon, InfoIcon, GavelIcon, ErrorIcon }; - - return ( - - - Event Log - - {events.length} events - - - - - {events.length === 0 ? ( - - - No events yet. Start a workflow to see events. - - - ) : ( - events.map((event, index) => ( - - - - {getEventIcon(event, icons)} - - - - {getEventTitle(event)} - - - - } - secondary={ - - {formatTime(event.timestamp)} - - } - /> - - {index < events.length - 1 && } - - )) - )} - - - ); -} - -export default EventLog; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx deleted file mode 100644 index 1852c058f..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx +++ /dev/null @@ -1,151 +0,0 @@ -import { useMemo, useEffect } from 'react'; -import ReactFlow, { - Background, - Controls, - MiniMap, - useNodesState, - useEdgesState, -} from 'reactflow'; -import 'reactflow/dist/style.css'; -import { Box } from '@mui/material'; -import CustomNode from './CustomNode'; -import { EXECUTOR_STATES } from '../constants/workflow'; - -const nodeTypes = { - custom: CustomNode, -}; - -// Define the workflow graph structure -const initialNodes = [ - { - id: 'alert_router', - type: 'custom', - position: { x: 400, y: 50 }, - data: { label: 'Alert Router', status: 'idle', description: 'Routes alerts to analysts' }, - }, - { - id: 'usage_pattern_executor', - type: 'custom', - position: { x: 150, y: 200 }, - data: { label: 'Usage Analyst', status: 'idle', description: 'Analyzes usage patterns' }, - }, - { - id: 'location_analysis_executor', - type: 'custom', - position: { x: 400, y: 200 }, - data: { label: 'Location Analyst', status: 'idle', description: 'Analyzes location anomalies' }, - }, - { - id: 'billing_charge_executor', - type: 'custom', - position: { x: 650, y: 200 }, - data: { label: 'Billing Analyst', status: 'idle', description: 'Analyzes billing patterns' }, - }, - { - id: 'fraud_risk_aggregator', - type: 'custom', - position: { x: 400, y: 350 }, - data: { label: 'Risk Aggregator', status: 'idle', description: 'Aggregates risk assessment' }, - }, - { - id: 'review_gateway', - type: 'custom', - position: { x: 550, y: 500 }, - data: { label: 'Review Gateway', status: 'idle', description: 'Human analyst review (pauses workflow)' }, - }, - { - id: 'auto_clear_executor', - type: 'custom', - position: { x: 250, y: 500 }, - data: { label: 'Auto Clear', status: 'idle', description: 'Auto-clears low risk' }, - }, - { - id: 'fraud_action_executor', - type: 'custom', - position: { x: 550, y: 650 }, - data: { label: 'Fraud Action', status: 'idle', description: 'Execute fraud action' }, - }, - { - id: 'final_notification_executor', - type: 'custom', - position: { x: 400, y: 800 }, - data: { label: 'Final Notification', status: 'idle', description: 'Send notifications' }, - }, -]; - -const initialEdges = [ - // Fan-out from alert router to analysts - { id: 'e1-1', source: 'alert_router', target: 'usage_pattern_executor', animated: true }, - { id: 'e1-2', source: 'alert_router', target: 'location_analysis_executor', animated: true }, - { id: 'e1-3', source: 'alert_router', target: 'billing_charge_executor', animated: true }, - - // Fan-in to aggregator - { id: 'e2-1', source: 'usage_pattern_executor', target: 'fraud_risk_aggregator' }, - { id: 'e2-2', source: 'location_analysis_executor', target: 'fraud_risk_aggregator' }, - { id: 'e2-3', source: 'billing_charge_executor', target: 'fraud_risk_aggregator' }, - - // Switch case from aggregator - { id: 'e3-1', source: 'fraud_risk_aggregator', target: 'review_gateway', label: 'High Risk', style: { stroke: '#f44336' } }, - { id: 'e3-2', source: 'fraud_risk_aggregator', target: 'auto_clear_executor', label: 'Low Risk', style: { stroke: '#4caf50' } }, - - // Review gateway to fraud action (human review happens via request_info, then proceeds) - { id: 'e4-1', source: 'review_gateway', target: 'fraud_action_executor', animated: true, style: { stroke: '#ff9800' } }, - - // Final paths - { id: 'e5-1', source: 'auto_clear_executor', target: 'final_notification_executor' }, - { id: 'e5-2', source: 'fraud_action_executor', target: 'final_notification_executor' }, -]; - -/** - * Workflow visualizer component using React Flow - * Displays the fraud detection workflow as an interactive graph - * @param {Object} props - Component props - * @param {Object} props.executorStates - Map of executor IDs to their current states - */ -function WorkflowVisualizer({ executorStates = {} }) { - // Update nodes with current executor states - const nodes = useMemo(() => { - return initialNodes.map((node) => ({ - ...node, - data: { - ...node.data, - status: executorStates[node.id] || EXECUTOR_STATES.IDLE, - }, - })); - }, [executorStates]); - - const [nodesState, setNodes, onNodesChange] = useNodesState(nodes); - const [edgesState, setEdges, onEdgesChange] = useEdgesState(initialEdges); - - // Update nodes when executor states change - useEffect(() => { - setNodes(nodes); - }, [nodes, setNodes]); - - return ( - - - - - { - const status = node.data.status; - if (status === 'running') return '#1976d2'; - if (status === 'completed') return '#4caf50'; - return '#9e9e9e'; - }} - /> - - - ); -} - -export default WorkflowVisualizer; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/constants/config.js b/agentic_ai/workflow/fraud_detection/ui/src/constants/config.js deleted file mode 100644 index b5bbabc03..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/constants/config.js +++ /dev/null @@ -1,33 +0,0 @@ -/** - * API configuration constants - */ -export const API_CONFIG = { - BASE_URL: import.meta.env.VITE_API_BASE_URL || 'http://localhost:8001', - WS_URL: import.meta.env.VITE_WS_URL || 'ws://localhost:8001/ws', - ENDPOINTS: { - ALERTS: '/api/alerts', - WORKFLOW_START: '/api/workflow/start', - WORKFLOW_DECISION: '/api/workflow/decision', - }, -}; - -/** - * WebSocket configuration - */ -export const WS_CONFIG = { - RECONNECT_DELAY: 3000, - MAX_RECONNECT_ATTEMPTS: 10, -}; - -/** - * Application constants - */ -export const APP_CONFIG = { - TITLE: import.meta.env.VITE_APP_TITLE || 'Fraud Detection Workflow Visualizer', -}; - -export default { - API_CONFIG, - WS_CONFIG, - APP_CONFIG, -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/constants/workflow.js b/agentic_ai/workflow/fraud_detection/ui/src/constants/workflow.js deleted file mode 100644 index 07d791540..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/constants/workflow.js +++ /dev/null @@ -1,53 +0,0 @@ -/** - * Workflow event types - */ -export const EVENT_TYPES = { - WORKFLOW_INITIALIZING: 'workflow_initializing', - WORKFLOW_STARTED: 'workflow_started', - WORKFLOW_COMPLETED: 'workflow_completed', - WORKFLOW_ERROR: 'workflow_error', - EXECUTOR_INVOKED: 'executor_invoked', - EXECUTOR_COMPLETED: 'executor_completed', - DECISION_REQUIRED: 'decision_required', - STATUS_CHANGE: 'status_change', - WORKFLOW_OUTPUT: 'workflow_output', -}; - -/** - * Alert severity levels - */ -export const SEVERITY_LEVELS = { - HIGH: 'high', - MEDIUM: 'medium', - LOW: 'low', -}; - -/** - * Executor states - */ -export const EXECUTOR_STATES = { - IDLE: 'idle', - RUNNING: 'running', - COMPLETED: 'completed', - ERROR: 'error', -}; - -/** - * Fraud action options - */ -export const ACTION_OPTIONS = [ - { value: 'clear', label: 'Clear - No Action Needed', color: 'success' }, - { value: 'lock_account', label: 'Lock Account', color: 'error' }, - { value: 'refund_charges', label: 'Refund Charges', color: 'warning' }, - { value: 'both', label: 'Lock Account & Refund', color: 'error' }, -]; - -/** - * WebSocket ready states - */ -export const WS_READY_STATES = { - CONNECTING: 'CONNECTING', - OPEN: 'OPEN', - CLOSING: 'CLOSING', - CLOSED: 'CLOSED', -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js b/agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js deleted file mode 100644 index dd9a4f13d..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js +++ /dev/null @@ -1,113 +0,0 @@ -import { useState, useEffect, useRef, useCallback } from 'react'; -import { WS_CONFIG } from '../constants/config'; - -/** - * Custom hook for managing WebSocket connections with automatic reconnection - * Uses a message queue to ensure no messages are lost when they arrive quickly - * @param {string} url - WebSocket URL to connect to - * @returns {Object} Object containing lastMessage, readyState, and sendMessage function - */ -export function useWebSocket(url) { - const [lastMessage, setLastMessage] = useState(null); - const [readyState, setReadyState] = useState('CONNECTING'); - const ws = useRef(null); - const reconnectTimeout = useRef(null); - const reconnectAttempts = useRef(0); - - // Message queue to prevent lost messages during rapid updates - const messageQueue = useRef([]); - const processingQueue = useRef(false); - - // Process messages from queue one at a time - const processQueue = useCallback(() => { - if (processingQueue.current || messageQueue.current.length === 0) { - return; - } - - processingQueue.current = true; - const message = messageQueue.current.shift(); - - // Use setTimeout to ensure React processes each state update - setLastMessage(message); - - // Schedule next message processing - setTimeout(() => { - processingQueue.current = false; - processQueue(); - }, 0); - }, []); - - const connect = useCallback(() => { - try { - ws.current = new WebSocket(url); - - ws.current.onopen = () => { - console.log('WebSocket connected'); - setReadyState('OPEN'); - reconnectAttempts.current = 0; - }; - - ws.current.onmessage = (event) => { - try { - const data = JSON.parse(event.data); - // Add to queue instead of directly setting state - messageQueue.current.push(data); - processQueue(); - } catch (error) { - console.error('Error parsing WebSocket message:', error); - } - }; - - ws.current.onerror = (error) => { - console.error('WebSocket error:', error); - }; - - ws.current.onclose = () => { - console.log('WebSocket disconnected'); - setReadyState('CLOSED'); - - // Attempt to reconnect if under max attempts - if (reconnectAttempts.current < WS_CONFIG.MAX_RECONNECT_ATTEMPTS) { - reconnectTimeout.current = setTimeout(() => { - reconnectAttempts.current += 1; - console.log( - `Attempting to reconnect... (${reconnectAttempts.current}/${WS_CONFIG.MAX_RECONNECT_ATTEMPTS})` - ); - connect(); - }, WS_CONFIG.RECONNECT_DELAY); - } else { - console.error('Max reconnection attempts reached'); - } - }; - } catch (error) { - console.error('Error creating WebSocket:', error); - } - }, [url, processQueue]); - - useEffect(() => { - connect(); - - return () => { - if (reconnectTimeout.current) { - clearTimeout(reconnectTimeout.current); - } - if (ws.current) { - ws.current.close(); - } - }; - }, [connect]); - - const sendMessage = useCallback((message) => { - if (ws.current && ws.current.readyState === WebSocket.OPEN) { - ws.current.send(JSON.stringify(message)); - } else { - console.warn('WebSocket is not open. Ready state:', ws.current?.readyState); - } - }, []); - - return { - lastMessage, - readyState, - sendMessage, - }; -} diff --git a/agentic_ai/workflow/fraud_detection/ui/src/index.css b/agentic_ai/workflow/fraud_detection/ui/src/index.css deleted file mode 100644 index 0979488ae..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/index.css +++ /dev/null @@ -1,18 +0,0 @@ -body { - margin: 0; - font-family: 'Roboto', -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Oxygen', - 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', - sans-serif; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; - background-color: #f5f5f5; -} - -* { - box-sizing: border-box; -} - -#root { - height: 100vh; - width: 100vw; -} diff --git a/agentic_ai/workflow/fraud_detection/ui/src/main.jsx b/agentic_ai/workflow/fraud_detection/ui/src/main.jsx deleted file mode 100644 index 8e3e8340e..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/main.jsx +++ /dev/null @@ -1,18 +0,0 @@ -import { StrictMode } from 'react'; -import { createRoot } from 'react-dom/client'; -import App from './App'; -import './index.css'; - -const rootElement = document.getElementById('root'); - -if (!rootElement) { - throw new Error('Failed to find the root element'); -} - -const root = createRoot(rootElement); - -root.render( - - - -); diff --git a/agentic_ai/workflow/fraud_detection/ui/src/theme/index.js b/agentic_ai/workflow/fraud_detection/ui/src/theme/index.js deleted file mode 100644 index 799f49a2f..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/theme/index.js +++ /dev/null @@ -1,84 +0,0 @@ -import { createTheme } from '@mui/material'; - -/** - * Application theme configuration - * Uses Material-UI theme customization - */ -export const theme = createTheme({ - palette: { - mode: 'light', - primary: { - main: '#1976d2', - light: '#42a5f5', - dark: '#1565c0', - contrastText: '#fff', - }, - secondary: { - main: '#dc004e', - light: '#f33a6a', - dark: '#9a0036', - contrastText: '#fff', - }, - success: { - main: '#4caf50', - light: '#81c784', - dark: '#388e3c', - }, - warning: { - main: '#ff9800', - light: '#ffb74d', - dark: '#f57c00', - }, - error: { - main: '#f44336', - light: '#e57373', - dark: '#d32f2f', - }, - info: { - main: '#2196f3', - light: '#64b5f6', - dark: '#1976d2', - }, - background: { - default: '#f5f5f5', - paper: '#ffffff', - }, - }, - typography: { - fontFamily: '"Roboto", "Helvetica", "Arial", sans-serif', - h6: { - fontWeight: 500, - }, - subtitle1: { - fontWeight: 500, - }, - body1: { - fontSize: '0.875rem', - }, - body2: { - fontSize: '0.75rem', - }, - }, - shape: { - borderRadius: 8, - }, - components: { - MuiButton: { - styleOverrides: { - root: { - textTransform: 'none', - borderRadius: 8, - }, - }, - }, - MuiPaper: { - styleOverrides: { - root: { - borderRadius: 8, - }, - }, - }, - }, -}); - -export default theme; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/utils/api.js b/agentic_ai/workflow/fraud_detection/ui/src/utils/api.js deleted file mode 100644 index 26ea9986c..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/utils/api.js +++ /dev/null @@ -1,73 +0,0 @@ -import { API_CONFIG } from '../constants/config'; - -/** - * Fetches alerts from the API - * @returns {Promise} Array of alerts - */ -export const fetchAlerts = async () => { - try { - const response = await fetch(`${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.ALERTS}`); - if (!response.ok) { - throw new Error(`HTTP error! status: ${response.status}`); - } - const data = await response.json(); - return data.alerts || []; - } catch (error) { - console.error('Error loading alerts:', error); - throw error; - } -}; - -/** - * Starts a workflow for a given alert - * @param {Object} alert - The alert object - * @returns {Promise} Response data - */ -export const startWorkflow = async (alert) => { - try { - const response = await fetch(`${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.WORKFLOW_START}`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify(alert), - }); - - if (!response.ok) { - throw new Error(`HTTP error! status: ${response.status}`); - } - - const data = await response.json(); - return data; - } catch (error) { - console.error('Error starting workflow:', error); - throw error; - } -}; - -/** - * Submits an analyst decision - * @param {Object} decision - The decision object - * @returns {Promise} Response data - */ -export const submitDecision = async (decision) => { - try { - const response = await fetch(`${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.WORKFLOW_DECISION}`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify(decision), - }); - - if (!response.ok) { - throw new Error(`HTTP error! status: ${response.status}`); - } - - const data = await response.json(); - return data; - } catch (error) { - console.error('Error submitting decision:', error); - throw error; - } -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/utils/helpers.js b/agentic_ai/workflow/fraud_detection/ui/src/utils/helpers.js deleted file mode 100644 index 8f303b5cc..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/utils/helpers.js +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Event helpers for workflow event processing - */ - -/** - * Generates a unique key for an event - * @param {Object} event - The event object - * @returns {string} Unique event key - */ -export const generateEventKey = (event) => { - return `${event.timestamp}-${event.type || event.event_type}-${event.executor_id || ''}`; -}; - -/** - * Checks if an event is a duplicate in an array of events - * @param {Object} event - The event to check - * @param {Array} events - Existing events array - * @returns {boolean} True if duplicate - */ -export const isDuplicateEvent = (event, events) => { - const eventKey = generateEventKey(event); - return events.some((e) => generateEventKey(e) === eventKey); -}; - -/** - * Formats a timestamp for display - * @param {string|number} timestamp - The timestamp - * @returns {string} Formatted time string - */ -export const formatTime = (timestamp) => { - const date = new Date(timestamp); - return date.toLocaleTimeString('en-US', { - hour: '2-digit', - minute: '2-digit', - second: '2-digit', - hour12: false, - }); -}; - -/** - * Formats a date and time for display - * @param {string|number} timestamp - The timestamp - * @returns {string} Formatted datetime string - */ -export const formatDateTime = (timestamp) => { - const date = new Date(timestamp); - return date.toLocaleString('en-US', { - month: 'short', - day: 'numeric', - hour: '2-digit', - minute: '2-digit', - second: '2-digit', - hour12: false, - }); -}; - -/** - * Truncates text to a maximum length - * @param {string} text - Text to truncate - * @param {number} maxLength - Maximum length - * @returns {string} Truncated text - */ -export const truncateText = (text, maxLength = 100) => { - if (!text || text.length <= maxLength) return text; - return `${text.substring(0, maxLength)}...`; -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/utils/uiHelpers.jsx b/agentic_ai/workflow/fraud_detection/ui/src/utils/uiHelpers.jsx deleted file mode 100644 index 83723375a..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/utils/uiHelpers.jsx +++ /dev/null @@ -1,189 +0,0 @@ -/** - * UI helper functions for components - */ - -/** - * Gets the appropriate icon for alert severity - * @param {string} severity - Alert severity level - * @param {Object} icons - Icon components object - * @returns {JSX.Element} Icon component - */ -export const getSeverityIcon = (severity, icons) => { - const { ErrorIcon, WarningIcon, InfoIcon } = icons; - switch (severity) { - case 'high': - return ; - case 'medium': - return ; - case 'low': - return ; - default: - return ; - } -}; - -/** - * Gets the appropriate color for alert severity - * @param {string} severity - Alert severity level - * @returns {string} MUI color name - */ -export const getSeverityColor = (severity) => { - switch (severity) { - case 'high': - return 'error'; - case 'medium': - return 'warning'; - case 'low': - return 'info'; - default: - return 'default'; - } -}; - -/** - * Gets the appropriate icon for event type - * @param {Object} event - Event object - * @param {Object} icons - Icon components object - * @returns {JSX.Element} Icon component - */ -export const getEventIcon = (event, icons) => { - const { PlayArrowIcon, CheckCircleIcon, InfoIcon, GavelIcon, ErrorIcon } = icons; - - switch (event.event_type) { - case 'executor_invoked': - return ; - case 'executor_completed': - return ; - case 'status_change': - return ; - case 'workflow_output': - return ; - default: - if (event.type === 'decision_required') { - return ; - } - if (event.type === 'workflow_error') { - return ; - } - return ; - } -}; - -/** - * Gets the appropriate color for event type - * @param {Object} event - Event object - * @returns {string} MUI color name - */ -export const getEventColor = (event) => { - switch (event.event_type) { - case 'executor_invoked': - return 'primary'; - case 'executor_completed': - return 'success'; - case 'status_change': - return 'info'; - case 'workflow_output': - return 'success'; - default: - if (event.type === 'decision_required') { - return 'warning'; - } - if (event.type === 'workflow_error') { - return 'error'; - } - return 'default'; - } -}; - -/** - * Gets the display title for an event - * @param {Object} event - Event object - * @returns {string} Event title - */ -export const getEventTitle = (event) => { - if (event.event_type === 'executor_invoked') { - return `${event.executor_id} started`; - } - if (event.event_type === 'executor_completed') { - return `${event.executor_id} completed`; - } - if (event.event_type === 'status_change') { - return `Status: ${event.status}`; - } - if (event.event_type === 'workflow_output') { - return 'Workflow Output'; - } - if (event.type === 'decision_required') { - return 'Decision Required'; - } - if (event.type === 'workflow_started') { - return 'Workflow Started'; - } - if (event.type === 'workflow_completed') { - return 'Workflow Completed'; - } - if (event.type === 'workflow_error') { - return 'Error Occurred'; - } - return event.type || 'Event'; -}; - -/** - * Gets risk level from risk score - * @param {number} score - Risk score (0-1) - * @returns {string} Risk level - */ -export const getRiskLevel = (score) => { - if (score >= 0.8) return 'Critical'; - if (score >= 0.6) return 'High'; - if (score >= 0.3) return 'Medium'; - return 'Low'; -}; - -/** - * Gets risk color from risk score - * @param {number} score - Risk score (0-1) - * @returns {string} MUI color name - */ -export const getRiskColor = (score) => { - if (score >= 0.8) return 'error'; - if (score >= 0.6) return 'warning'; - if (score >= 0.3) return 'info'; - return 'success'; -}; - -/** - * Gets node status color configuration - * @param {string} status - Node status - * @returns {Object} Color configuration object - */ -export const getNodeStatusColor = (status) => { - switch (status) { - case 'running': - return { bg: '#1976d2', text: '#ffffff' }; - case 'completed': - return { bg: '#4caf50', text: '#ffffff' }; - case 'error': - return { bg: '#f44336', text: '#ffffff' }; - default: - return { bg: '#ffffff', text: '#000000' }; - } -}; - -/** - * Gets status label for display - * @param {string} status - Status value - * @returns {string} Display label - */ -export const getStatusLabel = (status) => { - switch (status) { - case 'running': - return 'Running'; - case 'completed': - return 'Completed'; - case 'error': - return 'Error'; - default: - return 'Idle'; - } -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/vite.config.js b/agentic_ai/workflow/fraud_detection/ui/vite.config.js deleted file mode 100644 index e05580dca..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/vite.config.js +++ /dev/null @@ -1,34 +0,0 @@ -import { defineConfig } from 'vite' -import react from '@vitejs/plugin-react' - -// https://vitejs.dev/config/ -export default defineConfig({ - plugins: [react()], - server: { - port: 3000, - open: true, - proxy: { - '/api': { - target: 'http://localhost:8001', - changeOrigin: true, - }, - '/ws': { - target: 'ws://localhost:8001', - ws: true, - }, - }, - }, - build: { - outDir: 'dist', - sourcemap: true, - rollupOptions: { - output: { - manualChunks: { - vendor: ['react', 'react-dom'], - mui: ['@mui/material', '@mui/icons-material'], - reactflow: ['reactflow'], - }, - }, - }, - }, -}) diff --git a/agentic_ai/workflow/fraud_detection/uv.lock b/agentic_ai/workflow/fraud_detection/uv.lock deleted file mode 100644 index 0576fe255..000000000 --- a/agentic_ai/workflow/fraud_detection/uv.lock +++ /dev/null @@ -1,3039 +0,0 @@ -version = 1 -revision = 1 -requires-python = ">=3.12" -resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version < '3.13'", -] - -[options] -prerelease-mode = "allow" - -[[package]] -name = "a2a-sdk" -version = "0.3.8" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "google-api-core" }, - { name = "httpx" }, - { name = "httpx-sse" }, - { name = "protobuf" }, - { name = "pydantic" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/b3/dc/253ca3a07a43f49d1cf029dcbbcde8b101a3dbee1c115e45deae9c79625b/a2a_sdk-0.3.8.tar.gz", hash = "sha256:7ce8bcddb431db3f29753d602e9fa97dd060ef6c5db64542b5109302941f0d17", size = 223907 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/73/23/bf86f1d3a04a6d967176e20939a98207f8a8ed49480622ad6cf5ce232083/a2a_sdk-0.3.8-py3-none-any.whl", hash = "sha256:21254dd47d89a958b9d15576a69fe3d44aaef558858d148e187d1f1e26b320e7", size = 138098 }, -] - 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In production every process maps to an **Azure Container App (ACA)** revision — but the topology is _not_ uniform. The worker has **no ingress at all**; the MCP server is **internal-only**; and the backend is the **sole external surface**. + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#4A90D9', 'primaryTextColor': '#fff', 'primaryBorderColor': '#2E6BA4', 'lineColor': '#5C6B77', 'secondaryColor': '#F5A623', 'tertiaryColor': '#E8F4FD'}}}%% +flowchart TB + subgraph INTERNET["☁️ Internet"] + USER["🧑‍💻 Analyst / Browser"] + end + + subgraph AZURE["Azure Subscription"] + subgraph ACA_ENV["Azure Container Apps Environment"] + direction TB + + subgraph BACKEND["Backend (ACA)
── only external ingress ──"] + BE_APP["FastAPI BFF
REST + SSE + WebSocket
Port 8001"] + end + + subgraph WORKER["Worker (ACA)
── NO ingress ──"] + W_APP["DTS Worker
Pull-based gRPC
FraudAnalysisAgent Entity
Orchestration + Activities"] + end + + subgraph MCP_SVC["MCP Server (ACA)
── internal-only ingress ──"] + MCP_APP["FastMCP
Contoso Tools
Port 8000"] + end + end + + DTS["🔷 Azure Durable Task
Scheduler (DTS)
──────────────
Task Hub · Event Store
Timer Service"] + AOAI["🤖 Azure OpenAI
gpt-4o / gpt-5"] + COSMOS["🗄️ Cosmos DB
Customer Data
(replaces SQLite)"] + EVENTHUB["📡 Event Hubs
Real Telemetry
(replaces synthetic producer)"] + APPINS["📊 Application Insights
OpenTelemetry"] + SWA["🌐 Azure Static Web Apps
React UI"] + end + + USER -->|"HTTPS"| SWA + SWA -->|"API calls"| BE_APP + USER -->|"REST / SSE / WS"| BE_APP + + BE_APP -->|"gRPC (schedule_new_orchestration,
raise_event, get_instance)"| DTS + W_APP -.->|"gRPC PULL
(long-poll for work items)"| DTS + W_APP -->|"Streamable HTTP"| MCP_APP + W_APP -->|"Chat Completions API"| AOAI + MCP_APP -->|"Data queries"| COSMOS + EVENTHUB -->|"Ingested by"| BE_APP + + BE_APP -.->|"traces"| APPINS + W_APP -.->|"traces"| APPINS + + style INTERNET fill:#f0f4f8,stroke:#ccc + style AZURE fill:#e8f4fd,stroke:#2E6BA4,stroke-width:2px + style ACA_ENV fill:#fff,stroke:#4A90D9,stroke-width:2px + style BACKEND fill:#d4edda,stroke:#28a745 + style WORKER fill:#ffeeba,stroke:#ffc107 + style MCP_SVC fill:#d6d8db,stroke:#6c757d + style DTS fill:#cce5ff,stroke:#004085 + style AOAI fill:#e2d5f1,stroke:#6f42c1 + style COSMOS fill:#fff3cd,stroke:#856404 + style EVENTHUB fill:#f8d7da,stroke:#721c24 + style APPINS fill:#d1ecf1,stroke:#0c5460 + style SWA fill:#d4edda,stroke:#155724 +``` + +--- + +## Component Deep-Dive + +### 1. Worker — No Ingress, Pull-Based + +| Property | Value | +|----------|-------| +| **Ingress** | None — no HTTP port exposed | +| **Communication** | Outbound gRPC to DTS (long-poll for work items) | +| **Scaling trigger** | KEDA scaler watching DTS task-hub queue depth | +| **Min replicas** | 1 (must always be polling) | +| **Max replicas** | 10+ (each replica pulls independently) | +| **Identity** | Managed Identity — `DefaultAzureCredential()` | + +The worker **never receives inbound requests**. It calls `worker.start()` which opens a persistent gRPC stream to DTS, pulling orchestration work items, activity tasks, and entity operations. When there's nothing to do, it simply blocks on the long-poll. + +```mermaid +sequenceDiagram + participant DTS as Azure DTS + participant W as Worker (ACA) + participant AOAI as Azure OpenAI + participant MCP as MCP Server + + loop Pull-based work loop + W->>DTS: gRPC: GetWorkItems() [long-poll] + DTS-->>W: WorkItem (orchestration / activity / entity) + + alt Orchestration step + W->>W: Replay event history → resume generator + W->>DTS: CompleteOrchestration(actions=[]) + else Entity operation (agent call) + W->>MCP: Streamable HTTP (MCP tools) + MCP-->>W: Tool results + W->>AOAI: Chat Completions (with tool results) + AOAI-->>W: Assessment JSON + W->>DTS: CompleteEntity(state=DurableAgentState) + else Activity task + W->>W: Execute activity function + W->>DTS: CompleteActivity(result) + end + end +``` + +> **Key insight:** Because the worker has no ingress, there is **zero attack surface**. The only way to make the worker do something is to schedule an orchestration in DTS — which requires the `Durable Task Data Contributor` RBAC role. + +### 2. MCP Server — Internal-Only + +| Property | Value | +|----------|-------| +| **Ingress** | Internal only (ACA environment VNET) | +| **Consumers** | Worker only | +| **Protocol** | Streamable HTTP (`/mcp`) | +| **Data store** | Cosmos DB (replaces SQLite) | +| **Identity** | Managed Identity to Cosmos DB | + +The MCP server exposes Contoso business tools (customer lookup, transaction history, subscription details). In the workshop it uses SQLite; in production, swap the backend module to Cosmos DB — the MCP tool surface stays identical. + +### 3. Backend — The Only External Surface + +| Property | Value | +|----------|-------| +| **Ingress** | External HTTPS (public or VNET-integrated) | +| **Role** | Backend-for-Frontend (BFF) | +| **APIs** | REST, SSE, WebSocket | +| **DTS interaction** | gRPC client (schedule, query, raise events) | +| **Identity** | Managed Identity to DTS | + +The backend translates browser-friendly protocols into DTS SDK calls: + +```mermaid +flowchart LR + subgraph Browser + A["React UI"] + end + + subgraph Backend["Backend (ACA)"] + B1["POST /api/workflow/start"] --> B2["client.schedule_new_orchestration()"] + B3["POST /api/workflow/decision"] --> B4["client.raise_orchestration_event()"] + B5["GET /api/workflow/status/:id"] --> B6["client.get_instance_metadata()"] + B7["WS /api/workflow/ws/:id"] --> B8["client.wait_for_completion()"] + B9["GET /api/events/stream"] --> B10["SSE: telemetry + alerts"] + end + + subgraph DTS["Azure DTS"] + C["Task Hub"] + end + + A --> B1 + A --> B3 + A --> B5 + A --> B7 + A --> B9 + B2 --> C + B4 --> C + B6 --> C + B8 --> C + + style Browser fill:#e8f4fd,stroke:#4A90D9 + style Backend fill:#d4edda,stroke:#28a745 + style DTS fill:#cce5ff,stroke:#004085 +``` + +### 4. React UI — Static Web App + +The React UI is a static bundle (no server rendering). In production, deploy to **Azure Static Web Apps** with an API proxy rule routing `/api/*` to the Backend ACA. + +--- + +## What Changes From Workshop to Production? + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#4A90D9'}}}%% +flowchart LR + subgraph WORKSHOP["Workshop (localhost)"] + direction TB + W_DTS["Docker DTS Emulator
localhost:8080"] + W_MCP["MCP + SQLite
localhost:8000"] + W_WORKER["Worker process"] + W_BACKEND["Backend process
localhost:8001"] + W_UI["Vite dev server
localhost:3000"] + W_EVENTS["Synthetic event_producer.py"] + end + + subgraph PRODUCTION["Production (Azure)"] + direction TB + P_DTS["Azure DTS
Managed service"] + P_MCP["MCP + Cosmos DB
Internal ACA"] + P_WORKER["Worker ACA
No ingress"] + P_BACKEND["Backend ACA
External HTTPS"] + P_UI["Static Web App"] + P_EVENTS["Event Hubs
Real telemetry"] + end + + W_DTS -.->|"Same SDK, change endpoint"| P_DTS + W_MCP -.->|"Swap SQLite → Cosmos"| P_MCP + W_WORKER -.->|"Same code, add MI"| P_WORKER + W_BACKEND -.->|"Same code, add MI"| P_BACKEND + W_UI -.->|"npm build → deploy"| P_UI + W_EVENTS -.->|"Replace with real data"| P_EVENTS + + style WORKSHOP fill:#fff3cd,stroke:#856404,stroke-width:2px + style PRODUCTION fill:#d4edda,stroke:#28a745,stroke-width:2px +``` + +### Code Changes Required + +| Component | Change | LOC Impact | +|-----------|--------|------------| +| **Worker** | `DTS_ENDPOINT` env var → Azure URL | 0 lines (config only) | +| **Worker** | `DefaultAzureCredential()` already used when not localhost | 0 lines | +| **Backend** | Same as worker | 0 lines | +| **MCP Server** | Swap `_backend_sqlite.py` → `_backend_cosmos.py` | ~1 import line | +| **UI** | `npm run build` → deploy static files | 0 lines | +| **Event Producer** | Replace with Event Hubs consumer | ~50 lines (new module) | + +> **Total:** Near-zero code changes. The architecture was designed for this — `DTS_ENDPOINT` is the only knob. + +--- + +## Security Model + +```mermaid +flowchart TB + subgraph IDENTITY["🔐 Managed Identity"] + MI_WORKER["Worker MI"] + MI_BACKEND["Backend MI"] + MI_MCP["MCP MI"] + end + + MI_WORKER -->|"Durable Task Data Contributor"| DTS["Azure DTS"] + MI_WORKER -->|"Cognitive Services OpenAI User"| AOAI["Azure OpenAI"] + MI_BACKEND -->|"Durable Task Data Contributor"| DTS + MI_MCP -->|"Cosmos DB Data Reader"| COSMOS["Cosmos DB"] + + subgraph NETWORK["🌐 Network"] + EXT["External Traffic"] -->|"HTTPS only"| BE["Backend"] + BE -->|"Internal VNET"| MCP_S["MCP Server"] + WORKER_S["Worker"] -->|"Internal VNET"| MCP_S + WORKER_S -.->|"Outbound gRPC"| DTS + end + + style IDENTITY fill:#e2d5f1,stroke:#6f42c1,stroke-width:2px + style NETWORK fill:#e8f4fd,stroke:#4A90D9,stroke-width:2px +``` + +**Key principles:** +- **No API keys in production** — Managed Identity everywhere +- **No secrets in environment variables** — `DefaultAzureCredential()` auto-discovers MI +- **Worker has zero ingress** — no attack surface, pull-only +- **MCP server is internal-only** — not reachable from internet +- **Backend is the sole entry point** — all external traffic funneled through one surface + +--- + +## Scaling Characteristics + +```mermaid +%%{init: {'theme': 'base'}}%% +xychart-beta + title "Scaling Profile by Component" + x-axis ["Worker", "Backend", "MCP Server", "DTS"] + y-axis "Scaling Dimension" 0 --> 5 + bar [5, 3, 2, 5] +``` + +| Component | Scaling Model | Trigger | Notes | +|-----------|--------------|---------|-------| +| **Worker** | Horizontal (KEDA) | DTS queue depth | Each replica pulls independently. Stateless — all state lives in DTS. | +| **Backend** | Horizontal (HTTP) | Request count / CPU | Standard HTTP scaling. SSE connections are long-lived. | +| **MCP Server** | Horizontal (HTTP) | Request count | Stateless tool calls. Scale with worker count. | +| **DTS** | Managed service | Automatic | Microsoft manages partitioning and throughput. | + +### Why the Worker Scales So Well + +The worker is **completely stateless**. All durable state — orchestration event logs, entity state (`DurableAgentState`), timers, external events — lives in DTS storage. When you add a second worker replica: + +1. It calls `worker.start()` and begins long-polling DTS +2. DTS distributes work items across all connected workers +3. Any replica can pick up any orchestration — replay rebuilds state from the event log +4. If a replica dies, its in-progress work item times out and another replica picks it up + +This is the same model Azure Durable Functions uses at massive scale. + +--- + +## Deployment Checklist + +- [ ] Provision Azure DTS (Consumption SKU) + Task Hub +- [ ] Provision Azure Container Apps Environment with VNET +- [ ] Deploy Worker ACA (no ingress, MI enabled) +- [ ] Deploy MCP Server ACA (internal ingress, MI enabled) +- [ ] Deploy Backend ACA (external ingress, MI enabled) +- [ ] Assign RBAC: Worker MI → `Durable Task Data Contributor` on DTS +- [ ] Assign RBAC: Backend MI → `Durable Task Data Contributor` on DTS +- [ ] Assign RBAC: Worker MI → `Cognitive Services OpenAI User` on Azure OpenAI +- [ ] Assign RBAC: MCP MI → Cosmos DB `Data Reader` role +- [ ] Deploy React UI to Static Web Apps with proxy to Backend +- [ ] Configure Event Hubs for real telemetry ingestion +- [ ] Enable Application Insights for all containers +- [ ] Test: `provision_dts.ps1` covers DTS + RBAC steps + +--- + +## Cost Estimation (Consumption Tier) + +| Resource | SKU | Estimated Monthly Cost | +|----------|-----|----------------------| +| Azure DTS | Consumption | Pay-per-execution (~$0.01/1K orchestrations) | +| ACA Worker | Consumption | Pay-per-vCPU-second (idle = ~$0) | +| ACA Backend | Consumption | Pay-per-request | +| ACA MCP Server | Consumption | Pay-per-request | +| Azure OpenAI | Pay-as-you-go | ~$0.01–0.03 per investigation | +| Cosmos DB | Serverless | Pay-per-RU | +| Static Web Apps | Free tier | $0 | +| Application Insights | Pay-per-GB | First 5GB/month free | + +> For a low-volume fraud detection system (~100 alerts/day), expect **< $50/month** total. + +--- + +*Back to [Workshop README](README.md)* diff --git a/agentic_ai/workflow/fraud_detection_durable/README.md b/agentic_ai/workflow/fraud_detection_durable/README.md index 5aa5c7753..fb19b7f53 100644 --- a/agentic_ai/workflow/fraud_detection_durable/README.md +++ b/agentic_ai/workflow/fraud_detection_durable/README.md @@ -1,360 +1,591 @@ -# Durable Fraud Detection Workflow +# Ambient Fraud Detection — Durable Agentic Workflow + +A production-grade reference architecture for **ambient, long-running, durable, fail-over capable AI agent workflows** with human-in-the-loop (HITL) decision gates. + +This demo implements the complete lifecycle of a background (ambient) agent system: continuous telemetry monitoring → anomaly detection → multi-agent investigation → human approval → action execution — all with crash recovery, persistent state, and real-time UI visualization. + +> 📐 **Taking this to production?** See [PRODUCTION_ARCHITECTURE.md](PRODUCTION_ARCHITECTURE.md) for the Azure Container Apps deployment topology, security model, and scaling characteristics. + +--- + +## 🎯 Workshop Learning Objectives + +After this workshop you will understand how to: + +1. **Design a 3-layer ambient agent architecture** (Detection → Investigation → Decision) +2. **Choose the right durability boundary** — what needs DTS checkpointing vs. what doesn't +3. **Implement human-in-the-loop** with durable external events that survive process restarts +4. **Build stateful feedback loops** — analyst rejects → agent re-investigates with full context +5. **Integrate MCP tools** for real-time data access within agent workflows +6. **Add observability** with OpenTelemetry + Application Insights +7. **Explain _why_ this architecture is truly durable** — not just buzzwords, but the concrete mechanics + +--- + +## 🏗️ Architecture: The 3-Layer Pattern + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#4A90D9', 'primaryTextColor': '#fff', 'lineColor': '#5C6B77' }}}%% +flowchart TB + subgraph LAYER1["🟢 Layer 1 — Detection · Ambient Monitoring"] + direction LR + TELEMETRY["📊 Telemetry
Generator
2–5s interval"] + RULES["⚡ Rule Engine
Python, no LLM
• Multi-country login
• Spending spike > 3×
• Burst API calls
• Repeated auth failures"] + SUBMIT["🚨 Auto-submit
POST /api/workflow/start"] + SSE_OUT["📺 SSE Stream
→ React Live Feed"] + + TELEMETRY --> RULES + RULES -->|"anomaly detected"| SUBMIT + TELEMETRY -->|"all events"| SSE_OUT + RULES -->|"flagged events"| SSE_OUT + end + + subgraph LAYER2["🔵 Layer 2 — Investigation · Durable Agent Orchestration"] + direction TB + ORCH["🔷 DTS Orchestration
checkpointed, crash-recoverable"] + + subgraph ENTITY["dafx-FraudAnalysisAgent Entity"] + direction TB + INNER["Inner Workflow
(fast, async, in-memory)"] + + subgraph FANOUT["Fan-out / Fan-in"] + direction LR + ROUTER["AlertRouter"] --> USAGE["📈 Usage
Analyst"] + ROUTER --> LOCATION["🌍 Location
Analyst"] + ROUTER --> BILLING["💳 Billing
Analyst"] + end + + USAGE -->|"MCP tools"| AGG["🧠 FraudRisk
Aggregator
(LLM)"] + LOCATION -->|"MCP tools"| AGG + BILLING -->|"MCP tools"| AGG + AGG --> ASSESSMENT["📋 FraudRisk
Assessment"] + + INNER --- FANOUT + end + + ORCH -->|"yield fraud_agent.run()"| ENTITY + end + + subgraph LAYER3["🟠 Layer 3 — Decision & Action · HITL + Execution"] + direction TB + RISK_CHECK{"Risk ≥ 0.6?"} + + subgraph HITL_LOOP["HITL Feedback Loop
durable, stateful, crash-safe"] + direction TB + NOTIFY["📧 Notify Analyst
DTS Activity"] + WAIT["⏳ Wait for Decision
when_any(event, 72h timer)
Survives process death"] + DECIDE{"Analyst
Decision?"} + EXECUTE["✅ Execute Action
DTS Activity"] + REINVEST["🔄 Re-investigate
same session = full history"] + TIMEOUT_ACT["⏰ Escalate Timeout
DTS Activity"] + end + + AUTO_CLEAR["🟢 Auto-clear
DTS Activity"] + FINAL["📨 Send Notification
DTS Activity"] + + RISK_CHECK -->|"YES"| NOTIFY + NOTIFY --> WAIT + WAIT --> DECIDE + DECIDE -->|"Approve"| EXECUTE + DECIDE -->|"Reject + feedback"| REINVEST + DECIDE -->|"Timeout"| TIMEOUT_ACT + REINVEST -->|"loop back"| NOTIFY + RISK_CHECK -->|"NO"| AUTO_CLEAR + + EXECUTE --> FINAL + TIMEOUT_ACT --> FINAL + AUTO_CLEAR --> FINAL + end + + SUBMIT -->|"triggers"| ORCH + ENTITY -->|"risk score"| RISK_CHECK + + style LAYER1 fill:#d4edda,stroke:#28a745,stroke-width:2px + style LAYER2 fill:#cce5ff,stroke:#004085,stroke-width:2px + style LAYER3 fill:#fff3cd,stroke:#856404,stroke-width:2px + style ENTITY fill:#e8f4fd,stroke:#4A90D9 + style HITL_LOOP fill:#ffeeba,stroke:#ffc107 + style FANOUT fill:#f0f4f8,stroke:#adb5bd +``` + +### Why This Layering? + +| Layer | Uses LLM? | Durable? | Why? | +|-------|-----------|----------|------| +| **Layer 1** — Detection | ❌ No | ❌ No | Events arrive every 2–5s. An LLM call takes 2–5s and costs money. Simple rules catch 95% of benign events at zero cost. | +| **Layer 2** — Investigation | ✅ Yes | ✅ Yes (entity) | Complex multi-signal reasoning is the LLM's strength. Entity state persists the full conversation for re-investigation. | +| **Layer 3** — Decision | ❌ No | ✅ Yes (orchestration) | Human decisions can take hours/days. DTS timers and external events survive crashes and restarts. | + +--- + +## 🔑 Key Patterns + +### Pattern 1: Durability Boundaries — What Gets Checkpointed? + +Not everything needs to be durable. The key architectural insight is choosing **where** to draw the durability boundary: -A hybrid architecture combining **Workflow** (complex topology) and **Durable Task** (durability, HITL) for enterprise-grade fraud detection. +```mermaid +%%{init: {'theme': 'base'}}%% +flowchart LR + subgraph DURABLE["✅ DTS-Managed · each yield = checkpoint"] + direction TB + D1["yield fraud_agent.run(...)"] + D2["yield wait_for_external_event()"] + D3["yield create_timer(72h)"] + D4["yield call_activity(...)"] + end -## 🏗️ Architecture + subgraph FAST["⚡ Fast Async · retry on failure"] + direction TB + F1["Inner workflow fan-out/fan-in"] + F2["MCP tool calls via HTTP"] + F3["LLM calls to Azure OpenAI"] + end + DURABLE ~~~ FAST + + style DURABLE fill:#d4edda,stroke:#28a745,stroke-width:2px + style FAST fill:#fff3cd,stroke:#856404,stroke-width:2px ``` -┌─────────────────────────────────────────────────────────────────────────┐ -│ DURABLE TASK ORCHESTRATION (Outer Layer) │ -│ Handles: Durability, Long Waits, Crash Recovery │ -│ │ -│ ┌─────────────────────────────────────────────────────────────────┐ │ -│ │ 1. Receive Alert │ │ -│ │ 2. Call "run_fraud_analysis" Activity ──────────────────────┐ │ │ -│ │ │ │ │ -│ │ ┌─────────────────────────────────────────────────────┐ │ │ │ -│ │ │ WORKFLOW (Inner Layer - Activity) │ │ │ │ -│ │ │ Handles: Complex Topology, Fast Execution │ │ │ │ -│ │ │ │ │ │ │ -│ │ │ AlertRouter │ │ │ │ -│ │ │ ↓ │ │ │ │ -│ │ │ ┌───┴───┬───────┐ (fan-out) │ │ │ │ -│ │ │ ↓ ↓ ↓ │ │ │ │ -│ │ │ Usage Location Billing │ │ │ │ -│ │ │ └───────┼───────┘ (fan-in) │ │ │ │ -│ │ │ ↓ │ │ │ │ -│ │ │ Aggregator (LLM) │ │ │ │ -│ │ │ ↓ │ │ │ │ -│ │ │ Returns: FraudRiskAssessment │ │ │ │ -│ │ └─────────────────────────────────────────────────────┘ │ │ │ -│ │ ◄─────┘ │ │ -│ │ 3. Check Risk Score (simple if/else) │ │ -│ │ │ │ -│ │ IF risk >= 0.6: │ │ -│ │ → notify_analyst Activity │ │ -│ │ → wait_for_external_event("AnalystDecision") ⏸️ │ │ -│ │ → execute_fraud_action Activity │ │ -│ │ │ │ -│ │ ELSE: │ │ -│ │ → auto_clear Activity │ │ -│ │ │ │ -│ │ 4. send_notification Activity │ │ -│ └─────────────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────────────┘ + +**Why not checkpoint every LLM call?** Adding DTS checkpoints to each LLM call would add ~200ms overhead per call and massively complicate the agent topology. The inner workflow runs in ~10–20s — fast enough that retry-on-failure is the right strategy. If the worker crashes mid-inner-workflow, the entity call simply retries from the beginning. + +### Pattern 2: Stateful Feedback Loop via Durable Entity + +The `FraudAnalysisAgent` is registered as a DTS entity (`dafx-FraudAnalysisAgent`). The entity persists conversation history via `DurableAgentState`, enabling meaningful re-investigation: + +```mermaid +sequenceDiagram + participant O as DTS Orchestration + participant E as Entity
(dafx-FraudAnalysisAgent) + participant S as DurableAgentState + participant LLM as Azure OpenAI + + Note over O,S: First Investigation + O->>E: yield fraud_agent.run(alert_json, session) + E->>S: Load state (empty) + E->>S: Append user message (alert) + E->>LLM: Chat with full history + LLM-->>E: Risk assessment + E->>S: Append assistant response + E->>S: Persist state ← saved to DTS storage + E-->>O: AgentResponse (risk=0.85) + + Note over O,S: Analyst Rejects — Re-investigation + O->>E: yield fraud_agent.run(feedback, same session!) + E->>S: Load state (has alert + first analysis) + E->>S: Append user message (feedback) + E->>LLM: Chat with FULL history
(alert + analysis + feedback) + LLM-->>E: Deeper assessment + E->>S: Append assistant response + E->>S: Persist state + E-->>O: AgentResponse (risk=0.72) ``` -## 🎯 Why Hybrid Architecture? +The agent doesn't start from scratch — it sees the original alert, its first analysis, AND the analyst's feedback. This is what makes re-investigation meaningful. -| Feature | Workflow Only | Durable Task Only | **Hybrid (This)** | -|---------|--------------|-------------------|-------------------| -| Complex topology (fan-out/fan-in) | ✅ Easy | ❌ Manual | ✅ Easy | -| Crash recovery | ❌ Lost state | ✅ Automatic | ✅ Automatic | -| Human-in-the-loop | ⚠️ Manual checkpoints | ✅ Built-in events | ✅ Built-in events | -| Timeout handling | ❌ Not built-in | ✅ Native timers | ✅ Native timers | -| Long waits (hours/days) | ❌ Memory-bound | ✅ Persistent | ✅ Persistent | -| Visibility/Dashboard | ❌ Custom logging | ✅ DTS Dashboard | ✅ DTS Dashboard | +### Pattern 3: Ambient Detection Without LLM Overhead -## 📁 Project Structure +Layer 1 uses fast Python rule evaluation, not LLM inference: -``` -fraud_detection_durable/ -├── pyproject.toml # Dependencies -├── .env.sample # Environment template -├── fraud_analysis_workflow.py # Inner workflow (fan-out → aggregate) -├── worker.py # DTS Worker with orchestration -├── client.py # CLI client for testing -├── backend.py # FastAPI backend for UI -├── README.md # This file -└── ui/ # React UI - ├── src/ - │ ├── App.jsx # Main app with WebSocket connection - │ └── components/ - │ └── WorkflowVisualizer.jsx # Interactive workflow diagram - └── package.json +```python +# Rule: Multi-country login within 2 hours +if event.type == "login" and event.country != last_login_country: + if time_delta < timedelta(hours=2): + trigger_alert(event) # → Layer 2 DTS orchestration + +# Rule: Spending spike > 3× average +if event.type == "transaction" and event.amount > 3 * customer_average: + trigger_alert(event) ``` -## 🚀 Quick Start +At 1 event every 2–5 seconds, an LLM call (2–5s each) can't keep up. Rules handle the 95% of benign events at zero cost. The LLM's value is in Layer 2, where it reasons about complex multi-signal patterns. -### Prerequisites +### Pattern 4: Backend-For-Frontend (BFF) for Durable Workflows -1. **Docker** - For DTS emulator -2. **Python 3.12+** -3. **Azure OpenAI** - With a deployed model -4. **MCP Server** - Running on port 8000 +The browser cannot call DTS's gRPC SDK directly. The FastAPI backend translates REST/WebSocket into SDK calls: -### Step 1: Start Durable Task Scheduler +```mermaid +flowchart LR + BROWSER["🌐 Browser
(REST / SSE / WS)"] + BACKEND["⚙️ Backend
(FastAPI)"] + DTS["🔷 DTS
(gRPC SDK)"] + WORKER["🔧 Worker"] -```bash -docker run -d --name dts-emulator \ - -p 8080:8080 -p 8082:8082 \ - mcr.microsoft.com/dts/dts-emulator:latest + BROWSER -->|"HTTP"| BACKEND + BACKEND -->|"gRPC"| DTS + DTS -.->|"pull"| WORKER + + style BROWSER fill:#e8f4fd,stroke:#4A90D9 + style BACKEND fill:#d4edda,stroke:#28a745 + style DTS fill:#cce5ff,stroke:#004085 + style WORKER fill:#ffeeba,stroke:#ffc107 ``` -Dashboard: http://localhost:8082 +In production, swap `DTS_ENDPOINT` from `localhost:8080` to your Azure DTS endpoint. **Zero code changes.** See [PRODUCTION_ARCHITECTURE.md](PRODUCTION_ARCHITECTURE.md). -### Step 2: Start MCP Server +--- -```bash -cd mcp -uv run mcp_service.py -``` +## 🛡️ Why Is This Actually Durable? — Deep Dive -### Step 3: Configure Environment +This section explains the **concrete mechanics** that make this architecture truly durable, not just the claim. Understanding these internals is critical for the workshop. -```bash -cd agentic_ai/workflow/fraud_detection_durable -cp .env.sample .env -# Edit .env with your Azure OpenAI credentials -``` +### The Durability Stack -### Step 4: Install Dependencies +```mermaid +%%{init: {'theme': 'base'}}%% +flowchart TB + subgraph YOUR_CODE["Your Code"] + A["worker.py
Orchestration generator + activities"] + end -```bash -uv sync -``` + subgraph AF["agent-framework DTS Layer"] + B["DurableAIAgentWorker
DurableAIAgentOrchestrationContext
DurableAgentState"] + end -### Step 5: Start Worker + subgraph SDK["DTS Python SDK"] + C["DurableTaskSchedulerWorker
gRPC protocol"] + end -```bash -uv run worker.py -``` + subgraph DTS["Azure Durable Task Scheduler"] + D["Event Store · Timer Service
Entity State Store · Task Queue"] + end -### Step 6: Run Tests + YOUR_CODE --> AF + AF --> SDK + SDK --> DTS -**Option A: CLI Client** -```bash -uv run client.py + style YOUR_CODE fill:#d4edda,stroke:#28a745,stroke-width:2px + style AF fill:#e2d5f1,stroke:#6f42c1,stroke-width:2px + style SDK fill:#cce5ff,stroke:#004085,stroke-width:2px + style DTS fill:#fff3cd,stroke:#856404,stroke-width:2px ``` -**Option B: FastAPI Backend + React UI** -```bash -# Terminal 1: Backend -uv run backend.py +### 1. Event Sourcing — Checkpoints via `yield` + +Every `yield` in the orchestration generator is a **checkpoint written to DTS's event store**: -# Terminal 2: React UI -cd ui -npm install -npm run dev +```python +# Each yield writes an event to DTS's append-only log +response = yield fraud_agent.run(messages=alert, session=session) # ← checkpoint +yield context.call_activity("notify_analyst", input=assessment) # ← checkpoint +winner = yield when_any([wait_for_event(...), create_timer(72h)]) # ← checkpoint ``` -Open http://localhost:5173 to view the interactive workflow UI. +These aren't in-memory variables — they're **persisted facts** in DTS storage. If the process crashes after step 2, DTS knows steps 1 and 2 completed because their completion events exist in the log. -## 🧪 Test Scenarios +### 2. Replay, Not Restore — How Crash Recovery Works -### 1. High-Risk Alert with Analyst Approval +When a worker restarts after a crash, it doesn't "load state" — it **replays the orchestration function from the beginning**, but with a twist: -``` -Alert: ALERT-001 (multi_country_login, high severity) - ↓ -Workflow: Fan-out to 3 specialists - ↓ -Risk Score: 0.75 (HIGH RISK) - ↓ -⏸️ Waiting for analyst decision... - ↓ -Analyst: "lock_account" - ↓ -✅ Account locked, notification sent +```mermaid +flowchart TB + subgraph REPLAY["Orchestration Replay After Crash"] + direction TB + Y1["yield fraud_agent.run(...)"] -->|"DTS has completion event
→ returns cached result instantly
(no actual agent call)"| Y2["yield call_activity('notify_analyst')"] + Y2 -->|"DTS has completion event
→ returns cached result instantly"| Y3["yield when_any([event, timer])"] + Y3 -->|"No completion event yet
→ SUSPENDS HERE
waiting for real event"| WAIT["⏳ Waiting..."] + end + + style REPLAY fill:#e8f4fd,stroke:#4A90D9,stroke-width:2px + style WAIT fill:#ffeeba,stroke:#ffc107 ``` -### 2. Low-Risk Alert with Auto-Clear +The generator function re-executes, but each `yield` that already completed **returns its cached result instantly** without re-executing the actual work. The orchestration replays forward until it reaches the first incomplete step, then suspends. This is why your orchestration code must be **deterministic** — it's replayed, not restored. -``` -Alert: ALERT-002 (data_spike, low severity) - ↓ -Workflow: Fan-out to 3 specialists - ↓ -Risk Score: 0.35 (LOW RISK) - ↓ -✅ Auto-cleared, notification sent -``` +### 3. External Events Survive Process Death -### 3. Timeout Escalation +The `wait_for_external_event("AnalystDecision")` call is especially powerful: -``` -Alert: ALERT-003 (unusual_charges, high severity) - ↓ -Workflow: Fan-out to 3 specialists - ↓ -Risk Score: 0.80 (CRITICAL) - ↓ -⏸️ Waiting for analyst decision... - ↓ -⏰ Timeout (72 hours) - ↓ -⚠️ Escalated to manager +```mermaid +sequenceDiagram + participant W1 as Worker (original) + participant DTS as DTS Storage + participant W2 as Worker (restarted) + participant BE as Backend + participant UI as Analyst + + W1->>DTS: yield when_any([event, timer]) + Note over W1: Worker crashes! 💥 + + Note over DTS: Event subscription persists
Timer ticks in DTS storage + + UI->>BE: POST /api/workflow/decision + BE->>DTS: raise_orchestration_event
("AnalystDecision", data) + Note over DTS: Event stored in event log + + W2->>DTS: Start + long-poll for work + DTS->>W2: Orchestration has pending event + W2->>W2: Replay generator → resume at yield + W2->>W2: Process analyst decision ``` -## �️ React UI Features +The event subscription, the timer countdown, and the eventual analyst response — **all live in DTS storage**, not in the worker's memory. The worker is just a stateless compute runtime that pulls work. -The interactive React UI provides real-time visualization of the fraud detection workflow: +### 4. The Worker Is Stateless — All State Lives in DTS -### Interactive Workflow Diagram +This is the fundamental insight. The worker process holds **zero durable state**: -- **Real-time Status Updates**: Nodes change color based on execution state - - Gray: Pending - - Blue: Running (with pulse animation) - - Green: Completed - - Red: Failed +| What | Where It Lives | Survives Crash? | +|------|---------------|-----------------| +| Orchestration progress (which step) | DTS event log | ✅ Yes | +| Agent conversation history | DTS entity state (`DurableAgentState`) | ✅ Yes | +| Pending timers (72h analyst timeout) | DTS timer service | ✅ Yes | +| External event subscriptions | DTS event store | ✅ Yes | +| Activity results | DTS event log | ✅ Yes | +| In-flight LLM call | Worker memory | ❌ No (retried) | +| In-flight MCP tool call | Worker memory | ❌ No (retried) | -- **Clickable Nodes**: Click any workflow step to see detailed execution info: - - **Tool Calls**: Actual MCP tool calls made (e.g., `get_billing_summary`, `get_data_usage`) - - **Arguments**: Parameters passed to each tool - - **Results**: Output returned from each tool call - - **Step Output**: Final output from the agent step +When the worker crashes, the only things lost are in-flight LLM/MCP calls — and those are inside the inner workflow, which retries as a unit when the entity operation is re-dispatched. -### Human-in-the-Loop Panel +### 5. Entity State Persistence (`DurableAgentState`) -When the workflow reaches the Review Gateway (risk ≥ 0.6): -- Shows analyst decision options: `lock_account`, `flag_review`, `dismiss` -- Displays risk assessment details -- Allows analyst to submit decision via UI +The `agent-framework` library stores the full agent conversation in a structured schema: -### Example: Viewing Step Details +```mermaid +%%{init: {'theme': 'base'}}%% +erDiagram + ENTITY["dafx-FraudAnalysisAgent"] { + string instance_key "session_id" + } + STATE["DurableAgentState"] { + string schema_version "v1.1.0" + } + + ENTRY["ConversationEntry"] { + string type "request or response" + json messages "Message array" + json tool_calls "ToolCall array" + json errors "Error array" + json token_usage "TokenUsage" + } + + ENTITY ||--|| STATE : "persists" + STATE ||--o{ ENTRY : "conversationHistory" ``` -1. Click on "Usage Analyst" node -2. Popover shows: - - Tool Calls (Real): - • get_data_usage(subscription_id=5, start_date="2025-12-01", ...) - → Result: {"total_gb": 45.2, "daily_avg": 1.5, ...} - • get_billing_summary(customer_id=3) - → Result: {"current_balance": 150.00, ...} - - Output: "Usage analysis indicates 300% spike in data..." + +Every time the entity processes a request, the cycle is: +1. **Load** state from DTS → `DurableAgentState` +2. **Append** user message (alert or feedback) +3. **Rebuild** full chat history from all entries (filtering out errors) +4. **Call** LLM with complete conversation +5. **Append** assistant response +6. **Persist** state back to DTS via `self.persist_state()` + +This is why the agent can re-investigate with full context — the conversation history is a **durable, append-only log** stored in DTS, not in worker memory. + +### 6. What DTS Provides vs. What It Doesn't + +Understanding the **scope boundary** is critical: + +```mermaid +%%{init: {'theme': 'base'}}%% +flowchart LR + subgraph DTS_SCOPE["🔷 DTS Provides"] + direction TB + DS1["Persistent task queue"] + DS2["Event store · append-only log"] + DS3["Timer service"] + DS4["Entity state storage"] + DS5["Work item distribution"] + DS6["At-least-once delivery"] + end + + subgraph WORKER_SCOPE["🔧 Worker Provides"] + direction TB + WS1["Compute runtime · Python"] + WS2["LLM calls · Azure OpenAI"] + WS3["MCP tool calls · HTTP"] + WS4["Business logic · orchestration"] + WS5["Agent framework integration"] + end + + DTS_SCOPE ~~~ WORKER_SCOPE + + style DTS_SCOPE fill:#cce5ff,stroke:#004085,stroke-width:2px + style WORKER_SCOPE fill:#ffeeba,stroke:#ffc107,stroke-width:2px ``` -## �📊 DTS Dashboard +DTS is a **persistent task queue + event store + timer service**. It doesn't run your code — it stores the _record of what happened_ and _dispatches work items_ to workers. The worker is a **stateless compute runtime** that pulls tasks, executes your Python/LLM/MCP logic, and reports results back. If the worker dies, DTS still has the full event log; a new worker replays it and picks up where things left off. -Open http://localhost:8082 to see: +--- -- All orchestration instances -- Pending external events (analyst decisions) -- Activity execution logs -- Orchestration timeline and status +## 📁 Project Structure -![DTS Dashboard](../docs/media/dts-dashboard.png) +``` +fraud_detection_durable/ +├── worker.py # DTS Worker: orchestration + agent entity + activities +├── backend.py # FastAPI BFF: REST API, WebSocket, SSE, event producer +├── event_producer.py # Layer 1: telemetry generation + anomaly detection +├── fraud_analysis_workflow.py # Inner workflow: fan-out → aggregate (Layer 2) +├── provision_dts.ps1 # Azure DTS provisioning script +├── .env # Configuration (Azure OpenAI, DTS, App Insights) +├── pyproject.toml # Dependencies +├── README.md # This file +├── PRODUCTION_ARCHITECTURE.md # Production deployment on Azure Container Apps +└── ui/ # React/Vite UI + ├── src/ + │ ├── App.jsx # Main app: WebSocket + SSE connections + │ └── components/ + │ ├── ControlPanel.jsx # Alert selector + start button + │ ├── WorkflowVisualizer.jsx # React Flow DAG visualization + │ ├── AnalystDecisionPanel.jsx # HITL approve/reject/feedback + │ └── EventFeed.jsx # Live telemetry feed (Layer 1) + └── package.json +``` -## 🔧 Configuration +--- -### Environment Variables +## 🚀 Quick Start -| Variable | Description | Default | -|----------|-------------|---------| -| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint | Required | -| `AZURE_OPENAI_CHAT_DEPLOYMENT` | Deployment name | `gpt-4o` | -| `MCP_SERVER_URI` | MCP server URL | `http://localhost:8000/mcp` | -| `DTS_ENDPOINT` | DTS scheduler URL | `http://localhost:8080` | -| `DTS_TASKHUB` | DTS task hub name | `fraud-detection` | -| `ANALYST_APPROVAL_TIMEOUT_HOURS` | Timeout for analyst review | `72` | +### Prerequisites -### Risk Threshold +- **Docker** — for DTS emulator +- **Python 3.12+** with **uv** +- **Node.js 18+** — for React UI +- **Azure OpenAI** — with a deployed chat model +- **MCP Server** — Contoso tools on port 8000 + +### Service Startup + +```mermaid +%%{init: {'theme': 'base'}}%% +flowchart LR + S1["1️⃣ DTS Emulator
Port 8080"] + S2["2️⃣ MCP Server
Port 8000"] + S3["3️⃣ Worker
Pulls from DTS"] + S4["4️⃣ Backend
Port 8001"] + S5["5️⃣ React UI
Port 3000"] + + S1 --> S2 --> S3 --> S4 --> S5 + + style S1 fill:#cce5ff,stroke:#004085 + style S2 fill:#d6d8db,stroke:#6c757d + style S3 fill:#ffeeba,stroke:#ffc107 + style S4 fill:#d4edda,stroke:#28a745 + style S5 fill:#e8f4fd,stroke:#4A90D9 +``` -Edit `worker.py` to change the risk threshold: +#### 1. Start DTS Emulator -```python -# Current: 0.6 (60%) -if risk_score >= 0.6: - # High risk path -else: - # Low risk path +```bash +docker run -d --name dts-emulator \ + -p 8080:8080 -p 8082:8082 \ + mcr.microsoft.com/dts/dts-emulator:latest ``` -## 🏛️ Key Components +Dashboard: http://localhost:8082 -### 1. Inner Workflow (`fraud_analysis_workflow.py`) +> **Production:** Replace with Azure DTS — same SDK, just change `DTS_ENDPOINT`. See [provision_dts.ps1](provision_dts.ps1). -The workflow handles complex multi-agent topology: +#### 2. Start MCP Server -```python -# Fan-out: Alert → 3 Specialists -builder.add_edge(alert_router, usage_executor) -builder.add_edge(alert_router, location_executor) -builder.add_edge(alert_router, billing_executor) - -# Fan-in: 3 Specialists → Aggregator -builder.add_fan_in_edge( - [usage_executor, location_executor, billing_executor], - aggregator -) +```bash +cd mcp && uv run python mcp_service.py ``` -### 2. DTS Orchestration (`worker.py`) +#### 3. Start Worker -The orchestration handles durability and HITL: +```bash +cd agentic_ai/workflow/fraud_detection_durable +uv sync && uv run python worker.py +``` -```python -def fraud_detection_orchestration(context, payload): - # Run inner workflow as activity - assessment = yield context.call_activity("run_fraud_analysis", alert) - - if assessment["risk_score"] >= 0.6: - # Wait for analyst with timeout - approval_task = context.wait_for_external_event("AnalystDecision") - timeout_task = context.create_timer(timedelta(hours=72)) - - winner = yield when_any([approval_task, timeout_task]) - - if winner == approval_task: - yield context.call_activity("execute_fraud_action", decision) - else: - yield context.call_activity("escalate_timeout", assessment) - else: - yield context.call_activity("auto_clear_alert", assessment) +#### 4. Start Backend + +```bash +uv run python backend.py ``` -### 3. Activities +#### 5. Start React UI -| Activity | Purpose | -|----------|---------| -| `run_fraud_analysis` | Runs inner workflow, returns assessment | -| `notify_analyst` | Sends notification for review | -| `execute_fraud_action` | Executes approved action | -| `auto_clear_alert` | Auto-clears low-risk alerts | -| `escalate_timeout` | Escalates on timeout | -| `send_notification` | Sends final notification | +```bash +cd ui && npm install && npm run dev +# Open http://localhost:3000 +``` -## 🔄 Comparison with Original Implementation +--- -| Aspect | Original (`fraud_detection/`) | Durable (`fraud_detection_durable/`) | -|--------|-------------------------------|-------------------------------------| -| HITL Pattern | `ctx.request_info()` + `@response_handler` | `wait_for_external_event()` | -| Checkpointing | `FileCheckpointStorage` (manual) | DTS (automatic) | -| Timeout | Not built-in | Native `create_timer()` | -| Recovery | Load checkpoint manually | Automatic replay | -| Dashboard | Custom logging | DTS Dashboard | -| Topology | Full workflow | Workflow as activity | +## 🧪 Demo Scenarios -## 🐛 Troubleshooting +### Scenario 1: Ambient Detection → Auto-Clear -### "Cannot connect to DTS" +Watch the event feed — a multi-country login anomaly triggers automatic investigation. Low risk → auto-cleared. -```bash -# Check if DTS is running -docker ps | grep dts +### Scenario 2: Ambient Detection → HITL Approval + +A spending spike triggers investigation. High risk → analyst reviews → approves "lock account". + +### Scenario 3: Reject → Stateful Re-investigation + +Analyst rejects with feedback "check if VPN usage". Agent re-investigates with **full conversation history** (original alert + first analysis + analyst feedback). + +### Scenario 4: Kill & Recover (Durability Proof) 💥 -# Restart if needed -docker restart dts-emulator +This is the most important demo — it proves the architecture isn't just theoretical: + +```mermaid +sequenceDiagram + participant UI as Analyst + participant BE as Backend + participant DTS as DTS + participant W1 as Worker v1 + + UI->>BE: Start high-risk workflow + BE->>DTS: schedule_new_orchestration() + DTS->>W1: Work item: run orchestration + W1->>W1: yield fraud_agent.run() ✅ + W1->>W1: yield notify_analyst() ✅ + W1->>DTS: yield wait_for_external_event() + Note over W1: Status: Awaiting analyst review + + Note over W1: 💥 taskkill /F /IM python.exe + + participant W2 as Worker v2 + Note over W2: uv run python worker.py + + UI->>BE: POST /api/workflow/decision (approve) + BE->>DTS: raise_orchestration_event() + DTS->>W2: Orchestration has pending event + W2->>W2: Replay: yield agent.run() → cached ✅ + W2->>W2: Replay: yield notify() → cached ✅ + W2->>W2: Resume: when_any() → event arrived! + W2->>W2: yield execute_fraud_action() ✅ + Note over W2: Workflow completes normally! 🎉 ``` -### "Worker not processing" +1. Start a high-risk workflow → reaches "Awaiting analyst review" +2. `taskkill /F /IM python.exe` — kill all Python processes +3. `uv run python worker.py` — restart the worker +4. Submit analyst decision via UI +5. **Workflow completes normally** — DTS replayed the orchestration from its event log ✅ + +--- -1. Check worker is running: `uv run worker.py` -2. Check logs for errors -3. Verify DTS endpoint in `.env` +## 🔧 Configuration + +| Variable | Description | Default | +|----------|-------------|---------| +| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint | Required | +| `AZURE_OPENAI_CHAT_DEPLOYMENT` | Model deployment name | `gpt-4o` | +| `MCP_SERVER_URI` | MCP server URL | `http://localhost:8000/mcp` | +| `DTS_ENDPOINT` | DTS endpoint (local or Azure) | `http://localhost:8080` | +| `DTS_TASKHUB` | DTS task hub name | `default` | +| `ANALYST_APPROVAL_TIMEOUT_HOURS` | HITL timeout | `72` | +| `MAX_REVIEW_ATTEMPTS` | Max reject → re-investigate cycles | `3` | +| `EVENT_PRODUCER_ENABLED` | Enable Layer 1 event producer | `true` | +| `EVENT_INTERVAL_SECONDS` | Seconds between telemetry events | `3` | +| `BACKEND_OBSERVABILITY` | Enable Application Insights | `false` | -### "Analyst decision not received" +--- -1. Check instance ID matches -2. Verify event name is `AnalystDecision` -3. Check DTS dashboard for pending events +## 📐 Production Deployment -## 📚 Related Documentation +For Azure Container Apps deployment topology, security with Managed Identity, KEDA scaling, and cost estimation, see: -- [Agent Framework Workflow](../human-in-the-loop.md) -- [Durable Task Samples](https://github.com/microsoft/agent-framework/tree/main/python/samples/getting_started/durabletask) -- [Original Fraud Detection](../fraud_detection/README.md) +👉 **[PRODUCTION_ARCHITECTURE.md](PRODUCTION_ARCHITECTURE.md)** -## 📜 License +--- -Copyright (c) Microsoft. All rights reserved. +*Copyright (c) Microsoft. All rights reserved.* diff --git a/agentic_ai/workflow/fraud_detection_durable/backend.py b/agentic_ai/workflow/fraud_detection_durable/backend.py index df653c200..729d77cd6 100644 --- a/agentic_ai/workflow/fraud_detection_durable/backend.py +++ b/agentic_ai/workflow/fraud_detection_durable/backend.py @@ -31,15 +31,17 @@ # Add parent directories to path for the shared observability module sys.path.insert(0, str(Path(__file__).parent.parent.parent)) # agentic_ai/ -try: - from observability import setup_observability - _observability_enabled = setup_observability( - service_name="contoso-fraud-workflow", - enable_live_metrics=True, - enable_sensitive_data=os.getenv("ENABLE_SENSITIVE_DATA", "false").lower() in ("1", "true", "yes"), - ) -except ImportError: - _observability_enabled = False +_observability_enabled = False +if os.getenv("BACKEND_OBSERVABILITY", "false").lower() in ("1", "true", "yes"): + try: + from observability import setup_observability + _observability_enabled = setup_observability( + service_name="contoso-fraud-workflow", + enable_live_metrics=True, + enable_sensitive_data=os.getenv("ENABLE_SENSITIVE_DATA", "false").lower() in ("1", "true", "yes"), + ) + except (ImportError, Exception): + _observability_enabled = False # ------------------------------------------------------------------ from azure.identity import DefaultAzureCredential @@ -48,9 +50,11 @@ from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException from fastapi.middleware.cors import CORSMiddleware from fastapi.staticfiles import StaticFiles -from fastapi.responses import FileResponse +from fastapi.responses import FileResponse, StreamingResponse from pydantic import BaseModel +from event_producer import EventProducer, CUSTOMER_PROFILES, TelemetryEvent + # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) @@ -68,6 +72,7 @@ CORS_ORIGINS = [ "http://localhost:3000", "http://localhost:5173", + "http://localhost:8001", "http://localhost:8002", ] # Add Azure Container Apps URL if set @@ -129,7 +134,9 @@ class AnalystDecisionRequest(BaseModel): """Analyst decision submitted from UI.""" instance_id: str alert_id: str - approved_action: str + approved: bool = True # True = approve action, False = reject with feedback + approved_action: str = "clear" + feedback: str = "" # Feedback for re-investigation (when approved=False) analyst_notes: str = "" analyst_id: str = "analyst_ui" @@ -249,6 +256,16 @@ async def broadcast(self, instance_id: str, message: dict): manager = ConnectionManager() +# ============================================================================ +# Event Producer (Layer 1 — Ambient Detection) +# ============================================================================ + +event_producer = EventProducer( + interval_seconds=float(os.getenv("EVENT_INTERVAL_SECONDS", "3.0")), + anomaly_probability=float(os.getenv("ANOMALY_PROBABILITY", "0.08")), +) +_event_producer_task: asyncio.Task | None = None + # ============================================================================ # Background Task: Poll DTS for Status Updates @@ -438,7 +455,9 @@ async def submit_decision(request: AnalystDecisionRequest): decision = { "alert_id": request.alert_id, + "approved": request.approved, "approved_action": request.approved_action, + "feedback": request.feedback, "analyst_notes": request.analyst_notes, "analyst_id": request.analyst_id, } @@ -462,6 +481,38 @@ async def submit_decision(request: AnalystDecisionRequest): return {"status": "submitted", "instance_id": request.instance_id} +# ============================================================================ +# SSE Endpoint — Live Event Feed (Layer 1 → UI) +# ============================================================================ + + +@app.get("/api/events/stream") +async def event_stream(): + """Server-Sent Events stream of live telemetry + anomaly detections. + + The React UI connects here with EventSource to display the Live Feed panel. + Events are JSON objects with event_type, customer_name, is_anomaly, etc. + """ + queue = event_producer.subscribe() + + async def generate(): + try: + while True: + event = await queue.get() + data = json.dumps(event.to_dict()) + yield f"data: {data}\n\n" + except asyncio.CancelledError: + pass + finally: + event_producer.unsubscribe(queue) + + return StreamingResponse(generate(), media_type="text/event-stream", headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", # Disable nginx buffering + }) + + # ============================================================================ # WebSocket Endpoint # ============================================================================ @@ -529,17 +580,68 @@ async def websocket_endpoint(websocket: WebSocket, instance_id: str): @app.on_event("startup") async def startup(): """Initialize on startup.""" + global _event_producer_task + logger.info("="*60) logger.info("Starting Durable Fraud Detection Backend") logger.info("="*60) - # Pre-initialize DTS client - try: - get_dts_client() - logger.info("✓ DTS client initialized") - except Exception as e: - logger.error(f"Failed to initialize DTS client: {e}") - logger.error("Make sure DTS emulator is running") + # DTS client is lazy-initialized on first API call (get_dts_client()). + # Pre-init inside uvicorn's startup causes a CancelledError on Windows + # because gRPC's C extension installs a SIGINT handler that conflicts + # with asyncio's ProactorEventLoop signal handling. + logger.info("✓ DTS client will initialize on first request (lazy)") + + # Start Layer 1 event producer (ambient detection) + if os.getenv("EVENT_PRODUCER_ENABLED", "true").lower() in ("1", "true", "yes"): + async def alert_callback(alert_id, customer_id, alert_type, description, severity): + """Auto-submit detected anomaly to the durable workflow.""" + try: + client = get_dts_client() + alert = { + "alert_id": alert_id, + "customer_id": customer_id, + "alert_type": alert_type, + "description": description, + "timestamp": datetime.now().isoformat(), + "severity": severity, + "approval_timeout_hours": 0.05, + "auto_detected": True, + } + instance_id = client.schedule_new_orchestration( + ORCHESTRATION_NAME, + input=alert, + instance_id=f"fraud-{alert_id}-{int(time.time())}", + ) + logger.info(f"🤖 Auto-submitted alert {alert_id} → orchestration {instance_id}") + + # Broadcast workflow_auto_started to SSE so the UI can connect + started_event = TelemetryEvent( + id=f"WF-{alert_id}", + timestamp=alert["timestamp"], + customer_id=customer_id, + customer_name=CUSTOMER_PROFILES.get(customer_id, {}).get("name", f"Customer {customer_id}"), + event_type="workflow_auto_started", + details={ + "instance_id": instance_id, + "alert_id": alert_id, + "alert_type": alert_type, + "description": description, + "severity": severity, + }, + is_anomaly=True, + anomaly_rule=alert_type, + alert_triggered=True, + ) + await event_producer._broadcast(started_event) + except Exception as e: + logger.error(f"Failed to auto-submit alert: {e}") + + event_producer.set_alert_callback(alert_callback) + _event_producer_task = asyncio.create_task(event_producer.run()) + logger.info("✓ Event producer started (Layer 1 ambient detection)") + else: + logger.info("⏸ Event producer disabled (set EVENT_PRODUCER_ENABLED=true to enable)") logger.info("Backend ready! 🚀") @@ -549,6 +651,11 @@ async def shutdown(): """Cleanup on shutdown.""" logger.info("Shutting down backend...") + # Stop event producer + event_producer.stop() + if _event_producer_task: + _event_producer_task.cancel() + # Cancel polling tasks for task in _polling_tasks.values(): task.cancel() @@ -562,7 +669,7 @@ async def shutdown(): if __name__ == "__main__": import uvicorn - port = int(os.environ.get("BACKEND_PORT", "8002")) + port = int(os.environ.get("BACKEND_PORT", "8001")) uvicorn.run( app, host="0.0.0.0", diff --git a/agentic_ai/workflow/fraud_detection_durable/event_producer.py b/agentic_ai/workflow/fraud_detection_durable/event_producer.py new file mode 100644 index 000000000..3c73c9be2 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/event_producer.py @@ -0,0 +1,395 @@ +""" +Layer 1: Ambient Event Producer & Anomaly Detection. + +Generates simulated customer telemetry events and applies fast rule-based +anomaly detection. When an anomaly is detected, it auto-submits an alert +to the backend's /api/workflow/start endpoint, triggering the Layer 2 +durable investigation workflow. + +This module runs as an asyncio background task inside the FastAPI backend. +It does NOT use LLMs — rules are pure Python for speed and cost efficiency. + +Events are broadcast via SSE (Server-Sent Events) to the React UI's +Live Feed panel. +""" + +import asyncio +import logging +import random +import time +from collections import deque +from dataclasses import dataclass, field +from datetime import datetime, timedelta +from typing import Any + +logger = logging.getLogger(__name__) + + +# ============================================================================ +# Customer Profiles (baseline behavior for anomaly detection) +# ============================================================================ + +COUNTRIES = ["USA", "Canada", "UK", "Germany", "France", "Japan", "Australia", "Brazil", "India", "Russia"] +NORMAL_COUNTRIES = { + 1: ["USA", "Canada"], + 2: ["USA", "UK"], + 3: ["Germany", "France"], + 4: ["Japan", "Australia"], + 5: ["USA", "India"], +} + +CUSTOMER_PROFILES = { + 1: {"name": "Alice Johnson", "avg_transaction": 85.0, "avg_daily_data_gb": 1.2, "subscription_id": 5}, + 2: {"name": "Bob Smith", "avg_transaction": 120.0, "avg_daily_data_gb": 2.5, "subscription_id": 8}, + 3: {"name": "Carlos Rivera", "avg_transaction": 200.0, "avg_daily_data_gb": 0.8, "subscription_id": 12}, + 4: {"name": "Diana Chen", "avg_transaction": 55.0, "avg_daily_data_gb": 3.0, "subscription_id": 3}, + 5: {"name": "Ethan Patel", "avg_transaction": 150.0, "avg_daily_data_gb": 1.8, "subscription_id": 7}, +} + +EVENT_TYPES = ["login", "transaction", "data_usage", "api_call", "auth_failure"] + + +# ============================================================================ +# Data structures +# ============================================================================ + +@dataclass +class TelemetryEvent: + """A single telemetry event from the simulated environment.""" + id: str + timestamp: str + customer_id: int + customer_name: str + event_type: str + details: dict + is_anomaly: bool = False + anomaly_rule: str = "" + alert_triggered: bool = False + + def to_dict(self) -> dict: + return { + "id": self.id, + "timestamp": self.timestamp, + "customer_id": self.customer_id, + "customer_name": self.customer_name, + "event_type": self.event_type, + "details": self.details, + "is_anomaly": self.is_anomaly, + "anomaly_rule": self.anomaly_rule, + "alert_triggered": self.alert_triggered, + } + + +# ============================================================================ +# Event Producer +# ============================================================================ + +class EventProducer: + """Generates telemetry events and detects anomalies using fast rules. + + Maintains per-customer sliding windows for rule evaluation. + Broadcasts events to registered SSE subscribers. + """ + + def __init__( + self, + interval_seconds: float = 3.0, + anomaly_probability: float = 0.08, + ): + self.interval = interval_seconds + self.anomaly_probability = anomaly_probability + self._running = False + self._event_counter = 0 + self._subscribers: list[asyncio.Queue] = [] + + # Per-customer state for rule evaluation + self._recent_logins: dict[int, deque] = { + cid: deque(maxlen=10) for cid in CUSTOMER_PROFILES + } + self._recent_transactions: dict[int, deque] = { + cid: deque(maxlen=20) for cid in CUSTOMER_PROFILES + } + self._recent_auth_failures: dict[int, deque] = { + cid: deque(maxlen=10) for cid in CUSTOMER_PROFILES + } + + # Track which alerts we've fired to avoid duplicates + self._active_alerts: set[str] = set() + + # Callback for auto-submitting alerts + self._alert_callback: Any = None + + def set_alert_callback(self, callback): + """Set the async callback for when an anomaly triggers an alert.""" + self._alert_callback = callback + + def subscribe(self) -> asyncio.Queue: + """Subscribe to the event stream. Returns a queue to read events from.""" + queue: asyncio.Queue = asyncio.Queue(maxsize=100) + self._subscribers.append(queue) + return queue + + def unsubscribe(self, queue: asyncio.Queue): + """Unsubscribe from the event stream.""" + if queue in self._subscribers: + self._subscribers.remove(queue) + + async def _broadcast(self, event: TelemetryEvent): + """Send event to all SSE subscribers.""" + dead_queues = [] + for queue in self._subscribers: + try: + queue.put_nowait(event) + except asyncio.QueueFull: + dead_queues.append(queue) + for q in dead_queues: + self._subscribers.remove(q) + + # ======================================================================== + # Event Generation + # ======================================================================== + + def _generate_normal_event(self, customer_id: int) -> TelemetryEvent: + """Generate a normal (non-anomalous) telemetry event.""" + profile = CUSTOMER_PROFILES[customer_id] + event_type = random.choice(EVENT_TYPES) + self._event_counter += 1 + now = datetime.now() + + if event_type == "login": + country = random.choice(NORMAL_COUNTRIES[customer_id]) + details = {"country": country, "ip": f"192.168.{random.randint(1,255)}.{random.randint(1,255)}", "success": True} + elif event_type == "transaction": + amount = round(random.gauss(profile["avg_transaction"], profile["avg_transaction"] * 0.3), 2) + amount = max(5.0, amount) # No negative/tiny amounts + details = {"amount": amount, "currency": "USD", "merchant": random.choice(["Amazon", "Walmart", "Target", "BestBuy", "Costco"])} + elif event_type == "data_usage": + gb = round(random.gauss(profile["avg_daily_data_gb"], profile["avg_daily_data_gb"] * 0.2), 2) + gb = max(0.1, gb) + details = {"gb_used": gb, "subscription_id": profile["subscription_id"]} + elif event_type == "api_call": + details = {"endpoint": random.choice(["/api/account", "/api/billing", "/api/usage", "/api/profile"]), "status_code": 200, "latency_ms": random.randint(50, 300)} + else: # auth_failure + details = {"method": "password", "reason": "typo", "ip": f"10.0.{random.randint(1,255)}.{random.randint(1,255)}"} + + return TelemetryEvent( + id=f"EVT-{self._event_counter:06d}", + timestamp=now.isoformat(), + customer_id=customer_id, + customer_name=profile["name"], + event_type=event_type, + details=details, + ) + + def _generate_anomalous_event(self, customer_id: int) -> TelemetryEvent: + """Generate an event that should trigger an anomaly rule.""" + profile = CUSTOMER_PROFILES[customer_id] + self._event_counter += 1 + now = datetime.now() + + anomaly_type = random.choice(["multi_country_login", "spending_spike", "data_spike", "rapid_auth_failures"]) + + if anomaly_type == "multi_country_login": + # Login from unusual country + unusual = [c for c in COUNTRIES if c not in NORMAL_COUNTRIES[customer_id]] + country = random.choice(unusual) + details = {"country": country, "ip": f"203.{random.randint(1,255)}.{random.randint(1,255)}.{random.randint(1,255)}", "success": True} + return TelemetryEvent( + id=f"EVT-{self._event_counter:06d}", + timestamp=now.isoformat(), + customer_id=customer_id, + customer_name=profile["name"], + event_type="login", + details=details, + ) + + elif anomaly_type == "spending_spike": + # Transaction 4-8× the average + multiplier = random.uniform(4.0, 8.0) + amount = round(profile["avg_transaction"] * multiplier, 2) + details = {"amount": amount, "currency": "USD", "merchant": random.choice(["LuxuryGoods.com", "HighEnd Electronics", "Crypto Exchange"])} + return TelemetryEvent( + id=f"EVT-{self._event_counter:06d}", + timestamp=now.isoformat(), + customer_id=customer_id, + customer_name=profile["name"], + event_type="transaction", + details=details, + ) + + elif anomaly_type == "data_spike": + # Data usage 5-10× the average + multiplier = random.uniform(5.0, 10.0) + gb = round(profile["avg_daily_data_gb"] * multiplier, 2) + details = {"gb_used": gb, "subscription_id": profile["subscription_id"]} + return TelemetryEvent( + id=f"EVT-{self._event_counter:06d}", + timestamp=now.isoformat(), + customer_id=customer_id, + customer_name=profile["name"], + event_type="data_usage", + details=details, + ) + + else: # rapid_auth_failures + details = {"method": "password", "reason": "invalid_credentials", "ip": f"185.{random.randint(1,255)}.{random.randint(1,255)}.{random.randint(1,255)}"} + return TelemetryEvent( + id=f"EVT-{self._event_counter:06d}", + timestamp=now.isoformat(), + customer_id=customer_id, + customer_name=profile["name"], + event_type="auth_failure", + details=details, + ) + + # ======================================================================== + # Anomaly Detection Rules (fast Python, no LLM) + # ======================================================================== + + def _evaluate_rules(self, event: TelemetryEvent) -> tuple[bool, str]: + """Evaluate anomaly detection rules against an event. + + Returns (is_anomaly, rule_name). + """ + cid = event.customer_id + profile = CUSTOMER_PROFILES[cid] + + # Rule 1: Multi-country login within 2 hours + if event.event_type == "login" and event.details.get("success"): + country = event.details.get("country", "") + recent = self._recent_logins[cid] + for prev in recent: + prev_country = prev.get("country", "") + prev_time = prev.get("time", 0) + if prev_country and prev_country != country: + if time.time() - prev_time < 7200: # 2 hours + return True, "multi_country_login" + # Record this login + recent.append({"country": country, "time": time.time()}) + + # Rule 2: Transaction amount > 3× customer average + if event.event_type == "transaction": + amount = event.details.get("amount", 0) + if amount > profile["avg_transaction"] * 3: + return True, "spending_spike" + self._recent_transactions[cid].append({"amount": amount, "time": time.time()}) + + # Rule 3: Data usage > 4× daily average + if event.event_type == "data_usage": + gb = event.details.get("gb_used", 0) + if gb > profile["avg_daily_data_gb"] * 4: + return True, "data_usage_spike" + + # Rule 4: 3+ auth failures in 5 minutes + if event.event_type == "auth_failure": + recent_failures = self._recent_auth_failures[cid] + recent_failures.append({"time": time.time()}) + # Count failures in last 5 minutes + cutoff = time.time() - 300 + recent_count = sum(1 for f in recent_failures if f["time"] > cutoff) + if recent_count >= 3: + return True, "rapid_auth_failures" + + return False, "" + + def _make_alert_description(self, event: TelemetryEvent) -> str: + """Build a human-readable alert description from an anomalous event.""" + if event.anomaly_rule == "multi_country_login": + country = event.details.get("country", "unknown") + recent = self._recent_logins[event.customer_id] + prev_countries = [r["country"] for r in recent if r["country"] != country] + prev = prev_countries[-1] if prev_countries else "unknown" + return f"Login from {country} detected for customer {event.customer_id} ({event.customer_name}). Previous login was from {prev} within 2 hours. Possible credential compromise or account sharing." + elif event.anomaly_rule == "spending_spike": + amount = event.details.get("amount", 0) + avg = CUSTOMER_PROFILES[event.customer_id]["avg_transaction"] + return f"Transaction of ${amount:.2f} detected for customer {event.customer_id} ({event.customer_name}). This is {amount/avg:.1f}× their average of ${avg:.2f}. Possible unauthorized purchase." + elif event.anomaly_rule == "data_usage_spike": + gb = event.details.get("gb_used", 0) + avg = CUSTOMER_PROFILES[event.customer_id]["avg_daily_data_gb"] + return f"Data usage of {gb:.1f} GB detected for customer {event.customer_id} ({event.customer_name}). This is {gb/avg:.1f}× their daily average of {avg:.1f} GB. Possible data exfiltration or compromised device." + elif event.anomaly_rule == "rapid_auth_failures": + return f"Multiple authentication failures detected for customer {event.customer_id} ({event.customer_name}). 3+ failed attempts in 5 minutes from suspicious IP. Possible brute-force attack." + return f"Anomaly detected for customer {event.customer_id}: {event.anomaly_rule}" + + # ======================================================================== + # Main Loop + # ======================================================================== + + async def run(self): + """Main event production loop. Runs until stopped.""" + self._running = True + logger.info(f"🔄 Event producer started (interval={self.interval}s, anomaly_prob={self.anomaly_probability})") + + # Generate a few normal events first before any anomalies + warmup_count = 5 + event_count = 0 + + while self._running: + try: + customer_id = random.choice(list(CUSTOMER_PROFILES.keys())) + event_count += 1 + + # After warmup, sometimes generate anomalous events + if event_count > warmup_count and random.random() < self.anomaly_probability: + event = self._generate_anomalous_event(customer_id) + else: + event = self._generate_normal_event(customer_id) + + # Run anomaly rules + is_anomaly, rule = self._evaluate_rules(event) + if is_anomaly: + event.is_anomaly = True + event.anomaly_rule = rule + + # De-duplicate: don't fire the same rule for the same customer within 60s + dedup_key = f"{customer_id}-{rule}" + if dedup_key not in self._active_alerts: + self._active_alerts.add(dedup_key) + event.alert_triggered = True + + logger.warning( + f"⚠️ ANOMALY DETECTED: {rule} for customer {customer_id} " + f"({CUSTOMER_PROFILES[customer_id]['name']}) — triggering investigation" + ) + + # Auto-submit alert to Layer 2 + if self._alert_callback: + alert_type = rule + description = self._make_alert_description(event) + severity = "high" if rule in ("multi_country_login", "rapid_auth_failures") else "medium" + + try: + await self._alert_callback( + alert_id=f"AUTO-{event.id}", + customer_id=customer_id, + alert_type=alert_type, + description=description, + severity=severity, + ) + except Exception as e: + logger.error(f"Failed to submit alert: {e}") + + # Clear dedup after 60 seconds + asyncio.get_event_loop().call_later( + 60.0, lambda k=dedup_key: self._active_alerts.discard(k) + ) + + # Broadcast to SSE subscribers + await self._broadcast(event) + + # Wait for next event + jitter = random.uniform(-0.5, 0.5) + await asyncio.sleep(max(0.5, self.interval + jitter)) + + except asyncio.CancelledError: + break + except Exception as e: + logger.error(f"Event producer error: {e}", exc_info=True) + await asyncio.sleep(1) + + logger.info("Event producer stopped") + + def stop(self): + """Stop the event production loop.""" + self._running = False diff --git a/agentic_ai/workflow/fraud_detection_durable/provision_dts.ps1 b/agentic_ai/workflow/fraud_detection_durable/provision_dts.ps1 new file mode 100644 index 000000000..141242282 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/provision_dts.ps1 @@ -0,0 +1,193 @@ +<# +.SYNOPSIS + Provisions an Azure Durable Task Scheduler (DTS) resource and task hub + for the Fraud Detection Workshop demo. + +.DESCRIPTION + This script: + 1. Installs the `durabletask` Azure CLI extension (if missing) + 2. Creates a resource group (if it doesn't already exist) + 3. Creates a DTS Scheduler (Consumption SKU — free-tier preview) + 4. Creates a Task Hub inside the scheduler + 5. Assigns "Durable Task Data Contributor" RBAC to the current user + 6. Outputs the DTS_ENDPOINT and DTS_TASKHUB values for .env + +.PARAMETER ResourceGroup + Name of the Azure resource group. Default: "rg-fraud-workshop" + +.PARAMETER Location + Azure region. Default: "northcentralus" + Supported regions: northcentralus, westus2, westus3, eastus, etc. + +.PARAMETER SchedulerName + Name of the DTS Scheduler. Default: "dts-fraud-workshop" + +.PARAMETER TaskHubName + Name of the Task Hub. Default: "fraud-detection" + +.PARAMETER SkuName + SKU for the scheduler. Default: "consumption" (free preview). + Use "dedicated" for production (takes ~15min to provision). + +.EXAMPLE + .\provision_dts.ps1 + .\provision_dts.ps1 -Location eastus -SchedulerName my-scheduler + .\provision_dts.ps1 -SkuName dedicated +#> + +param( + [string]$ResourceGroup = "rg-fraud-workshop", + [string]$Location = "northcentralus", + [string]$SchedulerName = "dts-fraud-workshop", + [string]$TaskHubName = "fraud-detection", + [string]$SkuName = "consumption" +) + +$ErrorActionPreference = "Stop" + +Write-Host "" +Write-Host "========================================" -ForegroundColor Cyan +Write-Host " Azure Durable Task Scheduler Provisioning" -ForegroundColor Cyan +Write-Host "========================================" -ForegroundColor Cyan +Write-Host "" + +# ── 0. Verify Azure CLI login ─────────────────────────────────────────────── +Write-Host "[0/5] Checking Azure CLI login..." -ForegroundColor Yellow +try { + $account = az account show --output json 2>&1 | ConvertFrom-Json + $subscriptionId = $account.id + $userName = $account.user.name + Write-Host " Logged in as: $userName" -ForegroundColor Green + Write-Host " Subscription: $($account.name) ($subscriptionId)" -ForegroundColor Green +} +catch { + Write-Host " ERROR: Not logged in. Run 'az login' first." -ForegroundColor Red + exit 1 +} + +# ── 1. Install durabletask CLI extension ───────────────────────────────────── +Write-Host "" +Write-Host "[1/5] Installing durabletask CLI extension..." -ForegroundColor Yellow +$ErrorActionPreference = "Continue" +$extCheck = az extension show --name durabletask --query "name" -o tsv 2>$null +$ErrorActionPreference = "Stop" +if ($extCheck -eq "durabletask") { + Write-Host " Already installed." -ForegroundColor Green +} +else { + az extension add --name durabletask --yes 2>$null + Write-Host " Installed." -ForegroundColor Green +} +$ErrorActionPreference = "Stop" + +# ── 2. Create resource group ──────────────────────────────────────────────── +Write-Host "" +Write-Host "[2/5] Creating resource group '$ResourceGroup' in '$Location'..." -ForegroundColor Yellow +$rgExists = az group exists --name $ResourceGroup 2>$null +if ($rgExists -eq "true") { + Write-Host " Resource group already exists." -ForegroundColor Green +} +else { + az group create --name $ResourceGroup --location $Location --output none + Write-Host " Created." -ForegroundColor Green +} + +# ── 3. Create DTS Scheduler ──────────────────────────────────────────────── +Write-Host "" +Write-Host "[3/5] Creating DTS Scheduler '$SchedulerName' ($SkuName SKU)..." -ForegroundColor Yellow +Write-Host " This may take 5-15 minutes..." -ForegroundColor DarkYellow + +$schedulerArgs = @( + "durabletask", "scheduler", "create", + "--name", $SchedulerName, + "--resource-group", $ResourceGroup, + "--location", $Location, + "--ip-allowlist", "[0.0.0.0/0]", + "--sku-name", $SkuName +) +if ($SkuName -eq "dedicated") { + $schedulerArgs += @("--sku-capacity", "1") +} + +$ErrorActionPreference = "Continue" +$schedulerJson = az @schedulerArgs --output json 2>&1 | Where-Object { $_ -notmatch "^WARNING:" -and $_ -notmatch "^$" } +$ErrorActionPreference = "Stop" + +$scheduler = $schedulerJson | Out-String | ConvertFrom-Json + +$endpoint = $scheduler.properties.endpoint +if (-not $endpoint) { + # If creation returned quickly, the scheduler may already exist — try show + $ErrorActionPreference = "Continue" + $schedulerJson = az durabletask scheduler show --name $SchedulerName --resource-group $ResourceGroup --output json 2>&1 | Where-Object { $_ -notmatch "^WARNING:" } + $ErrorActionPreference = "Stop" + $scheduler = $schedulerJson | Out-String | ConvertFrom-Json + $endpoint = $scheduler.properties.endpoint +} + +Write-Host " Scheduler endpoint: $endpoint" -ForegroundColor Green +Write-Host " Provisioning state: $($scheduler.properties.provisioningState)" -ForegroundColor Green + +# ── 4. Create Task Hub ────────────────────────────────────────────────────── +Write-Host "" +Write-Host "[4/5] Creating Task Hub '$TaskHubName'..." -ForegroundColor Yellow +$ErrorActionPreference = "Continue" +az durabletask taskhub create --resource-group $ResourceGroup --scheduler-name $SchedulerName --name $TaskHubName --output none 2>$null +$ErrorActionPreference = "Stop" +Write-Host " Created." -ForegroundColor Green + +# ── 5. Assign RBAC to current user ────────────────────────────────────────── +Write-Host "" +Write-Host "[5/5] Assigning 'Durable Task Data Contributor' role to $userName..." -ForegroundColor Yellow + +$scope = "/subscriptions/$subscriptionId/resourceGroups/$ResourceGroup/providers/Microsoft.DurableTask/schedulers/$SchedulerName" + +# Get current user's object ID +$ErrorActionPreference = "Continue" +$assignee = az ad signed-in-user show --query "id" -o tsv 2>$null +$ErrorActionPreference = "Stop" + +if ($assignee) { + Write-Host " User object ID: $assignee" -ForegroundColor Gray + $ErrorActionPreference = "Continue" + $existing = az role assignment list --assignee $assignee --scope $scope --role "Durable Task Data Contributor" --query "length(@)" -o tsv 2>$null + $ErrorActionPreference = "Stop" + if ($existing -gt 0) { + Write-Host " Role already assigned." -ForegroundColor Green + } + else { + # Use --assignee-object-id + --assignee-principal-type for guest/EXT# users + $ErrorActionPreference = "Continue" + az role assignment create ` + --assignee-object-id $assignee ` + --assignee-principal-type "User" ` + --role "Durable Task Data Contributor" ` + --scope $scope ` + --output none 2>&1 | Where-Object { $_ -notmatch "^WARNING:" } + $ErrorActionPreference = "Stop" + Write-Host " Role assigned. (RBAC propagation may take 5-10 minutes)" -ForegroundColor Green + } +} +else { + Write-Host " WARNING: Could not determine user object ID. Assign the role manually:" -ForegroundColor DarkYellow + Write-Host " az role assignment create --assignee-object-id --assignee-principal-type User --role 'Durable Task Data Contributor' --scope '$scope'" -ForegroundColor DarkYellow +} + +# ── Summary ────────────────────────────────────────────────────────────────── +$dashboardUrl = "https://dashboard.durabletask.io/?endpoint=$endpoint&taskhub=$TaskHubName" + +Write-Host "" +Write-Host "========================================" -ForegroundColor Cyan +Write-Host " PROVISIONING COMPLETE" -ForegroundColor Cyan +Write-Host "========================================" -ForegroundColor Cyan +Write-Host "" +Write-Host "Add these to your .env file:" -ForegroundColor Green +Write-Host "" +Write-Host " DTS_ENDPOINT=$endpoint" -ForegroundColor White +Write-Host " DTS_TASKHUB=$TaskHubName" -ForegroundColor White +Write-Host "" +Write-Host "Dashboard: $dashboardUrl" -ForegroundColor Cyan +Write-Host "" +Write-Host "To tear down later:" -ForegroundColor DarkYellow +Write-Host " az group delete --name $ResourceGroup --yes --no-wait" -ForegroundColor DarkYellow +Write-Host "" diff --git a/agentic_ai/workflow/fraud_detection_durable/pyproject.toml b/agentic_ai/workflow/fraud_detection_durable/pyproject.toml index cac9c883f..a68f35b94 100644 --- a/agentic_ai/workflow/fraud_detection_durable/pyproject.toml +++ b/agentic_ai/workflow/fraud_detection_durable/pyproject.toml @@ -8,6 +8,9 @@ dependencies = [ # Agent Framework (includes azure sub-package) "agent-framework==1.0.0b260212", + # Durable Task Agent Framework integration + "agent-framework-durabletask>=1.0.0b260212", + # Observability "azure-monitor-opentelemetry>=1.8.5", diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx index 1c8421430..f8644dc2d 100644 --- a/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx @@ -15,6 +15,7 @@ import { import SecurityIcon from '@mui/icons-material/Security'; import CloudSyncIcon from '@mui/icons-material/CloudSync'; import WorkflowVisualizer from './components/WorkflowVisualizer'; +import EventFeed from './components/EventFeed'; import { API_CONFIG } from './constants/config'; import ControlPanel from './components/ControlPanel'; import AnalystDecisionPanel from './components/AnalystDecisionPanel'; @@ -134,6 +135,19 @@ function App() { location_analysis_executor: 'running', billing_charge_executor: 'running', }); + setPendingDecision(null); + } else if (customStatus.includes('Re-investigating')) { + // Re-investigation after analyst rejection - reset analysis nodes + setExecutorStates({ + alert_router: 'completed', + usage_pattern_executor: 'running', + location_analysis_executor: 'running', + billing_charge_executor: 'running', + fraud_risk_aggregator: 'idle', + review_gateway: 'idle', + }); + setPendingDecision(null); + setWorkflowRunning(true); } else if (customStatus.includes('Awaiting analyst')) { // Waiting for analyst - aggregation complete, waiting for human setExecutorStates({ @@ -179,8 +193,8 @@ function App() { })); } - // Check if decision is required - if (data.decision_required) { + // Check if decision is required (including re-investigation returns) + if (data.decision_required || customStatus.includes('Awaiting analyst')) { setPendingDecision({ instance_id: instanceId, alert_id: selectedAlert?.alert_id, @@ -238,11 +252,8 @@ function App() { if (data.type === 'decision_submitted') { setPendingDecision(null); setWorkflowRunning(true); - setExecutorStates((prev) => ({ - ...prev, - review_gateway: 'completed', - fraud_action_executor: 'running', - })); + // Don't assume approve — the status_update handler will set the right state + // based on whether the orchestration goes to "Executing" or "Re-investigating" } }, [instanceId, selectedAlert, addEvent]); @@ -299,8 +310,10 @@ function App() { body: JSON.stringify({ instance_id: instanceId, alert_id: selectedAlert?.alert_id, - approved_action: decision.approved_action, - analyst_notes: decision.analyst_notes, + approved: decision.approved !== false, + approved_action: decision.approved_action || 'clear', + feedback: decision.feedback || '', + analyst_notes: decision.analyst_notes || '', analyst_id: 'analyst_ui', }), }); @@ -345,8 +358,8 @@ function App() { {/* Main Content */} - {/* Left Column - Controls and Decision Panel */} - + {/* Left Column - Controls, Decision Panel, and Live Feed */} + )} + + {/* Layer 1 Live Feed */} + + { + // Connect the auto-triggered workflow to the visualizer + const syntheticAlert = { + alert_id: alertData.alert_id, + customer_id: alertData.customer_id, + alert_type: alertData.alert_type, + description: alertData.description, + severity: alertData.severity, + auto_detected: true, + }; + setSelectedAlert(syntheticAlert); + setWorkflowRunning(true); + setEvents([]); + setExecutorStates({ alert_router: 'running' }); + setPendingDecision(null); + setStepDetails({}); + // Setting instanceId triggers the WebSocket connection + setInstanceId(alertData.instance_id); + addEvent({ + type: 'workflow_auto_started', + instance_id: alertData.instance_id, + alert_id: alertData.alert_id, + message: `Layer 1 auto-detected ${alertData.alert_type} — investigation started`, + }); + }} + /> + {/* Center Column - Workflow Visualization */} - + Workflow Graph diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx index 1c87aea79..69cca1a2b 100644 --- a/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx @@ -12,32 +12,50 @@ import { Chip, Alert, Divider, + ToggleButton, + ToggleButtonGroup, } from '@mui/material'; import GavelIcon from '@mui/icons-material/Gavel'; import SendIcon from '@mui/icons-material/Send'; +import CheckCircleIcon from '@mui/icons-material/CheckCircle'; +import CancelIcon from '@mui/icons-material/Cancel'; import { ACTION_OPTIONS } from '../constants/workflow'; /** - * Panel for analyst to make decisions on fraud alerts (Durable version) - * @param {Object} props - Component props - * @param {Object} props.decision - Decision request object with instance_id, alert_id, customer_id - * @param {Function} props.onSubmit - Callback to submit decision + * Panel for analyst to approve or reject (with feedback) fraud alerts. + * Supports the stateful HITL feedback loop. */ function AnalystDecisionPanel({ decision, onSubmit }) { + const [mode, setMode] = useState('approve'); // 'approve' or 'reject' const [selectedAction, setSelectedAction] = useState( - decision.recommended_action || 'block' + decision.recommended_action || 'lock_account' ); const [notes, setNotes] = useState(''); + const [feedback, setFeedback] = useState(''); const handleSubmit = () => { - onSubmit({ - instance_id: decision.instance_id, - alert_id: decision.alert_id, - customer_id: decision.customer_id, - approved_action: selectedAction, - analyst_notes: notes || 'Analyst decision from UI', - analyst_id: 'analyst_ui', - }); + if (mode === 'approve') { + onSubmit({ + instance_id: decision.instance_id, + alert_id: decision.alert_id, + customer_id: decision.customer_id, + approved: true, + approved_action: selectedAction, + analyst_notes: notes || 'Approved from UI', + analyst_id: 'analyst_ui', + }); + } else { + onSubmit({ + instance_id: decision.instance_id, + alert_id: decision.alert_id, + customer_id: decision.customer_id, + approved: false, + approved_action: '', + feedback: feedback || 'Please investigate further', + analyst_notes: notes, + analyst_id: 'analyst_ui', + }); + } }; return ( @@ -99,27 +117,71 @@ function AnalystDecisionPanel({ decision, onSubmit }) { - {/* Decision Form */} - - Your Decision - - + {/* Approve / Reject Toggle */} + val && setMode(val)} + fullWidth + size="small" + sx={{ mb: 0.5 }} + > + + + Approve + + + + Reject + + + + {/* Approve: Action selector */} + {mode === 'approve' && ( + + Action to Execute + + + )} + + {/* Reject: Feedback field */} + {mode === 'reject' && ( + <> + + + Provide feedback for the AI to re-investigate with more context. + + + setFeedback(e.target.value)} + placeholder="e.g. Check billing charges in the last 7 days more carefully..." + sx={{ '& .MuiInputBase-input': { fontSize: '0.875rem' } }} + required + /> + + )} } + startIcon={mode === 'approve' ? : } onClick={handleSubmit} + disabled={mode === 'reject' && !feedback.trim()} sx={{ mt: 0.5, py: 0.75 }} > - Submit Decision + {mode === 'approve' ? 'Approve & Execute' : 'Reject & Re-investigate'} ); diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/components/EventFeed.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/EventFeed.jsx new file mode 100644 index 000000000..ba633c545 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/EventFeed.jsx @@ -0,0 +1,338 @@ +import React, { useState, useEffect, useRef, useCallback } from 'react'; +import { + Box, + Paper, + Typography, + Chip, + IconButton, + Tooltip, + Divider, + Badge, +} from '@mui/material'; +import PauseIcon from '@mui/icons-material/Pause'; +import PlayArrowIcon from '@mui/icons-material/PlayArrow'; +import FiberManualRecordIcon from '@mui/icons-material/FiberManualRecord'; +import WarningAmberIcon from '@mui/icons-material/WarningAmber'; +import DeleteSweepIcon from '@mui/icons-material/DeleteSweep'; +import OpenInNewIcon from '@mui/icons-material/OpenInNew'; +import { API_CONFIG } from '../constants/config'; + +const MAX_EVENTS = 80; + +const EVENT_TYPE_COLORS = { + login: '#2196f3', + transaction: '#4caf50', + data_usage: '#9c27b0', + api_call: '#607d8b', + auth_failure: '#f44336', +}; + +const EVENT_TYPE_LABELS = { + login: 'LOGIN', + transaction: 'TXN', + data_usage: 'DATA', + api_call: 'API', + auth_failure: 'AUTH', +}; + +/** + * Live Event Feed panel showing Layer 1 telemetry events. + * + * Connects to the backend's /api/events/stream SSE endpoint. + * Green = normal, amber/red = anomaly detected. + * + * When Layer 1 auto-triggers a workflow, a "workflow_auto_started" event + * arrives with the DTS instance_id. The user can click to connect the + * WorkflowVisualizer to that running orchestration. + */ +export default function EventFeed({ onAnomalyDetected, onWorkflowAutoStarted }) { + const [events, setEvents] = useState([]); + const [paused, setPaused] = useState(false); + const [connected, setConnected] = useState(false); + const [anomalyCount, setAnomalyCount] = useState(0); + const [autoStartedAlerts, setAutoStartedAlerts] = useState([]); + const scrollRef = useRef(null); + const pausedRef = useRef(false); + const eventsRef = useRef([]); + const eventSourceRef = useRef(null); + + // Keep ref in sync with state + useEffect(() => { pausedRef.current = paused; }, [paused]); + + // Auto-scroll to bottom when new events arrive + useEffect(() => { + if (!paused && scrollRef.current) { + scrollRef.current.scrollTop = scrollRef.current.scrollHeight; + } + }, [events, paused]); + + // Connect to SSE stream + useEffect(() => { + const url = `${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.EVENTS_STREAM}`; + console.log('Connecting to event stream:', url); + + const es = new EventSource(url); + eventSourceRef.current = es; + + es.onopen = () => { + console.log('Event stream connected'); + setConnected(true); + }; + + es.onmessage = (msg) => { + try { + const event = JSON.parse(msg.data); + + // Handle workflow auto-started events — these carry the DTS instance_id + if (event.event_type === 'workflow_auto_started') { + const alertInfo = { + instance_id: event.details?.instance_id, + alert_id: event.details?.alert_id, + alert_type: event.details?.alert_type, + description: event.details?.description, + severity: event.details?.severity, + customer_id: event.customer_id, + customer_name: event.customer_name, + timestamp: event.timestamp, + }; + setAutoStartedAlerts(prev => [alertInfo, ...prev].slice(0, 5)); + setAnomalyCount(prev => prev + 1); + // Don't add to the normal event list — it will render as a banner + return; + } + + if (event.is_anomaly) { + setAnomalyCount(prev => prev + 1); + if (onAnomalyDetected) { + onAnomalyDetected(event); + } + } + + if (!pausedRef.current) { + eventsRef.current = [...eventsRef.current.slice(-(MAX_EVENTS - 1)), event]; + setEvents(eventsRef.current); + } + } catch (err) { + console.error('Error parsing event:', err); + } + }; + + es.onerror = () => { + setConnected(false); + console.warn('Event stream disconnected, will retry...'); + }; + + return () => { + es.close(); + eventSourceRef.current = null; + }; + }, [onAnomalyDetected]); + + const handleClear = useCallback(() => { + eventsRef.current = []; + setEvents([]); + setAnomalyCount(0); + setAutoStartedAlerts([]); + }, []); + + const formatTime = (ts) => { + try { + const d = new Date(ts); + return d.toLocaleTimeString('en-US', { hour12: false, hour: '2-digit', minute: '2-digit', second: '2-digit' }); + } catch { + return ''; + } + }; + + const formatDetails = (event) => { + const d = event.details || {}; + switch (event.event_type) { + case 'login': + return `${d.country || '?'} · ${d.ip || ''}`; + case 'transaction': + return `$${(d.amount || 0).toFixed(2)} · ${d.merchant || ''}`; + case 'data_usage': + return `${(d.gb_used || 0).toFixed(1)} GB`; + case 'api_call': + return `${d.endpoint || ''} · ${d.status_code || ''}`; + case 'auth_failure': + return `${d.reason || ''} · ${d.ip || ''}`; + default: + return JSON.stringify(d).slice(0, 40); + } + }; + + return ( + + {/* Header */} + + + + Live Feed + + {anomalyCount > 0 && ( + } + label={anomalyCount} + size="small" + color="warning" + sx={{ height: 20, fontSize: 11 }} + /> + )} + + + + setPaused(p => !p)}> + {paused ? : } + + + + + + + + + + + {/* Auto-started workflow banners */} + {autoStartedAlerts.length > 0 && ( + + {autoStartedAlerts.map((a, i) => ( + onWorkflowAutoStarted && onWorkflowAutoStarted(a)} + sx={{ + display: 'flex', + alignItems: 'center', + gap: 0.5, + p: 0.5, + borderRadius: 1, + bgcolor: 'rgba(244,67,54,0.10)', + cursor: onWorkflowAutoStarted ? 'pointer' : 'default', + '&:hover': onWorkflowAutoStarted ? { bgcolor: 'rgba(244,67,54,0.20)' } : {}, + transition: 'background-color 0.15s', + }} + > + + + + ⚡ Auto-investigation started + + + C{a.customer_id} · {a.alert_type?.replace(/_/g, ' ')} · {a.severity} + + + {onWorkflowAutoStarted && ( + + + + )} + + ))} + + )} + + {/* Event list */} + + {events.length === 0 && ( + + {connected ? 'Waiting for events…' : 'Connecting to event stream…'} + + )} + {events.map((evt, i) => ( + + {/* Indicator dot */} + + {/* Timestamp */} + + {formatTime(evt.timestamp)} + + {/* Event type badge */} + + {/* Customer */} + + C{evt.customer_id} + + {/* Details */} + + {formatDetails(evt)} + + {/* Anomaly indicator */} + {evt.alert_triggered && ( + + )} + {evt.is_anomaly && !evt.alert_triggered && ( + + )} + + ))} + + + {/* Footer status */} + + + {events.length} events + + {paused && ( + + )} + + + ); +} diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js index 617845f48..d6295ab58 100644 --- a/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js @@ -13,10 +13,10 @@ const isSameOrigin = typeof window !== 'undefined' && !window.location.port.includes('3000'); // Not React dev server // In production (same origin), use relative URLs. In dev, use localhost. -const defaultBaseUrl = isSameOrigin ? '' : 'http://localhost:8002'; +const defaultBaseUrl = isSameOrigin ? '' : 'http://localhost:8001'; const defaultWsUrl = isSameOrigin ? `${window.location.protocol === 'https:' ? 'wss:' : 'ws:'}//${window.location.host}/ws` - : 'ws://localhost:8002/ws'; + : 'ws://localhost:8001/ws'; /** * API configuration constants @@ -28,6 +28,7 @@ export const API_CONFIG = { ALERTS: '/api/alerts', WORKFLOW_START: '/api/workflow/start', WORKFLOW_DECISION: '/api/workflow/decision', + EVENTS_STREAM: '/api/events/stream', }, }; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js index 07d791540..a04431ccc 100644 --- a/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js @@ -42,6 +42,14 @@ export const ACTION_OPTIONS = [ { value: 'both', label: 'Lock Account & Refund', color: 'error' }, ]; +/** + * Decision modes + */ +export const DECISION_MODES = [ + { value: 'approve', label: 'Approve Action', color: 'success' }, + { value: 'reject', label: 'Reject & Request Re-investigation', color: 'error' }, +]; + /** * WebSocket ready states */ diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/vite.config.js b/agentic_ai/workflow/fraud_detection_durable/ui/vite.config.js index e05580dca..af1187616 100644 --- a/agentic_ai/workflow/fraud_detection_durable/ui/vite.config.js +++ b/agentic_ai/workflow/fraud_detection_durable/ui/vite.config.js @@ -1,12 +1,24 @@ import { defineConfig } from 'vite' import react from '@vitejs/plugin-react' +import path from 'path' + +const __uiRoot = path.dirname(new URL(import.meta.url).pathname).replace(/^\/([A-Z]:)/, '$1') // https://vitejs.dev/config/ export default defineConfig({ plugins: [react()], + // Restrict dependency scanning to this UI directory only + optimizeDeps: { + entries: ['index.html', 'src/**/*.{js,jsx}'], + }, server: { port: 3000, open: true, + fs: { + // Only allow serving files from the UI directory + strict: true, + allow: [__uiRoot], + }, proxy: { '/api': { target: 'http://localhost:8001', diff --git a/agentic_ai/workflow/fraud_detection_durable/uv.lock b/agentic_ai/workflow/fraud_detection_durable/uv.lock index da416eb73..019742679 100644 --- a/agentic_ai/workflow/fraud_detection_durable/uv.lock +++ b/agentic_ai/workflow/fraud_detection_durable/uv.lock @@ -235,7 +235,7 @@ wheels = [ [[package]] name = "agent-framework-durabletask" -version = "1.0.0b260130" +version = "1.0.0b260212" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "agent-framework-core" }, @@ -243,9 +243,9 @@ dependencies = [ { name = "durabletask-azuremanaged" }, { name = "python-dateutil" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/e3/95/9d5ee7fd1fdcd52c10aa1b2902964701d1d62b9d35cc7d05115b90db6329/agent_framework_durabletask-1.0.0b260130.tar.gz", hash = "sha256:63a2c8e0968a51d8e132892e9d385d2b82ccb95263d2c0316dc46b0eaa4dd7a4", size = 30285 } +sdist = { url = "https://files.pythonhosted.org/packages/2f/2f/3747a6346190bbcd37cde269999fee9c5ad60492d3706e8c4ee3019830f4/agent_framework_durabletask-1.0.0b260212.tar.gz", hash = "sha256:dd54fb07410c60c5ad4550f0feaf80a4660bbbb98d137f4f8298a91af0fa9406", size = 30617 } wheels = [ - { url = "https://files.pythonhosted.org/packages/ba/22/122ed515935926137cc3c6ca795ef01b30feb82160cfc0f29a34f9d603de/agent_framework_durabletask-1.0.0b260130-py3-none-any.whl", hash = "sha256:a46e292800d10a62ce0923efe753594ddbf0bd6d1bb6e1258380f0dbf7d0302f", size = 36357 }, + { url = "https://files.pythonhosted.org/packages/79/5e/31f877ed9ad8945ef719cdf4728536037146bfbd4580322b4318e7389dc9/agent_framework_durabletask-1.0.0b260212-py3-none-any.whl", hash = "sha256:8a96927c244bae1498fc9cd73782dac3b1efeccff14e64f738feba68d4584733", size = 36413 }, ] [[package]] @@ -980,6 +980,7 @@ version = "0.1.0" source = { virtual = "." } dependencies = [ { name = "agent-framework" }, + { name = "agent-framework-durabletask" }, { name = "azure-identity" }, { name = "azure-monitor-opentelemetry" }, { name = "durabletask-azuremanaged" }, @@ -994,6 +995,7 @@ dependencies = [ [package.metadata] requires-dist = [ { name = "agent-framework", specifier = "==1.0.0b260212" }, + { name = "agent-framework-durabletask", specifier = ">=1.0.0b260212" }, { name = "azure-identity", specifier = ">=1.15.0" }, { name = "azure-monitor-opentelemetry", specifier = ">=1.8.5" }, { name = "durabletask-azuremanaged", specifier = ">=1.0.0a1" }, diff --git a/agentic_ai/workflow/fraud_detection_durable/worker.py b/agentic_ai/workflow/fraud_detection_durable/worker.py index 29d041f90..3d7bf2c2b 100644 --- a/agentic_ai/workflow/fraud_detection_durable/worker.py +++ b/agentic_ai/workflow/fraud_detection_durable/worker.py @@ -1,18 +1,19 @@ """ Durable Task Worker for Fraud Detection. -This worker hosts: -1. The main fraud_detection_orchestration (outer layer) -2. Activity functions for side effects -3. The inner workflow runs as an activity +Hybrid architecture combining: +- DurableAIAgentWorker: Registers FraudAnalysisAgent as a durable entity with + persistent conversation state (DurableAgentState) +- DurableAIAgentOrchestrationContext: Type-safe agent calls from within orchestrations +- Inner Workflow: Complex fan-out/fan-in topology with MCP tools for fast execution +- DTS Orchestration: Durability, HITL, timeouts, crash recovery, feedback loop -Architecture: -- DTS Orchestration handles: durability, HITL, timeouts, crash recovery -- Inner Workflow handles: complex fan-out/fan-in topology +The FraudAnalysisAgent entity preserves conversation history across invocations, +enabling stateful re-investigation when an analyst rejects and provides feedback. Prerequisites: - Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT -- Start Durable Task Scheduler: docker run -d --name dts -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest +- Start Durable Task Scheduler emulator - Start MCP Server: cd mcp && uv run mcp_service.py """ @@ -49,11 +50,22 @@ # ------------------------------------------------------------------ from azure.identity import DefaultAzureCredential, ManagedIdentityCredential from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker -from durabletask.task import ActivityContext, OrchestrationContext, Task, when_any, when_all +from durabletask.task import ActivityContext, OrchestrationContext, Task, when_any from pydantic import BaseModel, ValidationError -from agent_framework import MCPStreamableHTTPTool, WorkflowEvent +from agent_framework import ( + AgentResponse, + BaseAgent, + Content, + MCPStreamableHTTPTool, + Message, + WorkflowEvent, +) from agent_framework.azure import AzureOpenAIChatClient +from agent_framework_durabletask import ( + DurableAIAgentOrchestrationContext, + DurableAIAgentWorker, +) from fraud_analysis_workflow import ( SuspiciousActivityAlert, @@ -68,9 +80,151 @@ logger.info("✅ Application Insights observability enabled for fraud workflow worker") # Constants +FRAUD_AGENT_NAME = "FraudAnalysisAgent" ANALYST_APPROVAL_EVENT = "AnalystDecision" +# ============================================================================ +# FraudAnalysisAgent - Custom agent wrapping the inner workflow +# ============================================================================ + + +class FraudAnalysisAgent(BaseAgent): + """Custom agent that runs the fraud analysis workflow as a DTS entity. + + Registered via DurableAIAgentWorker as entity 'dafx-FraudAnalysisAgent'. + The entity persists conversation history (DurableAgentState), enabling + stateful re-investigation when analyst provides feedback. + + Each invocation creates fresh MCP connections (event-loop safe for DTS + entities which use asyncio.run() per call). + """ + + def __init__(self, *, name: str = FRAUD_AGENT_NAME): + super().__init__( + name=name, + description="Fraud analysis agent with fan-out workflow topology and MCP tools", + ) + self._mcp_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") + self._deployment_name = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4o") + + async def run(self, messages: Any = None, *, stream: bool = False, **kwargs: Any) -> AgentResponse: + """Run fraud analysis. Entity calls this with conversation history. + + On first call: messages contain the alert JSON. + On subsequent calls (re-investigation): messages contain full history + including previous analysis and analyst feedback. + """ + if stream: + # DTS entity always tries stream=True first, then falls back. + # Raise TypeError with "stream" in message so entity falls back cleanly. + raise TypeError("stream=True not supported by FraudAnalysisAgent") + + msg_list: list[Message] = messages if isinstance(messages, list) else [] + alert_data, previous_context = self._parse_messages(msg_list) + + if alert_data is None: + raise ValueError("No alert data found in messages") + + logger.info(f"[FraudAnalysisAgent] Running analysis for alert {alert_data.get('alert_id')}") + if previous_context: + logger.info("[FraudAnalysisAgent] Re-investigation with analyst feedback") + + # Create fresh MCP connection for this event loop (DTS entity uses asyncio.run()) + mcp_tool = MCPStreamableHTTPTool(name="contoso_mcp", url=self._mcp_uri, timeout=30) + async with mcp_tool: + # Create chat client + azure_client_id = os.getenv("AZURE_CLIENT_ID") + credential = ( + ManagedIdentityCredential(client_id=azure_client_id) + if azure_client_id + else DefaultAzureCredential() + ) + chat_client = AzureOpenAIChatClient( + credential=credential, + deployment_name=self._deployment_name, + ) + + # If re-investigating, enrich the alert description with previous context + alert = SuspiciousActivityAlert(**alert_data) + if previous_context: + alert = alert.model_copy(update={ + "description": ( + f"{alert.description}\n\n" + f"--- RE-INVESTIGATION CONTEXT ---\n{previous_context}" + ), + }) + + # Build and run the fan-out workflow + workflow = create_fraud_analysis_workflow(mcp_tool, chat_client) + assessment: FraudRiskAssessment | None = None + + async for event in workflow.run(alert, stream=True): + if isinstance(event, WorkflowEvent) and event.type == "output": + if isinstance(event.data, FraudRiskAssessment): + assessment = event.data + break + + if assessment is None: + raise ValueError("Workflow did not produce FraudRiskAssessment") + + # Add customer_id from original alert + assessment_dict = assessment.model_dump() + assessment_dict["customer_id"] = alert_data.get("customer_id", 0) + + logger.info( + f"[FraudAnalysisAgent] Analysis complete: " + f"risk={assessment.overall_risk_score:.2f}, action={assessment.recommended_action}" + ) + + # Return as AgentResponse so entity can persist in DurableAgentState + response_text = json.dumps(assessment_dict, indent=2) + return AgentResponse( + messages=[Message(role="assistant", contents=[Content.from_text(response_text)])], + ) + + def _parse_messages(self, messages: list[Message]) -> tuple[dict | None, str | None]: + """Extract alert data and previous context from entity conversation history. + + Returns: + (alert_data, previous_context) - alert_data is the original alert dict, + previous_context is a string with previous findings + analyst feedback. + """ + alert_data: dict | None = None + context_parts: list[str] = [] + + for msg in messages: + # Extract text from message + text = "" + if hasattr(msg, "text") and msg.text: + text = msg.text + elif hasattr(msg, "contents"): + for c in msg.contents: + if hasattr(c, "text") and c.text: + text += c.text + + if not text: + continue + + role = getattr(msg, "role", "") + + if role == "user": + if alert_data is None: + # First user message = alert JSON + try: + alert_data = json.loads(text) + except json.JSONDecodeError: + alert_data = None + else: + # Subsequent user messages = analyst feedback + context_parts.append(f"ANALYST FEEDBACK: {text}") + elif role == "assistant" and alert_data is not None: + # Previous analysis response + context_parts.append(f"PREVIOUS ANALYSIS:\n{text[:2000]}") + + return alert_data, "\n\n".join(context_parts) if context_parts else None + + # ============================================================================ # Input/Output Models # ============================================================================ @@ -84,14 +238,16 @@ class FraudDetectionInput(BaseModel): description: str = "" timestamp: str = "" severity: str = "medium" - approval_timeout_hours: float = 72.0 # 72 hours for analyst review + approval_timeout_hours: float = 72.0 max_review_attempts: int = 3 class AnalystDecision(BaseModel): - """Human analyst decision.""" + """Human analyst decision - supports approve, reject with feedback, or timeout.""" alert_id: str - approved_action: str # "lock_account", "refund_charges", "clear", "both" + approved: bool # True = approve action, False = reject and request re-investigation + approved_action: str = "clear" # "lock_account", "refund_charges", "clear", "both" + feedback: str = "" # Analyst feedback for re-investigation (when approved=False) analyst_notes: str = "" analyst_id: str = "analyst" @@ -104,142 +260,38 @@ class ActionResult(BaseModel): details: str -# ============================================================================ -# Global Resources (initialized once) -# ============================================================================ - -_mcp_tool: MCPStreamableHTTPTool | None = None -_chat_client: AzureOpenAIChatClient | None = None -_main_loop: asyncio.AbstractEventLoop | None = None - - -async def _ensure_resources(): - """Initialize global resources if not already done.""" - global _mcp_tool, _chat_client - - if _mcp_tool is None: - mcp_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") - _mcp_tool = MCPStreamableHTTPTool(name="contoso_mcp", url=mcp_uri, timeout=30) - await _mcp_tool.__aenter__() - logger.info(f"✓ MCP tool initialized at {mcp_uri}") - - if _chat_client is None: - # Use managed identity if AZURE_CLIENT_ID is set, otherwise DefaultAzureCredential - azure_client_id = os.getenv("AZURE_CLIENT_ID") - if azure_client_id: - from azure.identity import ManagedIdentityCredential - credential = ManagedIdentityCredential(client_id=azure_client_id) - logger.info(f"Using ManagedIdentityCredential with client_id: {azure_client_id}") - else: - credential = DefaultAzureCredential() - logger.info("Using DefaultAzureCredential") - - _chat_client = AzureOpenAIChatClient( - credential=credential, - deployment_name=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4o"), - ) - logger.info("✓ Azure OpenAI client initialized") - - return _mcp_tool, _chat_client - - # ============================================================================ # Activity Functions # ============================================================================ -def run_fraud_analysis(context: ActivityContext, alert_dict: dict) -> dict: - """ - Activity that runs the inner fraud analysis workflow. - - This is the bridge between Durable Task and the Workflow: - - Receives alert as dict - - Runs the fan-out → aggregate workflow - - Returns FraudRiskAssessment as dict - """ - logger.info(f"[Activity] run_fraud_analysis starting for alert {alert_dict.get('alert_id')}") - - async def _run_workflow(): - mcp_tool, chat_client = await _ensure_resources() - - # Create the workflow - workflow = create_fraud_analysis_workflow(mcp_tool, chat_client) - - # Create alert object - alert = SuspiciousActivityAlert(**alert_dict) - - # Run workflow and collect output - assessment: FraudRiskAssessment | None = None - - async for event in workflow.run(alert, stream=True): - if isinstance(event, WorkflowEvent) and event.type == "output": - if isinstance(event.data, FraudRiskAssessment): - assessment = event.data - break - - if assessment is None: - raise ValueError("Workflow did not produce FraudRiskAssessment") - - # Add customer_id from original alert - assessment_dict = assessment.model_dump() - assessment_dict["customer_id"] = alert.customer_id - - return assessment_dict - - # Run the async workflow on the main event loop (where MCP tool was initialized) - if _main_loop is None or _main_loop.is_closed(): - raise RuntimeError("Main event loop not available. Was the worker started correctly?") +def notify_analyst(context: ActivityContext, assessment_text: str) -> str: + """Activity to notify analyst for review.""" try: - future = asyncio.run_coroutine_threadsafe(_run_workflow(), _main_loop) - result = future.result(timeout=300) # 5 minute timeout - except Exception as e: - logger.error(f"[Activity] run_fraud_analysis FAILED: {type(e).__name__}: {e}", exc_info=True) - raise - logger.info(f"[Activity] run_fraud_analysis completed, risk_score={result.get('overall_risk_score')}") - return result - + assessment = json.loads(assessment_text) + alert_id = assessment.get("alert_id", "unknown") + risk_score = assessment.get("overall_risk_score", 0) + recommended = assessment.get("recommended_action", "unknown") + except (json.JSONDecodeError, AttributeError): + alert_id = "unknown" + risk_score = 0 + recommended = "unknown" -def notify_analyst(context: ActivityContext, assessment_dict: dict) -> str: - """ - Activity to notify analyst for review. - - In production, this would: - - Send email/Slack notification - - Create ticket in ITSM system - - Push to analyst dashboard - """ - alert_id = assessment_dict.get("alert_id") - risk_score = assessment_dict.get("overall_risk_score", 0) - recommended_action = assessment_dict.get("recommended_action", "unknown") - logger.info(f"[Activity] NOTIFICATION: Analyst review required for alert {alert_id}") - logger.info(f"[Activity] Risk Score: {risk_score:.2f}, Recommended: {recommended_action}") - logger.info(f"[Activity] Reasoning: {assessment_dict.get('reasoning', 'N/A')}") - - # In production: send notification via email, Slack, etc. + logger.info(f"[Activity] Risk Score: {risk_score}, Recommended: {recommended}") + return f"Analyst notified for alert {alert_id}" def execute_fraud_action(context: ActivityContext, decision_dict: dict) -> dict: - """ - Activity to execute the approved fraud action. - - In production, this would: - - Lock account via API - - Process refunds - - Update fraud database - """ - alert_id = decision_dict.get("alert_id") + """Activity to execute the approved fraud action.""" + alert_id = decision_dict.get("alert_id", "unknown") action = decision_dict.get("approved_action", "unknown") analyst_id = decision_dict.get("analyst_id", "unknown") - + logger.info(f"[Activity] Executing fraud action: {action} for alert {alert_id}") logger.info(f"[Activity] Approved by analyst: {analyst_id}") - - # In production: execute actual action - success = True - details = f"Action '{action}' executed successfully" - + if action == "lock_account": logger.info(f"[Activity] 🔒 Account locked for alert {alert_id}") elif action == "refund_charges": @@ -248,42 +300,45 @@ def execute_fraud_action(context: ActivityContext, decision_dict: dict) -> dict: logger.info(f"[Activity] 🔒💰 Account locked and charges refunded for alert {alert_id}") elif action == "clear": logger.info(f"[Activity] ✅ Alert cleared for {alert_id}") - + return ActionResult( alert_id=alert_id, action_taken=action, - success=success, - details=details, + success=True, + details=f"Action '{action}' executed successfully", ).model_dump() -def auto_clear_alert(context: ActivityContext, assessment_dict: dict) -> dict: - """ - Activity to auto-clear low-risk alerts. - """ - alert_id = assessment_dict.get("alert_id") - risk_score = assessment_dict.get("overall_risk_score", 0) - - logger.info(f"[Activity] Auto-clearing low-risk alert {alert_id} (risk={risk_score:.2f})") - +def auto_clear_alert(context: ActivityContext, assessment_text: str) -> dict: + """Activity to auto-clear low-risk alerts.""" + try: + assessment = json.loads(assessment_text) + alert_id = assessment.get("alert_id", "unknown") + risk_score = assessment.get("overall_risk_score", 0) + except (json.JSONDecodeError, AttributeError): + alert_id = "unknown" + risk_score = 0 + + logger.info(f"[Activity] Auto-clearing low-risk alert {alert_id} (risk={risk_score})") + return ActionResult( alert_id=alert_id, action_taken="auto_clear", success=True, - details=f"Alert auto-cleared due to low risk score ({risk_score:.2f})", + details=f"Alert auto-cleared due to low risk score ({risk_score})", ).model_dump() -def escalate_timeout(context: ActivityContext, assessment_dict: dict) -> dict: - """ - Activity to escalate when analyst review times out. - """ - alert_id = assessment_dict.get("alert_id") - +def escalate_timeout(context: ActivityContext, assessment_text: str) -> dict: + """Activity to escalate when analyst review times out.""" + try: + assessment = json.loads(assessment_text) + alert_id = assessment.get("alert_id", "unknown") + except (json.JSONDecodeError, AttributeError): + alert_id = "unknown" + logger.warning(f"[Activity] ⚠️ ESCALATION: Analyst review timed out for alert {alert_id}") - logger.warning(f"[Activity] Escalating to fraud manager...") - - # In production: escalate to manager, auto-lock account, etc. + return ActionResult( alert_id=alert_id, action_taken="escalate_timeout", @@ -293,184 +348,272 @@ def escalate_timeout(context: ActivityContext, assessment_dict: dict) -> dict: def send_notification(context: ActivityContext, result_dict: dict) -> str: - """ - Activity to send final notification. - """ - alert_id = result_dict.get("alert_id") + """Activity to send final notification.""" + alert_id = result_dict.get("alert_id", "unknown") action_taken = result_dict.get("action_taken", "unknown") - + logger.info(f"[Activity] Sending final notification for alert {alert_id}") logger.info(f"[Activity] Action taken: {action_taken}") - - # In production: send customer notification, update audit log + return f"Notification sent for alert {alert_id}, action: {action_taken}" # ============================================================================ -# Main Orchestration +# Main Orchestration (uses DurableAIAgentOrchestrationContext) # ============================================================================ def fraud_detection_orchestration( context: OrchestrationContext, - payload_raw: Any + payload_raw: Any, ) -> Generator[Task[Any], Any, dict]: - """ - Main Durable Task orchestration for fraud detection. - - This orchestration: - 1. Runs the inner workflow (fan-out → aggregate) as an activity - 2. Routes based on risk score (simple if/else) - 3. For high risk: waits for analyst decision with timeout - 4. Executes approved action or auto-clears - 5. Sends final notification - - Args: - context: The orchestration context - payload_raw: The input payload (alert data) - - Returns: - dict: Final result with status and action taken + """Main DTS orchestration for fraud detection with stateful HITL feedback loop. + + This orchestration demonstrates: + 1. DurableAIAgentOrchestrationContext for type-safe agent calls + 2. DurableAgentState for persistent conversation history across iterations + 3. Stateful feedback loop: analyst reject -> re-investigation with full context + 4. External events (HITL), timeouts, activity functions + + Flow: + 1. Call FraudAnalysisAgent entity (inner workflow: fan-out -> aggregate) + 2. Route based on risk score + 3. HIGH RISK: notify analyst -> wait for decision + - Approve -> execute action -> done + - Reject + feedback -> re-investigate (same session = stateful) -> loop + - Timeout -> escalate + 4. LOW RISK: auto-clear + 5. Send notification """ logger.info("[Orchestration] Starting fraud detection orchestration") - + # Validate input if not isinstance(payload_raw, dict): raise ValueError("Alert data is required") - try: payload = FraudDetectionInput.model_validate(payload_raw) except ValidationError as exc: raise ValueError(f"Invalid alert input: {exc}") from exc - + alert_id = payload.alert_id logger.info(f"[Orchestration] Processing alert {alert_id}") - - context.set_custom_status(json.dumps({ - "message": f"Running fraud analysis for {alert_id}", - "step_details": {}, - "risk_score": None, - })) - + + # ======================================================================== + # Set up DurableAIAgentOrchestrationContext for agent entity calls + # ======================================================================== + + agent_ctx = DurableAIAgentOrchestrationContext(context) + fraud_agent = agent_ctx.get_agent(FRAUD_AGENT_NAME) + fraud_session = fraud_agent.create_session() + + logger.info(f"[Orchestration] Created agent session: {fraud_session.session_id}") + # ======================================================================== - # Step 1: Run the inner workflow (fan-out → aggregate) + # Step 1: Initial fraud analysis via DurableAIAgent entity # ======================================================================== - - logger.info("[Orchestration] Step 1: Running fraud analysis workflow...") - - alert_dict = { + + alert_json = json.dumps({ "alert_id": payload.alert_id, "customer_id": payload.customer_id, "alert_type": payload.alert_type, "description": payload.description, "timestamp": payload.timestamp, "severity": payload.severity, - } - - assessment_task: Task[dict] = context.call_activity("run_fraud_analysis", input=alert_dict) - assessment: dict = yield assessment_task - + }) + + context.set_custom_status(json.dumps({ + "message": f"Running fraud analysis for {alert_id}", + "step_details": {}, + "risk_score": None, + })) + + logger.info("[Orchestration] Step 1: Running fraud analysis via agent entity...") + + response: AgentResponse = yield fraud_agent.run( + messages=alert_json, + session=fraud_session, + ) + + # Parse assessment from agent response + assessment = json.loads(response.text) risk_score = assessment.get("overall_risk_score", 0) - recommended_action = assessment.get("recommended_action", "unknown") step_details = assessment.get("step_details", {}) - - logger.info(f"[Orchestration] Analysis complete: risk={risk_score:.2f}, recommended={recommended_action}") - + + logger.info(f"[Orchestration] Analysis complete: risk={risk_score:.2f}") + # ======================================================================== - # Step 2: Route based on risk score + # Step 2: Route based on risk score with HITL feedback loop # ======================================================================== - + result: dict - + attempt = 0 + if risk_score >= 0.6: - # HIGH RISK - Human-in-the-loop - logger.info(f"[Orchestration] HIGH RISK ({risk_score:.2f}) - Requiring analyst review") - context.set_custom_status(json.dumps({ - "message": f"Awaiting analyst review (risk={risk_score:.2f})", - "step_details": step_details, - "risk_score": risk_score, - })) - - # Notify analyst - yield context.call_activity("notify_analyst", input=assessment) - - # Wait for analyst decision OR timeout - approval_task: Task[Any] = context.wait_for_external_event(ANALYST_APPROVAL_EVENT) - timeout_task: Task[Any] = context.create_timer( - context.current_utc_datetime + timedelta(hours=payload.approval_timeout_hours) - ) - - logger.info(f"[Orchestration] Waiting for analyst decision (timeout: {payload.approval_timeout_hours}h)") - - winner_task = yield when_any([approval_task, timeout_task]) - - if winner_task == approval_task: - # Analyst responded - decision_data: Any = approval_task.get_result() - logger.info(f"[Orchestration] Received analyst decision: {decision_data}") - - # Parse decision - if isinstance(decision_data, dict): - decision = AnalystDecision.model_validate(decision_data) - else: - # Handle string or other formats - decision = AnalystDecision( - alert_id=alert_id, - approved_action=str(decision_data), - analyst_notes="", - analyst_id="unknown", - ) - - # Update step_details with review_gateway completion - step_details["review_gateway"] = { - "status": "completed", - "tool_calls": [{ - "name": "analyst_decision", - "arguments": {"action": decision.approved_action}, - "result": f"Approved by {decision.analyst_id}: {decision.analyst_notes or 'No notes'}" - }], - "output": f"Action approved: {decision.approved_action}", - } - - context.set_custom_status(json.dumps({ - "message": "Executing analyst-approved action", - "step_details": step_details, - "risk_score": risk_score, - })) - - # Execute the approved action - action_result: dict = yield context.call_activity( - "execute_fraud_action", - input=decision.model_dump() + # HIGH RISK - Human-in-the-loop with feedback loop + while attempt < payload.max_review_attempts: + attempt += 1 + logger.info( + f"[Orchestration] HIGH RISK ({risk_score:.2f}) - " + f"Analyst review (attempt {attempt}/{payload.max_review_attempts})" ) - result = action_result - - # Update step_details with fraud_action_executor completion - step_details["fraud_action_executor"] = { - "status": "completed", - "tool_calls": [{ - "name": "execute_fraud_action", - "arguments": {"action": decision.approved_action, "alert_id": alert_id}, - "result": f"Action executed: {result.get('action_taken', 'unknown')}" - }], - "output": result.get("details", f"Executed action: {decision.approved_action}"), - } - - else: - # Timeout - escalate - logger.warning(f"[Orchestration] Analyst review timed out after {payload.approval_timeout_hours}h") + context.set_custom_status(json.dumps({ - "message": "Review timed out - escalating", + "message": f"Awaiting analyst review (risk={risk_score:.2f}, attempt {attempt})", "step_details": step_details, "risk_score": risk_score, })) - - escalation_result: dict = yield context.call_activity( - "escalate_timeout", - input=assessment + + # Notify analyst + yield context.call_activity("notify_analyst", input=response.text) + + # Wait for analyst decision OR timeout + approval_task: Task[Any] = context.wait_for_external_event(ANALYST_APPROVAL_EVENT) + timeout_task: Task[Any] = context.create_timer( + context.current_utc_datetime + timedelta(hours=payload.approval_timeout_hours) ) - result = escalation_result - + + logger.info(f"[Orchestration] Waiting for analyst decision (timeout: {payload.approval_timeout_hours}h)") + winner_task = yield when_any([approval_task, timeout_task]) + + if winner_task == approval_task: + # Analyst responded + decision_data: Any = approval_task.get_result() + logger.info(f"[Orchestration] Received analyst decision: {decision_data}") + + # Parse decision + if isinstance(decision_data, dict): + decision = AnalystDecision.model_validate(decision_data) + else: + decision = AnalystDecision( + alert_id=alert_id, + approved=True, + approved_action=str(decision_data), + ) + + if decision.approved: + # ==================================================== + # APPROVED: Execute the action + # ==================================================== + logger.info(f"[Orchestration] ✅ Analyst approved: {decision.approved_action}") + + step_details["review_gateway"] = { + "status": "completed", + "tool_calls": [{ + "name": "analyst_decision", + "arguments": {"action": decision.approved_action}, + "result": f"Approved by {decision.analyst_id}" + }], + "output": f"Action approved: {decision.approved_action}", + } + + context.set_custom_status(json.dumps({ + "message": "Executing analyst-approved action", + "step_details": step_details, + "risk_score": risk_score, + })) + + action_result: dict = yield context.call_activity( + "execute_fraud_action", + input=decision.model_dump(), + ) + result = action_result + + step_details["fraud_action_executor"] = { + "status": "completed", + "tool_calls": [{ + "name": "execute_fraud_action", + "arguments": {"action": decision.approved_action}, + "result": f"Executed: {result.get('action_taken')}" + }], + "output": result.get("details", "Action executed"), + } + break # Exit the HITL loop + + else: + # ==================================================== + # REJECTED: Re-investigate with feedback (SAME SESSION) + # ==================================================== + feedback = decision.feedback or decision.analyst_notes or "No specific feedback" + logger.info(f"[Orchestration] ❌ Analyst rejected. Feedback: {feedback}") + + if attempt >= payload.max_review_attempts: + logger.warning("[Orchestration] Max review attempts exhausted") + step_details["review_gateway"] = { + "status": "completed", + "output": f"Max attempts ({payload.max_review_attempts}) exhausted", + } + result = ActionResult( + alert_id=alert_id, + action_taken="max_attempts_exhausted", + success=False, + details=f"Could not resolve after {payload.max_review_attempts} reviews", + ).model_dump() + break + + step_details["review_gateway"] = { + "status": "rejected", + "tool_calls": [{ + "name": "analyst_decision", + "arguments": {"feedback": feedback}, + "result": f"Rejected by {decision.analyst_id}: {feedback}" + }], + "output": f"Rejected - re-investigating (attempt {attempt + 1})", + } + + context.set_custom_status(json.dumps({ + "message": f"Re-investigating with analyst feedback (attempt {attempt + 1})", + "step_details": step_details, + "risk_score": risk_score, + })) + + # *** STATEFUL RE-INVESTIGATION *** + # Same session -> entity retains full conversation history + # Agent sees: original alert + first analysis + feedback + feedback_msg = ( + f"The analyst REJECTED the previous assessment and requests re-investigation.\n" + f"Analyst feedback: {feedback}\n" + f"Please conduct a deeper investigation focusing on the analyst's concerns." + ) + + logger.info("[Orchestration] Re-running fraud analysis with feedback context...") + + response = yield fraud_agent.run( + messages=feedback_msg, + session=fraud_session, # SAME SESSION = stateful! + ) + + # Parse updated assessment + assessment = json.loads(response.text) + risk_score = assessment.get("overall_risk_score", 0) + step_details = assessment.get("step_details", {}) + + logger.info(f"[Orchestration] Re-investigation complete: risk={risk_score:.2f}") + continue # Loop back for another review + + else: + # Timeout - escalate + logger.warning("[Orchestration] Analyst review timed out") + context.set_custom_status(json.dumps({ + "message": "Review timed out - escalating", + "step_details": step_details, + "risk_score": risk_score, + })) + + escalation_result: dict = yield context.call_activity( + "escalate_timeout", input=response.text + ) + result = escalation_result + break + + else: + # while loop exhausted without break (safety fallback) + result = ActionResult( + alert_id=alert_id, + action_taken="max_attempts_exhausted", + success=False, + details="Review loop exhausted", + ).model_dump() + else: # LOW RISK - Auto-clear logger.info(f"[Orchestration] LOW RISK ({risk_score:.2f}) - Auto-clearing") @@ -479,62 +622,48 @@ def fraud_detection_orchestration( "step_details": step_details, "risk_score": risk_score, })) - - clear_result: dict = yield context.call_activity("auto_clear_alert", input=assessment) + + clear_result: dict = yield context.call_activity( + "auto_clear_alert", input=response.text + ) result = clear_result - - # Update step_details with auto_clear_executor completion + step_details["auto_clear_executor"] = { "status": "completed", - "tool_calls": [{ - "name": "auto_clear_alert", - "arguments": {"alert_id": alert_id, "risk_score": risk_score}, - "result": f"Alert auto-cleared (low risk: {risk_score:.2f})" - }], - "output": result.get("details", "Alert automatically cleared due to low risk score"), + "output": result.get("details", "Auto-cleared"), } - + # ======================================================================== # Step 3: Send final notification # ======================================================================== - + logger.info("[Orchestration] Step 3: Sending final notification") context.set_custom_status(json.dumps({ "message": "Sending notification", "step_details": step_details, "risk_score": risk_score, })) - + yield context.call_activity("send_notification", input=result) - - # Update step_details with final_notification_executor completion - step_details["final_notification_executor"] = { - "status": "completed", - "tool_calls": [{ - "name": "send_notification", - "arguments": {"alert_id": alert_id, "action_taken": result.get("action_taken")}, - "result": "Notification sent to customer and internal teams" - }], - "output": f"Notification sent for alert {alert_id}", - } - + # ======================================================================== # Complete # ======================================================================== - + logger.info(f"[Orchestration] ✅ Fraud detection completed for alert {alert_id}") context.set_custom_status(json.dumps({ "message": "Completed", "step_details": step_details, "risk_score": risk_score, })) - + return { "alert_id": alert_id, "status": "completed", "risk_score": risk_score, "action_taken": result.get("action_taken"), "success": result.get("success"), + "review_attempts": attempt, "step_details": step_details, } @@ -551,13 +680,12 @@ def get_worker( """Create a configured DurableTaskSchedulerWorker.""" taskhub_name = taskhub or os.getenv("DTS_TASKHUB", "default") endpoint_url = endpoint or os.getenv("DTS_ENDPOINT", "http://localhost:8080") - + logger.info(f"Using DTS endpoint: {endpoint_url}") logger.info(f"Using taskhub: {taskhub_name}") - - # Use credentials for Azure-hosted DTS, None for local emulator + credential = None if endpoint_url.startswith("http://localhost") else DefaultAzureCredential() - + return DurableTaskSchedulerWorker( host_address=endpoint_url, secure_channel=not endpoint_url.startswith("http://localhost"), @@ -566,62 +694,85 @@ def get_worker( ) -def setup_worker(worker: DurableTaskSchedulerWorker) -> None: - """Set up the worker with orchestrations and activities.""" - +def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker: + """Set up the worker with agents, orchestrations, and activities. + + Returns: + DurableAIAgentWorker with agents registered + """ + # Wrap raw worker with DurableAIAgentWorker for agent entity registration + agent_worker = DurableAIAgentWorker(worker) + + # Register FraudAnalysisAgent as a durable entity (dafx-FraudAnalysisAgent) + logger.info("Registering FraudAnalysisAgent as durable entity...") + fraud_agent = FraudAnalysisAgent() + agent_worker.add_agent(fraud_agent) + logger.info(f"✓ Registered entity: dafx-{fraud_agent.name}") + + # Register activity functions on the raw worker logger.info("Registering activities...") - worker.add_activity(run_fraud_analysis) worker.add_activity(notify_analyst) worker.add_activity(execute_fraud_action) worker.add_activity(auto_clear_alert) worker.add_activity(escalate_timeout) worker.add_activity(send_notification) logger.info("✓ Activities registered") - + + # Register the orchestration on the raw worker logger.info("Registering orchestration...") worker.add_orchestrator(fraud_detection_orchestration) logger.info("✓ Orchestration registered") + return agent_worker + async def main(): """Main entry point for the worker process.""" - global _main_loop - _main_loop = asyncio.get_running_loop() - - logger.info("="*60) + logger.info("=" * 60) logger.info("Starting Durable Fraud Detection Worker") - logger.info("="*60) - - # Pre-initialize resources - logger.info("Initializing resources...") - try: - await _ensure_resources() - except Exception as e: - logger.error(f"Failed to initialize resources: {e}") - logger.error("Make sure MCP server is running and Azure OpenAI is configured") - return - + logger.info(" Architecture: DurableAIAgentWorker + Workflow + HITL") + logger.info("=" * 60) + # Create and setup worker worker = get_worker() - setup_worker(worker) - + agent_worker = setup_worker(worker) + logger.info("") + logger.info(f"Registered agents: {agent_worker.registered_agent_names}") logger.info("Worker is ready and listening for orchestrations!") logger.info("Dashboard: http://localhost:8082") logger.info("Press Ctrl+C to stop.") logger.info("") - + try: worker.start() - - # Keep running - while True: - await asyncio.sleep(1) + + # Keep running — use threading.Event for Windows compatibility. + # asyncio.sleep() can be cancelled by gRPC's SIGINT handler on Windows, + # so we fall back to a thread-safe wait. + import threading + stop_event = threading.Event() + try: + while not stop_event.wait(timeout=1): + pass + except (KeyboardInterrupt, asyncio.CancelledError): + pass except KeyboardInterrupt: logger.info("Worker shutdown initiated") - + finally: + # Gracefully stop the worker to tear down gRPC channels. + # Without this, the gRPC C-extension thread can keep the process + # alive indefinitely even after the Python runtime shuts down. + try: + worker.stop() + except Exception: + pass + logger.info("Worker stopped") if __name__ == "__main__": - asyncio.run(main()) + try: + asyncio.run(main()) + except KeyboardInterrupt: + pass diff --git a/mcp/mcp_service.py b/mcp/mcp_service.py index ae5052aab..53fb89bf1 100644 --- a/mcp/mcp_service.py +++ b/mcp/mcp_service.py @@ -416,17 +416,17 @@ async def _protected_resource_metadata(request: Request): # TOOL ENDPOINTS # ############################################################################## @mcp.tool(description="List all customers with basic info") -async def get_all_customers() -> List[CustomerSummary]: +async def get_all_customers() -> list: data = await get_all_customers_async() - return [CustomerSummary(**r) for r in data] + return data @mcp.tool(description="Get a full customer profile including their subscriptions") async def get_customer_detail( customer_id: Annotated[int, "Customer identifier value"], -) -> CustomerDetail: +) -> dict: data = await get_customer_detail_async(customer_id) - return CustomerDetail(**data) + return data @mcp.tool( @@ -436,26 +436,17 @@ async def get_customer_detail( ) async def get_subscription_detail( subscription_id: Annotated[int, "Subscription identifier value"], -) -> SubscriptionDetail: +) -> dict: data = await get_subscription_detail_async(subscription_id) - - # Convert nested data to Pydantic models - invoices = [] - for inv_data in data['invoices']: - payments = [Payment(**p) for p in inv_data['payments']] - invoices.append(Invoice(**{**inv_data, 'payments': payments})) - - service_incidents = [ServiceIncident(**si) for si in data['service_incidents']] - - return SubscriptionDetail(**{**data, 'invoices': invoices, 'service_incidents': service_incidents}) + return data @mcp.tool(description="Return invoice‑level payments list") async def get_invoice_payments( invoice_id: Annotated[int, "Invoice identifier value"], -) -> List[Payment]: +) -> list: data = await get_invoice_payments_async(invoice_id) - return [Payment(**r) for r in data] + return data @mcp.tool(description="Record a payment for a given invoice and get new outstanding balance") @@ -473,17 +464,15 @@ async def get_data_usage( start_date: Annotated[str, "Inclusive start date (YYYY-MM-DD)"], end_date: Annotated[str, "Inclusive end date (YYYY-MM-DD)"], aggregate: Annotated[bool, "Set to true for aggregate statistics"] = False, -) -> List[DataUsageRecord] | Dict[str, Any]: +) -> list | Dict[str, Any]: result = await get_data_usage_async(subscription_id, start_date, end_date, aggregate) - if aggregate: - return result - return [DataUsageRecord(**r) for r in result] + return result @mcp.tool(description="List every active promotion (no filtering)") -async def get_promotions() -> List[Promotion]: +async def get_promotions() -> list: data = await get_promotions_async() - return [Promotion(**r) for r in data] + return data @mcp.tool( @@ -492,9 +481,9 @@ async def get_promotions() -> List[Promotion]: ) async def get_eligible_promotions( customer_id: Annotated[int, "Customer identifier value"], -) -> List[Promotion]: +) -> list: data = await get_eligible_promotions_async(customer_id) - return [Promotion(**r) for r in data] + return data # ─── Knowledge Base Search ─────────────────────────────────────────────── @@ -502,27 +491,27 @@ async def get_eligible_promotions( async def search_knowledge_base( query: Annotated[str, "Natural language query"], topk: Annotated[int, "Number of top documents to return"] = 3, -) -> List[KBDoc]: +) -> list: data = await search_knowledge_base_async(query, topk) - return [KBDoc(**r) for r in data] + return data # ─── Security Logs ─────────────────────────────────────────────────────── @mcp.tool(description="Security events for a customer (newest first)") async def get_security_logs( customer_id: Annotated[int, "Customer identifier value"], -) -> List[SecurityLog]: +) -> list: data = await get_security_logs_async(customer_id) - return [SecurityLog(**r) for r in data] + return data # ─── Orders ────────────────────────────────────────────────────────────── @mcp.tool(description="All orders placed by a customer") async def get_customer_orders( customer_id: Annotated[int, "Customer identifier value"], -) -> List[Order]: +) -> list: data = await get_customer_orders_async(customer_id) - return [Order(**r) for r in data] + return data # ─── Support Tickets ──────────────────────────────────────────────────── @@ -530,9 +519,9 @@ async def get_customer_orders( async def get_support_tickets( customer_id: Annotated[int, "Customer identifier value"], open_only: Annotated[bool, "Filter to open tickets"] = False, -) -> List[SupportTicket]: +) -> list: data = await get_support_tickets_async(customer_id, open_only) - return [SupportTicket(**r) for r in data] + return data @mcp.tool(description="Create a new support ticket for a customer") @@ -543,9 +532,9 @@ async def create_support_ticket( priority: Annotated[str, "Ticket priority"], subject: Annotated[str, "Ticket subject"], description: Annotated[str, "Ticket description"], -) -> SupportTicket: +) -> dict: data = await create_support_ticket_async(customer_id, subscription_id, category, priority, subject, description) - return SupportTicket(**data) + return data # ─── Products ──────────────────────────────────────────────────────────── @@ -560,17 +549,17 @@ class Product(BaseModel): @mcp.tool(description="List / search available products (optional category filter)") async def get_products( category: Annotated[Optional[str], "Optional category filter"] = None, -) -> List[Product]: +) -> list: data = await get_products_async(category) - return [Product(**r) for r in data] + return data @mcp.tool(description="Return a single product by ID") async def get_product_detail( product_id: Annotated[int, "Product identifier value"], -) -> Product: +) -> dict: data = await get_product_detail_async(product_id) - return Product(**data) + return data # ─── Update Subscription ──────────────────────────────────────────────── From ee3f9f280d083e7c232b426f174293364b2cfe48 Mon Sep 17 00:00:00 2001 From: "James N." Date: Wed, 18 Feb 2026 19:48:12 -0800 Subject: [PATCH 03/37] update readme --- .../fraud_detection_durable/README.md | 385 ++++++++++++------ 1 file changed, 261 insertions(+), 124 deletions(-) diff --git a/agentic_ai/workflow/fraud_detection_durable/README.md b/agentic_ai/workflow/fraud_detection_durable/README.md index fb19b7f53..c64a376fd 100644 --- a/agentic_ai/workflow/fraud_detection_durable/README.md +++ b/agentic_ai/workflow/fraud_detection_durable/README.md @@ -1,28 +1,191 @@ -# Ambient Fraud Detection — Durable Agentic Workflow +# Durable Agentic Workflows — A Design Pattern for Long-Running AI Agent Orchestration -A production-grade reference architecture for **ambient, long-running, durable, fail-over capable AI agent workflows** with human-in-the-loop (HITL) decision gates. +## The Problem: AI Agents That Must Not Forget -This demo implements the complete lifecycle of a background (ambient) agent system: continuous telemetry monitoring → anomaly detection → multi-agent investigation → human approval → action execution — all with crash recovery, persistent state, and real-time UI visualization. +Most AI agent demos run in a single request-response cycle: user asks, agent thinks, agent answers. But real-world agent systems need to do things that take **minutes, hours, or days** — and they must survive failures along the way. -> 📐 **Taking this to production?** See [PRODUCTION_ARCHITECTURE.md](PRODUCTION_ARCHITECTURE.md) for the Azure Container Apps deployment topology, security model, and scaling characteristics. +Consider what happens when an AI agent needs to: + +- **Monitor transactions 24/7 for fraud** — continuously ingest telemetry events, detect anomalies in real time, autonomously launch multi-agent investigations, then wait for a human analyst to approve or reject the recommended action (which might take hours or happen over a weekend). This is an *ambient agent* — it runs in the background with no user prompt, watching for patterns that require intervention +- **Manage an IT incident** — detect anomaly, triage severity, page on-call engineer, wait for acknowledgment (with escalation if no response in 15 minutes), coordinate remediation steps, produce post-mortem +- **Process a loan application** — pull credit reports, verify employment, run compliance checks in parallel, wait for underwriter approval, then execute the disbursement +- **Orchestrate a supply chain order** — validate inventory across warehouses, reserve stock, calculate shipping, wait for supplier confirmation, handle partial fulfillments, retry failed shipments + +These scenarios share a common trait: **no human initiates the work**. Events arrive continuously — transactions, alerts, sensor readings, log entries — and the agent must watch, decide, act, and sometimes wait for human input before proceeding. This is fundamentally different from chatbot-style "user asks, agent answers" interactions. + +These are **long-running, multi-step, stateful workflows** where: + +1. **Human decisions inject unbounded delays** — an analyst might respond in 5 minutes or 5 days +2. **Multiple agents collaborate** — a router dispatches to specialists, an aggregator synthesizes results +3. **Failures are inevitable** — processes crash, containers restart, VMs get preempted, network connections drop +4. **Actions must not be repeated** — you can't lock a bank account twice or charge a customer twice because the worker restarted + +The fundamental question is: **when the process running your agent dies, what happens to all the work it already completed?** --- -## 🎯 Workshop Learning Objectives +## Why Building This Yourself Is Harder Than You Think + +The instinct is to reach for familiar tools: "I'll just checkpoint my state to a database or blob storage." This works for simple linear flows, but falls apart in real agent orchestration scenarios. Here are the five problems you'll need to solve — and each one is a distributed systems project in its own right. + +### 1. The Wait-for-Human Problem + +Your orchestration reaches a point where it needs a human decision. The agent has produced a risk assessment; now an analyst must approve or reject. + +**DIY approach:** Save current state to blob → poll a database for the decision → resume. + +**What goes wrong:** +- You need an event subscription system: when the analyst submits a decision via your API, something must correlate that decision back to the specific waiting orchestration instance +- You need a timeout mechanism: if no decision arrives in 72 hours, escalate automatically +- You need both to race: whichever fires first (human decision or timeout) wins, and the other must be cancelled cleanly +- All of this must survive process restarts — the timer can't live in `asyncio`; the event subscription can't live in a Python dict + +With a durable orchestrator, this is one line: + +```python +winner = yield when_any([ + context.wait_for_external_event("AnalystDecision"), + context.create_timer(timedelta(hours=72)) +]) +``` + +The event subscription, the timer, and the race — all persisted in the orchestrator's storage, not in worker memory. + +### 2. The Replay/Resume Problem + +Your workflow has 8 steps. It crashed after step 5. How do you resume at step 6? + +**DIY approach:** Save a `current_step` counter → on restart, load it → use a giant if/elif chain to jump to the right step. + +**What goes wrong:** +- Each step's *output* was consumed by later steps. You need to save every intermediate result, not just the step number +- If your workflow has branches (`if risk > 0.6: wait for human; else: auto-clear`), the if/elif chain grows exponentially +- If your workflow has loops (analyst rejects → re-investigate → wait again), the state machine becomes nearly impossible to maintain +- Every time you change the workflow logic, you must update the resume logic in lockstep + +With a durable orchestrator, there is no resume logic. The framework **replays your function from the beginning**, returning cached results for completed steps and suspending at the first incomplete step. Your code is the state machine. + +### 3. The Concurrency Problem + +Multiple worker instances are running (for scale). Two workers pick up the same orchestration after a restart. + +**DIY approach:** Distributed locks via Redis or blob leases → acquire before processing → release after. -After this workshop you will understand how to: +**What goes wrong:** +- Lock expiration vs. long-running work: if the LLM call takes 30 seconds and your lock expires at 15, another worker enters +- Deadlocks when one worker holds lock A and waits for lock B while another holds lock B and waits for lock A +- Partial completion: worker acquires lock, completes 3 of 5 steps, crashes — now you have half-done state and need rollback -1. **Design a 3-layer ambient agent architecture** (Detection → Investigation → Decision) -2. **Choose the right durability boundary** — what needs DTS checkpointing vs. what doesn't -3. **Implement human-in-the-loop** with durable external events that survive process restarts -4. **Build stateful feedback loops** — analyst rejects → agent re-investigates with full context -5. **Integrate MCP tools** for real-time data access within agent workflows -6. **Add observability** with OpenTelemetry + Application Insights -7. **Explain _why_ this architecture is truly durable** — not just buzzwords, but the concrete mechanics +A durable orchestrator manages concurrency internally via its event store — each orchestration instance has an atomic event stream with built-in sequencing. + +### 4. The Exactly-Once Side Effects Problem + +Step 5 is "lock the customer's bank account." The worker executed it, but crashed before recording that it succeeded. + +**DIY approach:** Idempotency keys for every side effect → check before executing → mark after. + +**What goes wrong:** +- You need an idempotency store separate from your state store (they must be updated atomically or you get inconsistencies) +- Every activity function must be wrapped with idempotency-key generation, lookup, and recording +- The pattern is different for every external system (REST API idempotency key vs. database upsert vs. message dedup) + +A durable orchestrator treats activities as **events in an append-only log**. If the worker crashes after the activity completes but before it records the result, the replay will see the completion event and skip re-execution automatically. + +### 5. The Stateful Agent Conversation Problem + +Your agent needs to re-investigate with full context: the original alert, its first analysis, the analyst's feedback. This conversation must persist across process restarts and even across multiple reject-reinvestigate cycles. + +**DIY approach:** Serialize the chat history to blob/database → load on each call → append → save. + +**What goes wrong:** +- Concurrent writes: two activities might try to update the same conversation simultaneously +- You need optimistic concurrency (ETags) or pessimistic locking +- Schema evolution: as your agent's message format changes, old serialized conversations must still deserialize +- Garbage collection: conversations that are complete should eventually be pruned + +A durable orchestrator with **entity state** (like DTS entities) handles all of this: atomic reads/writes, built-in concurrency control, and structured state that the framework manages. + +--- + +### The Honest Assessment + +| Concern | DIY Blob/DB Checkpointing | Durable Orchestrator (DTS) | +|---------|---------------------------|---------------------------| +| Simple linear pipeline, no human wait | ✅ Works fine, less infra | ⚠️ Overkill | +| Human-in-the-loop with timeout | 🔴 Build entire event system | ✅ `when_any([event, timer])` | +| Crash recovery with branches/loops | 🔴 Exponential state machine | ✅ Automatic replay | +| Multi-worker concurrency | 🔴 Distributed locks | ✅ Built-in event sequencing | +| Exactly-once side effects | 🔴 Idempotency infrastructure | ✅ Activity completion log | +| Persistent agent conversations | 🟡 Possible but manual | ✅ Entity state with concurrency | + +**Bottom line:** If your agent workflow is a straight-line script with no human waits, DIY checkpointing works. The moment you add human-in-the-loop, branching, loops, or multi-step crash recovery, you're building a workflow engine — and building a *correct* one is a multi-year distributed systems project. --- -## 🏗️ Architecture: The 3-Layer Pattern +## Enter: Azure Durable Task Scheduler (DTS) + +The [Azure Durable Task Scheduler](https://learn.microsoft.com/en-us/azure/durable-task-scheduler/) is a managed service that provides exactly the primitives needed for durable agent orchestration: + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#4A90D9', 'primaryTextColor': '#fff' }}}%% +flowchart LR + subgraph DTS_SCOPE["🔷 What DTS Provides"] + direction TB + DS1["📋 Persistent task queue"] + DS2["📝 Event store — append-only log"] + DS3["⏱️ Timer service — survives restarts"] + DS4["🗃️ Entity state storage"] + DS5["📦 Work item distribution"] + DS6["🔁 At-least-once delivery"] + end + + subgraph WORKER_SCOPE["🔧 What Your Code Provides"] + direction TB + WS1["🐍 Compute runtime — Python"] + WS2["🤖 LLM calls — Azure OpenAI"] + WS3["🔌 Tool calls — MCP, APIs"] + WS4["📊 Business logic — orchestration"] + WS5["🧩 Agent framework integration"] + end + + DTS_SCOPE ~~~ WORKER_SCOPE + + style DTS_SCOPE fill:#cce5ff,stroke:#004085,stroke-width:2px + style WORKER_SCOPE fill:#ffeeba,stroke:#ffc107,stroke-width:2px +``` + +**DTS is a persistent task queue + event store + timer service.** It doesn't run your code — it stores the *record of what happened* and *dispatches work items* to workers. Your worker is a **stateless compute runtime** that pulls tasks, executes your Python/LLM/MCP logic, and reports results back. If the worker dies, DTS still has the full event log; a new worker replays it and picks up where things left off. + +### Key Properties + +| Property | How It Works | +|----------|-------------| +| **Checkpointing** | Every `yield` in your orchestration writes a completion event to DTS's append-only log | +| **Crash recovery** | Worker replays the orchestration function; completed yields return cached results instantly | +| **Human-in-the-loop** | `wait_for_external_event()` creates a subscription in DTS storage — survives indefinitely | +| **Timers** | `create_timer(72h)` fires in DTS's timer service — not in your process memory | +| **Entity state** | Agent conversation history persisted as a structured entity, with atomic updates | +| **Scaling** | Multiple workers pull from the same task hub — DTS distributes work items | + +### Development Experience + +DTS ships a **local emulator** as a Docker container, so you develop and test locally without an Azure subscription: + +```bash +# Local development — zero cloud dependency +docker run -d --name dts-emulator -p 8080:8080 mcr.microsoft.com/dts/dts-emulator:latest + +# Production — same SDK, same code, just change the endpoint +DTS_ENDPOINT=https://your-dts.northcentralus.durabletask.io +``` + +--- + +## Reference Architecture: Ambient Fraud Detection + +To prove these patterns, we built a complete fraud detection system that exercises every durable orchestration primitive: fan-out/fan-in agents, human-in-the-loop with timeout, stateful feedback loops, and crash recovery. + +### The 3-Layer Design ```mermaid %%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#4A90D9', 'primaryTextColor': '#fff', 'lineColor': '#5C6B77' }}}%% @@ -110,17 +273,43 @@ flowchart TB ### Why This Layering? -| Layer | Uses LLM? | Durable? | Why? | -|-------|-----------|----------|------| +| Layer | Uses LLM? | Durable? | Rationale | +|-------|-----------|----------|-----------| | **Layer 1** — Detection | ❌ No | ❌ No | Events arrive every 2–5s. An LLM call takes 2–5s and costs money. Simple rules catch 95% of benign events at zero cost. | | **Layer 2** — Investigation | ✅ Yes | ✅ Yes (entity) | Complex multi-signal reasoning is the LLM's strength. Entity state persists the full conversation for re-investigation. | | **Layer 3** — Decision | ❌ No | ✅ Yes (orchestration) | Human decisions can take hours/days. DTS timers and external events survive crashes and restarts. | +### Service Topology + +The system runs as four independent processes communicating through DTS and HTTP: + +```mermaid +flowchart LR + BROWSER["🌐 Browser
(REST / SSE / WS)"] + BACKEND["⚙️ Backend
(FastAPI)"] + DTS["🔷 DTS
(gRPC SDK)"] + WORKER["🔧 Worker"] + MCP["🔌 MCP Server"] + + BROWSER -->|"HTTP"| BACKEND + BACKEND -->|"gRPC"| DTS + DTS -.->|"pull"| WORKER + WORKER -->|"HTTP"| MCP + + style BROWSER fill:#e8f4fd,stroke:#4A90D9 + style BACKEND fill:#d4edda,stroke:#28a745 + style DTS fill:#cce5ff,stroke:#004085 + style WORKER fill:#ffeeba,stroke:#ffc107 + style MCP fill:#d6d8db,stroke:#6c757d +``` + +The browser cannot call DTS's gRPC SDK directly. The FastAPI backend acts as a Backend-For-Frontend (BFF), translating REST/WebSocket/SSE into SDK calls. In production, swap `DTS_ENDPOINT` from `localhost:8080` to your Azure DTS endpoint — **zero code changes**. + --- -## 🔑 Key Patterns +## Key Design Patterns -### Pattern 1: Durability Boundaries — What Gets Checkpointed? +### Pattern 1: Durability Boundaries — What Gets Checkpointed Not everything needs to be durable. The key architectural insight is choosing **where** to draw the durability boundary: @@ -148,7 +337,7 @@ flowchart LR style FAST fill:#fff3cd,stroke:#856404,stroke-width:2px ``` -**Why not checkpoint every LLM call?** Adding DTS checkpoints to each LLM call would add ~200ms overhead per call and massively complicate the agent topology. The inner workflow runs in ~10–20s — fast enough that retry-on-failure is the right strategy. If the worker crashes mid-inner-workflow, the entity call simply retries from the beginning. +**Why not checkpoint every LLM call?** Adding DTS checkpoints to each LLM call would add ~200ms overhead per call and massively complicate the agent topology. The inner workflow runs in ~10–20 seconds — fast enough that retry-on-failure is the right strategy. If the worker crashes mid-inner-workflow, the entity call simply retries from the beginning. ### Pattern 2: Stateful Feedback Loop via Durable Entity @@ -182,7 +371,7 @@ sequenceDiagram E-->>O: AgentResponse (risk=0.72) ``` -The agent doesn't start from scratch — it sees the original alert, its first analysis, AND the analyst's feedback. This is what makes re-investigation meaningful. +The agent doesn't start from scratch — it sees the original alert, its first analysis, AND the analyst's feedback. This is what makes re-investigation meaningful rather than redundant. ### Pattern 3: Ambient Detection Without LLM Overhead @@ -201,34 +390,11 @@ if event.type == "transaction" and event.amount > 3 * customer_average: At 1 event every 2–5 seconds, an LLM call (2–5s each) can't keep up. Rules handle the 95% of benign events at zero cost. The LLM's value is in Layer 2, where it reasons about complex multi-signal patterns. -### Pattern 4: Backend-For-Frontend (BFF) for Durable Workflows - -The browser cannot call DTS's gRPC SDK directly. The FastAPI backend translates REST/WebSocket into SDK calls: - -```mermaid -flowchart LR - BROWSER["🌐 Browser
(REST / SSE / WS)"] - BACKEND["⚙️ Backend
(FastAPI)"] - DTS["🔷 DTS
(gRPC SDK)"] - WORKER["🔧 Worker"] - - BROWSER -->|"HTTP"| BACKEND - BACKEND -->|"gRPC"| DTS - DTS -.->|"pull"| WORKER - - style BROWSER fill:#e8f4fd,stroke:#4A90D9 - style BACKEND fill:#d4edda,stroke:#28a745 - style DTS fill:#cce5ff,stroke:#004085 - style WORKER fill:#ffeeba,stroke:#ffc107 -``` - -In production, swap `DTS_ENDPOINT` from `localhost:8080` to your Azure DTS endpoint. **Zero code changes.** See [PRODUCTION_ARCHITECTURE.md](PRODUCTION_ARCHITECTURE.md). - --- -## 🛡️ Why Is This Actually Durable? — Deep Dive +## Durability Mechanics — Deep Dive -This section explains the **concrete mechanics** that make this architecture truly durable, not just the claim. Understanding these internals is critical for the workshop. +This section explains the **concrete mechanics** that make the architecture truly durable — not just the claim, but how it actually works under the hood. ### The Durability Stack @@ -276,7 +442,7 @@ These aren't in-memory variables — they're **persisted facts** in DTS storage. ### 2. Replay, Not Restore — How Crash Recovery Works -When a worker restarts after a crash, it doesn't "load state" — it **replays the orchestration function from the beginning**, but with a twist: +When a worker restarts after a crash, it doesn't "load state" — it **replays the orchestration function from the beginning**, but with a critical twist: ```mermaid flowchart TB @@ -295,7 +461,7 @@ The generator function re-executes, but each `yield` that already completed **re ### 3. External Events Survive Process Death -The `wait_for_external_event("AnalystDecision")` call is especially powerful: +The `wait_for_external_event("AnalystDecision")` call is especially powerful — it creates a subscription that lives entirely in DTS storage: ```mermaid sequenceDiagram @@ -340,7 +506,7 @@ When the worker crashes, the only things lost are in-flight LLM/MCP calls — an ### 5. Entity State Persistence (`DurableAgentState`) -The `agent-framework` library stores the full agent conversation in a structured schema: +The agent-framework library stores the full agent conversation in a structured schema: ```mermaid %%{init: {'theme': 'base'}}%% @@ -365,79 +531,21 @@ erDiagram STATE ||--o{ ENTRY : "conversationHistory" ``` -Every time the entity processes a request, the cycle is: +Every time the entity processes a request: 1. **Load** state from DTS → `DurableAgentState` -2. **Append** user message (alert or feedback) -3. **Rebuild** full chat history from all entries (filtering out errors) +2. **Append** user message (alert or analyst feedback) +3. **Rebuild** full chat history from all entries 4. **Call** LLM with complete conversation 5. **Append** assistant response 6. **Persist** state back to DTS via `self.persist_state()` This is why the agent can re-investigate with full context — the conversation history is a **durable, append-only log** stored in DTS, not in worker memory. -### 6. What DTS Provides vs. What It Doesn't - -Understanding the **scope boundary** is critical: - -```mermaid -%%{init: {'theme': 'base'}}%% -flowchart LR - subgraph DTS_SCOPE["🔷 DTS Provides"] - direction TB - DS1["Persistent task queue"] - DS2["Event store · append-only log"] - DS3["Timer service"] - DS4["Entity state storage"] - DS5["Work item distribution"] - DS6["At-least-once delivery"] - end - - subgraph WORKER_SCOPE["🔧 Worker Provides"] - direction TB - WS1["Compute runtime · Python"] - WS2["LLM calls · Azure OpenAI"] - WS3["MCP tool calls · HTTP"] - WS4["Business logic · orchestration"] - WS5["Agent framework integration"] - end - - DTS_SCOPE ~~~ WORKER_SCOPE - - style DTS_SCOPE fill:#cce5ff,stroke:#004085,stroke-width:2px - style WORKER_SCOPE fill:#ffeeba,stroke:#ffc107,stroke-width:2px -``` - -DTS is a **persistent task queue + event store + timer service**. It doesn't run your code — it stores the _record of what happened_ and _dispatches work items_ to workers. The worker is a **stateless compute runtime** that pulls tasks, executes your Python/LLM/MCP logic, and reports results back. If the worker dies, DTS still has the full event log; a new worker replays it and picks up where things left off. - --- -## 📁 Project Structure - -``` -fraud_detection_durable/ -├── worker.py # DTS Worker: orchestration + agent entity + activities -├── backend.py # FastAPI BFF: REST API, WebSocket, SSE, event producer -├── event_producer.py # Layer 1: telemetry generation + anomaly detection -├── fraud_analysis_workflow.py # Inner workflow: fan-out → aggregate (Layer 2) -├── provision_dts.ps1 # Azure DTS provisioning script -├── .env # Configuration (Azure OpenAI, DTS, App Insights) -├── pyproject.toml # Dependencies -├── README.md # This file -├── PRODUCTION_ARCHITECTURE.md # Production deployment on Azure Container Apps -└── ui/ # React/Vite UI - ├── src/ - │ ├── App.jsx # Main app: WebSocket + SSE connections - │ └── components/ - │ ├── ControlPanel.jsx # Alert selector + start button - │ ├── WorkflowVisualizer.jsx # React Flow DAG visualization - │ ├── AnalystDecisionPanel.jsx # HITL approve/reject/feedback - │ └── EventFeed.jsx # Live telemetry feed (Layer 1) - └── package.json -``` - ---- +## Running the Demo -## 🚀 Quick Start +This section covers how to run the reference implementation locally to see the patterns in action. ### Prerequisites @@ -447,7 +555,7 @@ fraud_detection_durable/ - **Azure OpenAI** — with a deployed chat model - **MCP Server** — Contoso tools on port 8000 -### Service Startup +### Service Startup Order ```mermaid %%{init: {'theme': 'base'}}%% @@ -507,23 +615,29 @@ cd ui && npm install && npm run dev --- -## 🧪 Demo Scenarios +## Demo Scenarios ### Scenario 1: Ambient Detection → Auto-Clear -Watch the event feed — a multi-country login anomaly triggers automatic investigation. Low risk → auto-cleared. +Watch the event feed — a multi-country login anomaly triggers automatic investigation. The agent assesses low risk → auto-cleared without human involvement. + +**What it proves:** Layer 1 rule engine triggering Layer 2 durable orchestration, with Layer 3 auto-clear path. ### Scenario 2: Ambient Detection → HITL Approval -A spending spike triggers investigation. High risk → analyst reviews → approves "lock account". +A spending spike triggers investigation. Agent assesses high risk → analyst reviews in the UI → approves "lock account" → action executed. + +**What it proves:** Full 3-layer flow including human-in-the-loop via durable external events. ### Scenario 3: Reject → Stateful Re-investigation -Analyst rejects with feedback "check if VPN usage". Agent re-investigates with **full conversation history** (original alert + first analysis + analyst feedback). +Analyst rejects with feedback "check if VPN usage." Agent re-investigates with **full conversation history** (original alert + first analysis + analyst feedback) and produces a refined assessment. + +**What it proves:** Entity state persistence enables meaningful multi-turn investigation across the HITL feedback loop. -### Scenario 4: Kill & Recover (Durability Proof) 💥 +### Scenario 4: Kill & Recover — The Durability Proof 💥 -This is the most important demo — it proves the architecture isn't just theoretical: +This is the critical scenario — it proves the architecture delivers on its durability claims: ```mermaid sequenceDiagram @@ -555,15 +669,40 @@ sequenceDiagram Note over W2: Workflow completes normally! 🎉 ``` +**Steps:** 1. Start a high-risk workflow → reaches "Awaiting analyst review" 2. `taskkill /F /IM python.exe` — kill all Python processes 3. `uv run python worker.py` — restart the worker 4. Submit analyst decision via UI -5. **Workflow completes normally** — DTS replayed the orchestration from its event log ✅ +5. **Workflow completes normally** — DTS replayed the orchestration from its event log --- -## 🔧 Configuration +## Project Structure + +``` +fraud_detection_durable/ +├── worker.py # DTS Worker: orchestration + agent entity + activities +├── backend.py # FastAPI BFF: REST API, WebSocket, SSE, event producer +├── event_producer.py # Layer 1: telemetry generation + anomaly detection +├── fraud_analysis_workflow.py # Inner workflow: fan-out → aggregate (Layer 2) +├── provision_dts.ps1 # Azure DTS provisioning script +├── .env # Configuration (Azure OpenAI, DTS, App Insights) +├── pyproject.toml # Dependencies +├── README.md # This file +├── PRODUCTION_ARCHITECTURE.md # Production deployment on Azure Container Apps +└── ui/ # React/Vite UI + ├── src/ + │ ├── App.jsx # Main app: WebSocket + SSE connections + │ └── components/ + │ ├── ControlPanel.jsx # Alert selector + start button + │ ├── WorkflowVisualizer.jsx # React Flow DAG visualization + │ ├── AnalystDecisionPanel.jsx # HITL approve/reject/feedback + │ └── EventFeed.jsx # Live telemetry feed (Layer 1) + └── package.json +``` + +## Configuration | Variable | Description | Default | |----------|-------------|---------| @@ -578,11 +717,9 @@ sequenceDiagram | `EVENT_INTERVAL_SECONDS` | Seconds between telemetry events | `3` | | `BACKEND_OBSERVABILITY` | Enable Application Insights | `false` | ---- - -## 📐 Production Deployment +## Production Deployment -For Azure Container Apps deployment topology, security with Managed Identity, KEDA scaling, and cost estimation, see: +For Azure Container Apps deployment topology, Managed Identity security, KEDA scaling, and cost estimation, see: 👉 **[PRODUCTION_ARCHITECTURE.md](PRODUCTION_ARCHITECTURE.md)** From 391b2d3e293bb87d11123213040b0e8b27192f88 Mon Sep 17 00:00:00 2001 From: James Nguyen Date: Thu, 19 Feb 2026 10:28:06 -0800 Subject: [PATCH 04/37] Updating Durable Agent Implementation (#404) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme --------- Co-authored-by: James N. --- .vite/deps/_metadata.json | 8 + .vite/deps/package.json | 3 + agentic_ai/observability/setup.py | 22 +- .../workflow/fraud_detection/.env.sample | 12 - .../workflow/fraud_detection/ARCHITECTURE.md | 895 --- .../workflow/fraud_detection/DIAGRAMS.md | 420 -- .../fraud_detection/IMPLEMENTATION.md | 434 -- .../workflow/fraud_detection/QUICKSTART.md | 263 - agentic_ai/workflow/fraud_detection/README.md | 407 -- .../workflow/fraud_detection/backend.py | 1176 ---- .../fraud_detection_workflow.py | 1195 ---- .../workflow/fraud_detection/pyproject.toml | 25 - .../workflow/fraud_detection/scenario.md | 29 - .../fraud_detection/start_backend.bat | 17 - .../workflow/fraud_detection/ui/.dockerignore | 30 - .../workflow/fraud_detection/ui/.env.example | 6 - .../workflow/fraud_detection/ui/.eslintrc.cjs | 22 - 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a/.vite/deps/package.json b/.vite/deps/package.json new file mode 100644 index 000000000..3dbc1ca59 --- /dev/null +++ b/.vite/deps/package.json @@ -0,0 +1,3 @@ +{ + "type": "module" +} diff --git a/agentic_ai/observability/setup.py b/agentic_ai/observability/setup.py index e2cbe063c..6efe21bc4 100644 --- a/agentic_ai/observability/setup.py +++ b/agentic_ai/observability/setup.py @@ -75,6 +75,19 @@ def setup_observability( # Enable Agent Framework instrumentation enable_instrumentation(enable_sensitive_data=enable_sensitive_data) + # Workaround: agent_framework._tools.py calls + # model_dump_json(ensure_ascii=False), but Pydantic v2 doesn't + # support that kwarg, causing every tool call to fail with + # "Function failed". Monkeypatch BaseModel.model_dump_json to + # silently strip unsupported kwargs so observability doesn't + # break tool execution. Remove once the library is fixed. + import pydantic + _orig_mdj = pydantic.BaseModel.model_dump_json + def _safe_model_dump_json(self, **kwargs): + kwargs.pop("ensure_ascii", None) + return _orig_mdj(self, **kwargs) + pydantic.BaseModel.model_dump_json = _safe_model_dump_json # type: ignore[assignment] + _initialized = True print(f"✅ Application Insights observability enabled (service: {service_name})") logger.info(f"✅ Application Insights observability enabled (service: {service_name})") @@ -84,9 +97,12 @@ def setup_observability( print(f"❌ Observability dependencies not installed: {e}") logger.warning(f"Observability dependencies not installed: {e}") return False - except Exception as e: - print(f"❌ Failed to configure observability: {e}") - logger.warning(f"Failed to configure observability: {e}") + except BaseException as e: + # Catch ALL errors including KeyboardInterrupt from import deadlocks + # in azure-ai-projects telemetry instrumentor (openai SDK version conflicts). + # Observability is never worth crashing the service. + print(f"⚠️ Observability setup failed (non-fatal): {type(e).__name__}: {e}") + logger.warning(f"Observability setup failed (non-fatal): {type(e).__name__}: {e}") return False diff --git a/agentic_ai/workflow/fraud_detection/.env.sample b/agentic_ai/workflow/fraud_detection/.env.sample deleted file mode 100644 index 5d9f9deb8..000000000 --- a/agentic_ai/workflow/fraud_detection/.env.sample +++ /dev/null @@ -1,12 +0,0 @@ -# Azure OpenAI Configuration -AZURE_OPENAI_API_KEY=your-azure-openai-api-key-here -AZURE_OPENAI_CHAT_DEPLOYMENT=your-deployment-name -AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/ -AZURE_OPENAI_API_VERSION=2024-10-01-preview - -# MCP Server Configuration -MCP_SERVER_URI=http://localhost:8000/mcp - -# Application Insights (optional - enables telemetry) -# APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=... -# ENABLE_SENSITIVE_DATA=true diff --git a/agentic_ai/workflow/fraud_detection/ARCHITECTURE.md b/agentic_ai/workflow/fraud_detection/ARCHITECTURE.md deleted file mode 100644 index a7a1c6b9f..000000000 --- a/agentic_ai/workflow/fraud_detection/ARCHITECTURE.md +++ /dev/null @@ -1,895 +0,0 @@ -# Fraud Detection Workflow - End-to-End System Architecture - -## Overview - -The Fraud Detection Workflow is a real-time, multi-agent AI system that analyzes suspicious activities, performs risk assessments, and enables human-in-the-loop decision-making. The system combines Azure OpenAI agents, Model Context Protocol (MCP) tools, and a React-based visualization interface. - ---- - -## High-Level Architecture - -``` -┌─────────────────────────────────────────────────────────────────────────────┐ -│ USER INTERFACE LAYER │ -│ │ -│ ┌────────────────────────────────────────────────────────────────────┐ │ -│ │ React Frontend (Vite) │ │ -│ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │ │ -│ │ │ Workflow │ │ Analyst │ │ Event Log │ │ │ -│ │ │ Control │ │ Decision │ │ (Real-time Stream) │ │ │ -│ │ │ Panel │ │ Panel │ │ │ │ │ -│ │ └──────────────┘ └──────────────┘ └──────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ React Flow - Workflow Visualization │ │ │ -│ │ │ (Interactive DAG with real-time executor state updates) │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └────────────────────────────────────────────────────────────────────┘ │ -│ │ │ -│ HTTP REST API │ WebSocket (Event Stream) │ -└────────────────────────────────────┼────────────────────────────────────────┘ - │ -┌────────────────────────────────────┼────────────────────────────────────────┐ -│ APPLICATION LAYER │ -│ │ │ -│ ┌────────────────────────────────▼────────────────────────────────────┐ │ -│ │ FastAPI Backend Server │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ REST API Endpoints │ │ │ -│ │ │ • POST /api/workflow/start - Start new workflow │ │ │ -│ │ │ • POST /api/workflow/decision - Submit analyst decision │ │ │ -│ │ │ • GET /api/workflow/status - Get workflow status │ │ │ -│ │ │ • GET /api/alerts - Get sample alerts │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ WebSocket Manager │ │ │ -│ │ │ • Real-time event broadcasting to all connected clients │ │ │ -│ │ │ • Connection lifecycle management │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ Workflow Orchestration │ │ │ -│ │ │ • Workflow initialization & execution │ │ │ -│ │ │ • Event streaming & processing │ │ │ -│ │ │ • Human-in-the-loop coordination │ │ │ -│ │ │ • Checkpoint management for resume capability │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └──────────────────────────────────────────────────────────────────────┘ │ -│ │ │ -└────────────────────────────────────┼────────────────────────────────────────┘ - │ -┌────────────────────────────────────┼────────────────────────────────────────┐ -│ AGENT FRAMEWORK LAYER │ -│ │ │ -│ ┌────────────────────────────────▼────────────────────────────────────┐ │ -│ │ Microsoft Agent Framework (agent-framework) │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ Workflow Engine │ │ │ -│ │ │ • DAG-based execution (Directed Acyclic Graph) │ │ │ -│ │ │ • State management & transitions │ │ │ -│ │ │ • Checkpointing & resumability │ │ │ -│ │ │ • Event emission (ExecutorInvoked, ExecutorCompleted, etc.) │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ Executor Nodes (Agents) │ │ │ -│ │ │ │ │ │ -│ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ -│ │ │ │ Alert │────▶│ Usage │────▶│ Risk │ │ │ │ -│ │ │ │ Router │ │ Pattern │ │ Aggregator │ │ │ │ -│ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ -│ │ │ │ │ │ -│ │ │ ┌─────────────┐ ┌─────────────┐ ┌──────┐ │ │ │ -│ │ │ │ Location │────▶│ Analyst │───────────▶│ Auto │ │ │ │ -│ │ │ │ Analysis │ │ Review │ │ Clear│ │ │ │ -│ │ │ └─────────────┘ └─────────────┘ └──────┘ │ │ │ -│ │ │ (Human-in-Loop) │ │ │ │ -│ │ │ ┌─────────────┐ │ │ │ │ -│ │ │ │ Billing │───────────────────────────┬─────────┘ │ │ │ -│ │ │ │ Charge │ │ │ │ │ -│ │ │ └─────────────┘ ▼ │ │ │ -│ │ │ ┌─────────────┐ │ │ │ -│ │ │ │ Fraud │ │ │ │ -│ │ │ │ Action │ │ │ │ -│ │ │ └─────────────┘ │ │ │ -│ │ │ │ │ │ │ -│ │ │ ▼ │ │ │ -│ │ │ ┌─────────────┐ │ │ │ -│ │ │ │ Final │ │ │ │ -│ │ │ │ Notification│ │ │ │ -│ │ │ └─────────────┘ │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ RequestInfoExecutor (Human-in-the-Loop) │ │ │ -│ │ │ • Pauses workflow execution │ │ │ -│ │ │ • Creates checkpoint with current state │ │ │ -│ │ │ • Waits for external decision (analyst input) │ │ │ -│ │ │ • Resumes from checkpoint with decision │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └──────────────────────────────────────────────────────────────────────┘ │ -│ │ │ -└────────────────────────────────────┼────────────────────────────────────────┘ - │ -┌────────────────────────────────────┼────────────────────────────────────────┐ -│ AI & TOOLS LAYER │ -│ │ │ -│ ┌────────────────────────────────▼────────────────────────────────────┐ │ -│ │ Azure OpenAI Service │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ GPT-4 Model (Chat Completion) │ │ │ -│ │ │ • Agent reasoning & decision-making │ │ │ -│ │ │ • Natural language analysis │ │ │ -│ │ │ • Risk assessment generation │ │ │ -│ │ │ • Tool calling (function calling) │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └──────────────────────────────────────────────────────────────────────┘ │ -│ │ │ -│ ┌────────────────────────────────▼────────────────────────────────────┐ │ -│ │ Model Context Protocol (MCP) Server │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ FastMCP Server (HTTP/SSE) │ │ │ -│ │ │ • Exposes tools via standardized MCP protocol │ │ │ -│ │ │ • Handles tool invocation requests │ │ │ -│ │ │ • Returns structured tool responses │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ MCP Tools (Simulated Customer Service System) │ │ │ -│ │ │ │ │ │ -│ │ │ Customer Tools: Subscription Tools: │ │ │ -│ │ │ • get_all_customers() • get_subscription_detail()│ │ │ -│ │ │ • get_customer_detail() • update_subscription() │ │ │ -│ │ │ • get_customer_orders() • get_data_usage() │ │ │ -│ │ │ • unlock_account() │ │ │ -│ │ │ │ │ │ -│ │ │ Billing Tools: Support Tools: │ │ │ -│ │ │ • get_billing_summary() • get_support_tickets() │ │ │ -│ │ │ • pay_invoice() • create_support_ticket() │ │ │ -│ │ │ • get_invoice_payments() │ │ │ -│ │ │ │ │ │ -│ │ │ Knowledge Base: │ │ │ -│ │ │ • search_knowledge_base() - Semantic search on policies │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └──────────────────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────────────────┘ - │ -┌────────────────────────────────────┼────────────────────────────────────────┐ -│ DATA & STORAGE LAYER │ -│ │ │ -│ ┌────────────────────────────────▼────────────────────────────────────┐ │ -│ │ File-Based Storage │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ Checkpoint Storage (./checkpoints/) │ │ │ -│ │ │ • Workflow state snapshots │ │ │ -│ │ │ • Executor states at pause points │ │ │ -│ │ │ • Pending request information │ │ │ -│ │ │ • JSON format, UUID-based filenames │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ │ ┌──────────────────────────────────────────────────────────────┐ │ │ -│ │ │ Simulated Database (In-Memory) │ │ │ -│ │ │ • Customer records │ │ │ -│ │ │ • Subscription data │ │ │ -│ │ │ • Invoice & payment history │ │ │ -│ │ │ • Support tickets │ │ │ -│ │ │ • Security logs │ │ │ -│ │ │ (Generated with Faker library for demo purposes) │ │ │ -│ │ └──────────────────────────────────────────────────────────────┘ │ │ -│ └──────────────────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────────────────┘ -``` - ---- - -## Component Details - -### 1. Frontend Layer (React + Vite) - -**Technology Stack:** -- React 18+ with Hooks -- Material-UI (MUI) for components -- React Flow for workflow visualization -- WebSocket for real-time updates -- Vite for fast development builds - -**Key Components:** - -#### 1.1 Workflow Control Panel -- Displays available alerts (from backend) -- Alert selection dropdown with severity indicators -- "Start Workflow" button with loading state -- Shows active workflow status - -#### 1.2 Analyst Decision Panel -- Appears when `decision_required` event received -- Shows risk assessment & AI analysis -- Dropdown for decision selection -- Text area for analyst notes -- "Submit Decision" button -- Compact layout optimized for visibility - -#### 1.3 Workflow Graph Visualizer -- Interactive DAG visualization using React Flow -- Real-time executor state updates: - - `idle` - Gray, not yet executed - - `running` - Blue, currently executing - - `completed` - Green, finished successfully -- Shows workflow structure and data flow -- Zoom, pan, and fit-to-screen controls - -#### 1.4 Event Log Panel -- Real-time event stream display -- Color-coded event types -- Timestamps and executor tracking -- Auto-scroll to latest events -- Custom scrollbar with overflow handling -- Deduplication to prevent duplicate events - -**Communication Protocols:** -- REST API for control operations (start workflow, submit decisions) -- WebSocket for event streaming (bi-directional, persistent connection) - ---- - -### 2. Backend Layer (FastAPI) - -**Technology Stack:** -- FastAPI for async REST API -- WebSocket support for real-time events -- Python 3.12+ with async/await -- Pydantic for data validation - -**Key Modules:** - -#### 2.1 REST API Endpoints - -```python -POST /api/workflow/start - • Input: SuspiciousActivityAlert - • Creates workflow instance - • Returns: { status, alert_id, message } - • Spawns async workflow execution task - -GET /api/alerts - • Returns sample alerts for testing - • Includes: alert_id, customer_id, alert_type, severity - -POST /api/workflow/decision - • Input: AnalystDecisionRequest - • Resolves checkpoint for resume - • Continues workflow with analyst's decision - • Returns: { status, message } - -GET /api/workflow/status/{alert_id} - • Returns current workflow state - • Includes: status, current_executor, events, pending_decision -``` - -#### 2.2 WebSocket Manager - -**Connection Lifecycle:** -```python -1. Client connects → Accept WebSocket -2. Add to active_connections list -3. Stream events via broadcast() -4. On disconnect → Remove from list -``` - -**Event Broadcasting:** -- Sends JSON messages to all connected clients -- Handles disconnections gracefully -- No message queuing (real-time only) - -**Event Types:** -- `workflow_initializing` - Setup in progress -- `workflow_started` - Execution beginning -- `executor_invoked` - Agent starting work -- `executor_completed` - Agent finished -- `decision_required` - Human input needed -- `workflow_completed` - All done -- `workflow_error` - Error occurred - -#### 2.3 Workflow Orchestration - -**Initialization Phase:** -```python -async def run_workflow(alert): - 1. Create workflow instance (pre-initialized resources) - 2. Send progress updates during setup - 3. Store workflow reference & instance - 4. Stream events via workflow.run_stream() - 5. Handle RequestInfoEvent → pause for analyst -``` - -**Resume Phase:** -```python -async def continue_workflow(alert_id, responses, checkpoint_id): - 1. Create NEW workflow instance - 2. Load checkpoint state - 3. Resume with workflow.run_stream_from_checkpoint() - 4. Provide analyst responses via responses dict - 5. Continue streaming events -``` - -**Checkpoint Resolution:** -- Retries up to 20 times with exponential backoff -- Checkpoints written AFTER superstep completion -- Maps request_id to checkpoint_id for resume - ---- - -### 3. Agent Framework Layer - -**Technology Stack:** -- Microsoft Agent Framework (`agent-framework`) -- DAG-based workflow engine -- State machine with transitions -- Checkpoint-based resumability - -**Architecture Pattern:** -- **Directed Acyclic Graph (DAG)** for workflow structure -- **Executor Nodes** as individual agents/tasks -- **Edges** define dependencies and data flow -- **State Management** tracks execution progress - -#### 3.1 Workflow Engine - -**Execution Model:** -``` -SuperStep Cycle: -1. Identify ready executors (dependencies met) -2. Execute executors in parallel -3. Collect outputs -4. Update shared state -5. Write checkpoint -6. Repeat until terminal state -``` - -**Event System:** -```python -- ExecutorInvokedEvent → Executor starting -- ExecutorCompletedEvent → Executor finished -- WorkflowStatusEvent → State transition -- RequestInfoEvent → Human input needed -- WorkflowOutputEvent → Final result -``` - -**Checkpointing:** -- Snapshot of entire workflow state -- Includes: executor states, shared state, pending requests -- File-based storage (JSON) -- Required for human-in-the-loop resume - -#### 3.2 Executor Nodes (Agents) - -**Executor Types:** - -1. **AlertRouter** - Entry point, routes alert to analysis -2. **UsagePatternExecutor** - Analyzes data usage patterns -3. **LocationAnalysisExecutor** - Analyzes login locations -4. **BillingChargeExecutor** - Checks billing anomalies -5. **RiskAggregator** - Combines analyses, calculates risk score -6. **ReviewGateway** - Decides: auto-clear or analyst review -7. **AutoClear** - Handles low-risk alerts automatically -8. **RequestInfoExecutor (AnalystReview)** - Human-in-the-loop -9. **FraudAction** - Executes approved action (lock/refund/etc.) -10. **FinalNotification** - Logs completion and notifies - -**Executor Pattern:** -```python -class CustomExecutor(Executor): - async def execute(self, input_data, shared_state): - # Access MCP tools - tool_result = await self.mcp_tool.call_tool(...) - - # LLM reasoning - response = await self.chat_client.chat(...) - - # Update state - return output_data -``` - -#### 3.3 RequestInfoExecutor (Human-in-the-Loop) - -**Mechanism:** -```python -1. Workflow reaches RequestInfoExecutor -2. Executor pauses (yields RequestInfoEvent) -3. Checkpoint created with pending request -4. Backend broadcasts decision_required to UI -5. Analyst provides decision via UI -6. Backend calls continue_workflow() with responses -7. Workflow resumes from checkpoint -8. RequestInfoExecutor returns analyst's response -9. Workflow continues execution -``` - -**Key Features:** -- Multiple pending requests supported -- Request ID mapping to checkpoint iteration -- Timeout handling (if needed) -- Request-response correlation - ---- - -### 4. AI & Tools Layer - -#### 4.1 Azure OpenAI Service - -**Model Configuration:** -```python -Deployment: gpt-4 (or gpt-4-turbo) -Authentication: Azure CLI Credential (DefaultAzureCredential) -Endpoint: Azure OpenAI resource endpoint -Features: - - Chat completion - - Function calling (tool calling) - - JSON mode responses -``` - -**Usage Pattern:** -```python -# Agent makes decision -response = await chat_client.chat( - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_message} - ], - tools=available_tools, - temperature=0.7 -) - -# LLM decides to call a tool -tool_call = response.tool_calls[0] -tool_result = await mcp_tool.call_tool(tool_call.name, args) - -# LLM processes result and continues -``` - -#### 4.2 Model Context Protocol (MCP) Server - -**Purpose:** -- Standardized protocol for tool exposure -- HTTP-based (FastMCP implementation) -- Server-Sent Events (SSE) for streaming -- Used by agents to access customer data - -**MCP Tool Integration:** -```python -# Backend initializes MCP client -mcp_tool = MCPStreamableHTTPTool( - name="contoso_mcp", - url="http://localhost:8000/mcp", - headers={"Content-Type": "application/json"} -) - -# Agent calls tool -result = await mcp_tool.call_tool( - "get_customer_detail", - {"params": {"customer_id": 123}} -) -``` - -**Tool Categories:** - -**Customer Management:** -- `get_all_customers()` - List all customers -- `get_customer_detail(customer_id)` - Full customer profile -- `get_customer_orders(customer_id)` - Order history -- `unlock_account(customer_id)` - Unlock locked account - -**Subscription Management:** -- `get_subscription_detail(subscription_id)` - Detailed subscription info -- `update_subscription(...)` - Modify subscription settings -- `get_data_usage(...)` - Data usage over time period - -**Billing:** -- `get_billing_summary(customer_id)` - Current balance -- `pay_invoice(invoice_id, amount)` - Record payment -- `get_invoice_payments(invoice_id)` - Payment history - -**Support & Security:** -- `get_support_tickets(customer_id)` - Ticket history -- `create_support_ticket(...)` - Create new ticket -- `get_security_logs(customer_id)` - Security events -- `search_knowledge_base(query)` - Semantic search policies - ---- - -### 5. Data & Storage Layer - -#### 5.1 Checkpoint Storage - -**Format:** -```json -{ - "checkpoint_id": "uuid-here", - "workflow_id": "workflow-uuid", - "timestamp": "2025-10-09T10:30:00Z", - "executor_states": { - "executor_id": { - "status": "completed", - "output": {...} - } - }, - "shared_state": { - "alert": {...}, - "risk_assessment": {...}, - "__pending_requests__": { - "request-id": {...} - } - } -} -``` - -**Location:** `./checkpoints/` -**Cleanup:** Deleted after workflow completion - -#### 5.2 Simulated Database - -**Implementation:** -- In-memory Python data structures -- Generated with Faker library -- Realistic customer scenarios - -**Data Models:** -```python -Customer: - - customer_id, name, email, phone - - account_status, loyalty_tier - - created_at, last_login - -Subscription: - - subscription_id, customer_id, product_id - - status, start_date, end_date - - data_cap_gb, speed_tier - - autopay_enabled, roaming_enabled - -Invoice: - - invoice_id, customer_id, subscription_id - - amount, due_date, status - - payments (linked) - -SupportTicket: - - ticket_id, customer_id, subscription_id - - category, priority, status - - subject, description -``` - ---- - -## Data Flow: Complete Request Cycle - -### Scenario: High-Risk Alert with Analyst Review - -``` -┌──────────────────────────────────────────────────────────────────────────┐ -│ PHASE 1: WORKFLOW START │ -└──────────────────────────────────────────────────────────────────────────┘ - -1. User selects "ALERT-001" in UI -2. UI → POST /api/workflow/start { alert_id: "ALERT-001", ... } -3. Backend: - - Creates workflow instance - - Spawns run_workflow(alert) task - - Returns immediately -4. Workflow Engine: - - Broadcasts: workflow_initializing - - Creates workflow graph - - Broadcasts: workflow_started -5. UI updates: Workflow Control shows "Running..." - -┌──────────────────────────────────────────────────────────────────────────┐ -│ PHASE 2: AGENT ANALYSIS (Automatic) │ -└──────────────────────────────────────────────────────────────────────────┘ - -6. AlertRouter executes: - - Broadcasts: executor_invoked (AlertRouter) - - Routes alert to analysis executors - - Broadcasts: executor_completed (AlertRouter) - -7. Analysis Executors (parallel): - A. UsagePatternExecutor: - - Calls MCP: get_data_usage() - - LLM analyzes: "500% spike is suspicious" - - Broadcasts: executor_completed - - B. LocationAnalysisExecutor: - - Calls MCP: get_security_logs() - - LLM analyzes: "USA to Russia in 2 hours - impossible travel" - - Broadcasts: executor_completed - - C. BillingChargeExecutor: - - Calls MCP: get_billing_summary() - - LLM analyzes: "No unusual charges" - - Broadcasts: executor_completed - -8. RiskAggregator executes: - - Combines all analysis results - - Calculates overall_risk_score: 0.88 - - Determines risk_level: "critical" - - Recommends: "lock_account" & "refund_charges" - - Broadcasts: executor_completed - -9. ReviewGateway executes: - - Evaluates: risk_score >= 0.6 → needs review - - Routes to: analyst_review (not auto_clear) - - Broadcasts: executor_completed - -┌──────────────────────────────────────────────────────────────────────────┐ -│ PHASE 3: HUMAN-IN-THE-LOOP (Pause & Wait) │ -└──────────────────────────────────────────────────────────────────────────┘ - -10. AnalystReview (RequestInfoExecutor) executes: - - Yields RequestInfoEvent - - Workflow pauses - - Checkpoint written to disk - -11. Backend processes RequestInfoEvent: - - Resolves checkpoint (retry with backoff) - - Stores pending_decisions[alert_id] - - Broadcasts: decision_required - - WebSocket → UI - -12. UI receives decision_required: - - Displays Analyst Decision Panel - - Shows: risk score, analysis, recommended action - - Workflow graph: "analyst_review" node shows "running" - -13. Analyst makes decision: - - Selects: "Lock Account & Refund" - - Adds notes: "Confirmed fraud - Russia login" - - Clicks "Submit Decision" - -14. UI → POST /api/workflow/decision - { - request_id: "req-123", - approved_action: "both", - analyst_notes: "Confirmed fraud..." - } - -┌──────────────────────────────────────────────────────────────────────────┐ -│ PHASE 4: WORKFLOW RESUME │ -└──────────────────────────────────────────────────────────────────────────┘ - -15. Backend continue_workflow(): - - Creates NEW workflow instance - - Loads checkpoint from disk - - Calls: workflow.run_stream_from_checkpoint( - checkpoint_id, - responses={req_id: AnalystDecision(...)} - ) - -16. Workflow Engine: - - Restores state from checkpoint - - Resumes at analyst_review executor - - RequestInfoExecutor returns analyst's decision - - Broadcasts: executor_completed (analyst_review) - -17. FraudAction executes: - - Input: approved_action = "both" - - Calls MCP: update_subscription(status="locked") - - Calls MCP: pay_invoice(refund_amount) - - Broadcasts: executor_completed - -18. FinalNotification executes: - - Calls MCP: create_support_ticket(...) - - Logs: "Alert ALERT-001 resolved - account locked & refunded" - - Broadcasts: executor_completed - -19. Workflow completes: - - Broadcasts: workflow_completed - - Cleanup checkpoints - - UI updates: All nodes green, workflow done - -┌──────────────────────────────────────────────────────────────────────────┐ -│ PHASE 5: UI UPDATE CYCLE (Throughout) │ -└──────────────────────────────────────────────────────────────────────────┘ - -WebSocket events → UI state updates: - -• executor_invoked → Node color: blue (running) -• executor_completed → Node color: green (completed) -• decision_required → Show Analyst Decision Panel -• workflow_completed → Reset UI, show success - -Event Log displays all events in real-time with timestamps -``` - ---- - -## Key Design Patterns - -### 1. Event-Driven Architecture -- **Pattern:** Observer pattern via WebSocket -- **Benefit:** Real-time UI updates without polling -- **Implementation:** ConnectionManager broadcasts to all clients - -### 2. Checkpoint-Resume Pattern -- **Pattern:** Memento pattern for workflow state -- **Benefit:** Enable human-in-the-loop without blocking -- **Implementation:** FileCheckpointStorage with JSON serialization - -### 3. Agent Pattern -- **Pattern:** Autonomous agents with tools -- **Benefit:** Modular, testable, extensible analysis -- **Implementation:** Executor nodes with LLM + MCP tools - -### 4. DAG-Based Orchestration -- **Pattern:** Workflow as directed acyclic graph -- **Benefit:** Parallel execution, clear dependencies -- **Implementation:** Agent Framework workflow engine - -### 5. Tool Abstraction (MCP) -- **Pattern:** Adapter pattern for external systems -- **Benefit:** Standardized tool interface, swap implementations -- **Implementation:** MCP protocol with HTTP transport - ---- - -## Scalability Considerations - -### Current Design (Single Server) -- **Concurrent Workflows:** Limited by FastAPI worker threads -- **State Storage:** File-based (not distributed) -- **WebSocket:** Single server, no load balancing - -### Production Enhancements - -#### 1. Distributed Workflow Execution -``` -Replace: FileCheckpointStorage -With: Azure Cosmos DB or Redis -Benefit: Multiple backend instances, shared state -``` - -#### 2. Message Queue for Events -``` -Add: Azure Service Bus or RabbitMQ -Pattern: Producer (workflow) → Queue → Consumer (WebSocket broadcaster) -Benefit: Decouple execution from UI updates -``` - -#### 3. Horizontal Scaling -``` -Add: Load balancer + multiple backend instances -Add: Redis for WebSocket connection state -Pattern: Sticky sessions or broadcast via Redis pub/sub -``` - -#### 4. Async Job Processing -``` -Add: Celery or Azure Functions -Pattern: Backend submits workflow jobs to queue -Workers execute workflows, emit events to message bus -``` - ---- - -## Security Considerations - -### Authentication & Authorization -- **Current:** Azure CLI credential (dev only) -- **Production:** Add user authentication (OAuth2, OIDC) -- **RBAC:** Analyst vs. Admin vs. Viewer roles - -### Data Protection -- **Encryption:** TLS for API & WebSocket -- **PII Handling:** Mask sensitive customer data in logs -- **Audit Trail:** Log all analyst decisions with timestamps - -### MCP Tool Security -- **API Keys:** Secure MCP server with authentication -- **Rate Limiting:** Prevent tool abuse -- **Input Validation:** Sanitize all tool parameters - ---- - -## Monitoring & Observability - -### Application Insights Integration -```python -from azure.monitor.opentelemetry import configure_azure_monitor - -configure_azure_monitor( - connection_string=os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING") -) -``` - -**Track:** -- Workflow execution times -- Analyst response times -- Tool call durations -- Error rates & exceptions -- WebSocket connection metrics - -### Logging Strategy -``` -INFO: Workflow lifecycle events -DEBUG: Tool calls & LLM prompts -ERROR: Exceptions with stack traces -WARN: Checkpoint resolution delays -``` - -### Tracing -- OpenTelemetry tracing built into agent-framework -- Correlate events across workflow → backend → MCP → LLM - ---- - -## Deployment Architecture - -### Development -``` -Local Machine: -├─ Frontend: npm run dev (Vite) → localhost:3000 -├─ Backend: uvicorn backend:app → localhost:8001 -└─ MCP Server: uvicorn mcp_service:app → localhost:8000 -``` - -### Production (Azure) - -``` -┌────────────────────────────────────────────────────────────┐ -│ Azure Front Door │ -│ (CDN + Load Balancer) │ -└─────────────────────┬──────────────────────────────────────┘ - │ - ┌─────────────┴─────────────┐ - │ │ - ▼ ▼ -┌───────────────┐ ┌───────────────┐ -│ Static Web │ │ Azure App │ -│ App │ │ Service │ -│ (React build) │ │ (FastAPI) │ -└───────────────┘ └───────┬───────┘ - │ - ┌──────────────┼──────────────┐ - │ │ │ - ▼ ▼ ▼ - ┌─────────────┐ ┌──────────┐ ┌──────────┐ - │ Azure │ │ Azure │ │ MCP │ - │ OpenAI │ │ Cosmos DB│ │ Server │ - └─────────────┘ └──────────┘ └──────────┘ - │ - ▼ - ┌──────────┐ - │ Azure SQL│ - │ Database │ - └──────────┘ -``` - ---- - -## Technology Summary - -| Layer | Technology | Purpose | -|-------|-----------|---------| -| **Frontend** | React 18 | UI framework | -| | Material-UI | Component library | -| | React Flow | Workflow visualization | -| | WebSocket API | Real-time events | -| | Vite | Build tool | -| **Backend** | FastAPI | REST API server | -| | WebSocket | Event streaming | -| | Uvicorn | ASGI server | -| | Pydantic | Data validation | -| **Agent Framework** | agent-framework | Workflow engine | -| | Azure OpenAI | LLM reasoning | -| | MCP Client | Tool invocation | -| **MCP Server** | FastMCP | MCP implementation | -| | Faker | Test data generation | -| **Storage** | File System | Checkpoints (dev) | -| | JSON | Checkpoint format | -| **AI** | Azure OpenAI | GPT-4 model | -| | Azure CLI Credential | Authentication | - ---- - -## Conclusion - -This architecture demonstrates a modern, event-driven AI agent system with: - -✅ **Real-time visualization** - WebSocket streaming for live updates -✅ **Human-in-the-loop** - Checkpoint-based pause/resume -✅ **Modular agents** - Each executor is independent and testable -✅ **Standardized tools** - MCP protocol for tool integration -✅ **Production-ready patterns** - Async/await, error handling, logging -✅ **Scalable design** - Ready for distributed deployment - -The system can handle complex fraud detection scenarios while providing full transparency and control to human analysts. diff --git a/agentic_ai/workflow/fraud_detection/DIAGRAMS.md b/agentic_ai/workflow/fraud_detection/DIAGRAMS.md deleted file mode 100644 index 0fb8daa11..000000000 --- a/agentic_ai/workflow/fraud_detection/DIAGRAMS.md +++ /dev/null @@ -1,420 +0,0 @@ -# Fraud Detection Workflow - Visual Diagrams - -## Complete Workflow Topology - -``` -┌─────────────────────────────────────────────────────────────────────────┐ -│ SUSPICIOUS ACTIVITY ALERT │ -│ │ -│ ┌────────────────────────────────────────────────────────────────┐ │ -│ │ Alert ID: ALERT-001 │ │ -│ │ Customer ID: 1 │ │ -│ │ Type: multi_country_login │ │ -│ │ Description: Login attempts from USA and Russia within 2 hours │ │ -│ │ Severity: high │ │ -│ └────────────────────────────────────────────────────────────────┘ │ -└───────────────────────────────┬──────────────────────────────────────────┘ - ↓ - ┌───────────────────────┐ - │ AlertRouter │ - │ Executor │ - │ │ - │ - Receives alert │ - │ - Routes to 3 │ - │ specialist agents │ - └───────────┬───────────┘ - ↓ - ┌──────────────────────┼──────────────────────┐ - ↓ ↓ ↓ -┌────────────────────┐ ┌────────────────────┐ ┌────────────────────┐ -│ UsagePattern │ │ LocationAnalysis │ │ BillingCharge │ -│ Executor │ │ Executor │ │ Executor │ -│ │ │ │ │ │ -│ MCP Tools: │ │ MCP Tools: │ │ MCP Tools: │ -│ • get_customer_ │ │ • get_customer_ │ │ • get_customer_ │ -│ detail │ │ detail │ │ detail │ -│ • get_subscription │ │ • get_security_ │ │ • get_billing_ │ -│ _detail │ │ logs │ │ summary │ -│ • get_data_usage │ │ • search_ │ │ • get_subscription │ -│ • search_ │ │ knowledge_base │ │ _detail │ -│ knowledge_base │ │ │ │ • get_customer_ │ -│ │ │ │ │ orders │ -│ ↓ │ │ ↓ │ │ ↓ │ -│ Risk Score: 0.7 │ │ Risk Score: 0.85 │ │ Risk Score: 0.4 │ -│ Indicators: │ │ Indicators: │ │ Indicators: │ -│ • Data spike │ │ • Impossible │ │ • Normal billing │ -│ • Off-peak usage │ │ travel │ │ pattern │ -└─────────┬──────────┘ └─────────┬──────────┘ └─────────┬──────────┘ - └──────────────────────┼──────────────────────┘ - ↓ - ┌────────────────────────┐ - │ FraudRiskAggregator │ - │ Executor (LLM Agent) │ - │ │ - │ Analyzes all findings │ - │ Calculates weighted │ - │ risk score │ - │ │ - │ Overall Risk: 0.65 │ - │ Level: high │ - │ Action: lock_account │ - └───────────┬────────────┘ - ↓ - ┌───────────────────────┐ - │ RISK SCORE SWITCH │ - │ │ - │ Score >= 0.6? │ - └───────────┬───────────┘ - ↓ - ┌─────────────────┴─────────────────┐ - ↓ ↓ - (YES - High Risk) (NO - Low Risk) - ↓ ↓ -┌────────────────────────┐ ┌────────────────────────┐ -│ RequestInfoExecutor │ │ AutoClearExecutor │ -│ (Human-in-the-Loop) │ │ │ -│ │ │ Automatically clears │ -│ ┌────────────────────┐ │ │ low-risk alerts │ -│ │ WORKFLOW PAUSED │ │ │ │ -│ │ │ │ │ Creates ActionResult: │ -│ │ Checkpoint saved │ │ │ • action: cleared │ -│ │ │ │ │ • success: true │ -│ │ Waiting for analyst│ │ └───────────┬────────────┘ -│ │ decision... │ │ │ -│ └────────────────────┘ │ │ -│ │ │ -│ ┌────────────────────┐ │ │ -│ │ ANALYST REVIEWS │ │ │ -│ │ │ │ │ -│ │ Inputs: │ │ │ -│ │ • Alert details │ │ │ -│ │ • Risk assessment │ │ │ -│ │ • All findings │ │ │ -│ │ │ │ │ -│ │ Decision: │ │ │ -│ │ • lock_account │ │ │ -│ │ • refund_charges │ │ │ -│ │ • both │ │ │ -│ │ • clear │ │ │ -│ └────────────────────┘ │ │ -│ │ │ -│ Analyst Decision: │ │ -│ • Action: lock_account │ │ -│ • Notes: "Confirmed │ │ -│ fraudulent activity" │ │ -└───────────┬────────────┘ │ - ↓ │ -┌────────────────────────┐ │ -│ FraudActionExecutor │ │ -│ │ │ -│ Executes approved │ │ -│ action: │ │ -│ • Lock account │ │ -│ • Refund charges │ │ -│ • Both │ │ -│ • Clear │ │ -│ │ │ -│ Creates ActionResult: │ │ -│ • action_taken │ │ -│ • success: true │ │ -│ • details │ │ -│ • timestamp │ │ -└───────────┬────────────┘ │ - └──────────────┬──────────────────────┘ - ↓ - ┌────────────────────────┐ - │ FinalNotification │ - │ Executor │ - │ │ - │ • Notify customer │ - │ • Log audit trail │ - │ • Close alert │ - │ │ - │ Workflow Output: │ - │ FinalNotification { │ - │ customer_notified │ - │ audit_logged │ - │ } │ - └────────────────────────┘ - ↓ - ✅ WORKFLOW COMPLETE -``` - -## Message Flow Sequence - -``` -1. SuspiciousActivityAlert - ├─ alert_id: "ALERT-001" - ├─ customer_id: 1 - ├─ alert_type: "multi_country_login" - └─ severity: "high" - -2. Fan-Out (Parallel Execution) - ├─→ UsageAnalysisResult - │ ├─ risk_score: 0.7 - │ └─ risk_indicators: ["data_spike", "off_peak_usage"] - │ - ├─→ LocationAnalysisResult - │ ├─ risk_score: 0.85 - │ └─ risk_indicators: ["impossible_travel", "high_risk_region"] - │ - └─→ BillingAnalysisResult - ├─ risk_score: 0.4 - └─ risk_indicators: ["normal_pattern"] - -3. Fan-In (Aggregation) - → FraudRiskAssessment - ├─ overall_risk_score: 0.65 - ├─ risk_level: "high" - ├─ recommended_action: "lock_account" - └─ reasoning: "Multiple high-risk indicators..." - -4. Switch/Case (Conditional Routing) - IF risk_score >= 0.6: - → RequestInfoEvent - └─ Workflow PAUSES ⏸️ - - ELSE: - → AutoClear - └─ ActionResult(action_taken="cleared") - -5. Human Input (Analyst Decision) - → AnalystDecision - ├─ approved_action: "lock_account" - └─ analyst_notes: "Confirmed fraudulent activity" - - Workflow RESUMES ▶️ - -6. Action Execution - → ActionResult - ├─ action_taken: "lock_account" - ├─ success: true - └─ details: "Account locked. Analyst: analyst_001" - -7. Final Notification - → FinalNotification - ├─ customer_notified: true - ├─ audit_logged: true - └─ resolution: "Account locked due to fraudulent activity" - -8. Workflow Output - ✅ Complete -``` - -## Timing Diagram - -``` -Time → - -0s AlertRouter - ↓ -1s ┌──────────────┬──────────────┬──────────────┐ - UsagePattern Location Billing - Executor Executor Executor - ↓ ↓ ↓ - - [Parallel MCP Tool Calls] - -12s ↓ ↓ ↓ - Risk: 0.7 Risk: 0.85 Risk: 0.4 - └──────────────┴──────────────┘ - -13s FraudRiskAggregator (LLM) - ↓ - -18s Risk Assessment: 0.65 (HIGH) - ↓ - -19s Switch/Case Routing - ↓ - - [HIGH RISK PATH] - ↓ - -20s RequestInfoExecutor - ⏸️ WORKFLOW PAUSED - - [WAITING FOR ANALYST...] - -5m Analyst Reviews Case - ↓ - -5m30s AnalystDecision: lock_account - ▶️ WORKFLOW RESUMED - ↓ - -5m31s FraudActionExecutor - ↓ - -5m32s FinalNotificationExecutor - ↓ - -5m33s ✅ COMPLETE - -Total Active Time: ~33 seconds -Total Wall Time: ~5 minutes 33 seconds (includes analyst review) -``` - -## State Transitions - -``` -┌─────────────────┐ -│ INITIALIZED │ -│ │ -│ • Workflow built│ -│ • Executors │ -│ ready │ -└────────┬────────┘ - ↓ - run_stream() - ↓ -┌─────────────────┐ -│ RUNNING │ -│ │ -│ • Processing │ -│ alert │ -│ • Superstep 1 │ -└────────┬────────┘ - ↓ - 3 specialists complete - ↓ -┌─────────────────┐ -│ AGGREGATING │ -│ │ -│ • Fan-in wait │ -│ • LLM analysis │ -│ • Superstep 2 │ -└────────┬────────┘ - ↓ - Risk score >= 0.6 - ↓ -┌─────────────────┐ -│ WAITING_INPUT │ -│ │ -│ • Checkpoint │ -│ saved │ -│ • RequestInfo │ -│ event emitted │ -└────────┬────────┘ - ↓ - Analyst decision received - ↓ -┌─────────────────┐ -│ EXECUTING │ -│ │ -│ • Fraud action │ -│ • Superstep 3 │ -└────────┬────────┘ - ↓ -┌─────────────────┐ -│ NOTIFYING │ -│ │ -│ • Customer │ -│ • Audit log │ -│ • Superstep 4 │ -└────────┬────────┘ - ↓ -┌─────────────────┐ -│ COMPLETED │ -│ │ -│ • Final output │ -│ • Idle status │ -└─────────────────┘ -``` - -## Checkpoint Structure - -``` -WorkflowCheckpoint { - checkpoint_id: "cp_123456789" - workflow_id: "fraud_detection_001" - timestamp: "2025-10-07T10:30:45" - iteration_count: 3 - - pending_messages: { - "analyst_review": [ - FraudRiskAssessment { - alert_id: "ALERT-001" - overall_risk_score: 0.65 - recommended_action: "lock_account" - ... - } - ] - } - - shared_state: { - "current_alert_id": "ALERT-001" - "customer_id": 1 - } - - executor_states: { - "usage_pattern_executor": { - "analysis_complete": true - "risk_score": 0.7 - } - "location_analysis_executor": { - "analysis_complete": true - "risk_score": 0.85 - } - "billing_charge_executor": { - "analysis_complete": true - "risk_score": 0.4 - } - "fraud_risk_aggregator": { - "assessment_complete": true - "overall_risk": 0.65 - } - } -} -``` - -## Error Handling Flow - -``` -┌─────────────────┐ -│ Normal Flow │ -└────────┬────────┘ - ↓ - Try Execute - ↓ - ┌────┴────┐ - │ │ -Success Exception - │ │ - ↓ ↓ -Continue ┌──────────────┐ - │ Error Handler│ - └──────┬───────┘ - ↓ - ┌─────────────┐ - │ Log Error │ - └─────┬───────┘ - ↓ - ┌─────────────┐ - │ Checkpoint │ - │ Save │ - └─────┬───────┘ - ↓ - ┌─────────────┐ - │ Notify │ - │ Admin │ - └─────┬───────┘ - ↓ - ┌─────────────┐ - │ Workflow │ - │ FAILED │ - └─────────────┘ -``` - ---- - -## Legend - -- `┌──┐` Executor/Component -- `↓` Message flow -- `→` Data flow -- `⏸️` Workflow paused -- `▶️` Workflow resumed -- `✅` Success/Complete -- `[...]` Parallel execution -- `{...}` Data structure diff --git a/agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md b/agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md deleted file mode 100644 index fe2ae2649..000000000 --- a/agentic_ai/workflow/fraud_detection/IMPLEMENTATION.md +++ /dev/null @@ -1,434 +0,0 @@ -# Fraud Detection Workflow Implementation Summary - -## 📋 Overview - -Successfully implemented the Contoso Fraud Detection & Escalation Workflow as specified in `scenario.md`. This is a production-ready console application demonstrating advanced Agent Framework workflow patterns. - -## ✅ What Was Implemented - -### 1. **Complete Workflow Topology** -- ✅ AlertRouterExecutor (entry point) -- ✅ Three specialist agent executors with MCP tools: - - UsagePatternExecutor - - LocationAnalysisExecutor - - BillingChargeExecutor -- ✅ FraudRiskAggregatorExecutor (LLM-based fan-in) -- ✅ Switch/case routing based on risk score -- ✅ ReviewGatewayExecutor for human-in-the-loop (uses ctx.request_info API) -- ✅ AutoClearExecutor for low-risk cases -- ✅ FraudActionExecutor for action execution -- ✅ FinalNotificationExecutor for completion - -### 2. **Key Patterns Demonstrated** - -#### Fan-Out Pattern -```python -# One alert → three parallel analyses -builder.add_edge(alert_router, usage_executor) -builder.add_edge(alert_router, location_executor) -builder.add_edge(alert_router, billing_executor) -``` - -#### Fan-In Pattern -```python -# Three analyses → one aggregator (waits for all) -builder.add_fan_in_edge( - [usage_executor, location_executor, billing_executor], - aggregator -) -``` - -#### Switch/Case Routing -```python -# Route based on risk score -builder.add_switch_case_edge( - aggregator, - cases=[ - (lambda a: a.overall_risk_score >= 0.6, analyst_review), # High risk - (lambda a: a.overall_risk_score < 0.6, auto_clear), # Low risk - ], -) -``` - -#### Human-in-the-Loop -```python -# ReviewGatewayExecutor uses ctx.request_info() to pause workflow -class ReviewGatewayExecutor(Executor): - async def handle_assessment(self, ctx, assessment: FraudRiskAssessment): - request = AnalystReviewRequest(...) - await ctx.request_info(request, AnalystDecision) - - @response_handler - async def handle_analyst_response(self, ctx, decision: AnalystDecision): - # Process the analyst decision when workflow resumes - await ctx.send_message(decision) -``` - -### 3. **MCP Tool Integration** - -Each specialist agent has **filtered access** to domain-specific MCP tools: - -**UsagePatternExecutor**: -- `get_customer_detail` -- `get_subscription_detail` -- `get_data_usage` -- `search_knowledge_base` - -**LocationAnalysisExecutor**: -- `get_customer_detail` -- `get_security_logs` -- `search_knowledge_base` - -**BillingChargeExecutor**: -- `get_customer_detail` -- `get_billing_summary` -- `get_subscription_detail` -- `get_customer_orders` -- `search_knowledge_base` - -### 4. **Type-Safe Message Flow** - -All messages use Pydantic models for validation: - -```python -SuspiciousActivityAlert → [3 specialist executors] - ↓ -[UsageAnalysisResult, LocationAnalysisResult, BillingAnalysisResult] - ↓ -FraudRiskAssessment → (Switch/Case) - ↓ -AnalystDecision OR ActionResult - ↓ -FinalNotification -``` - -### 5. **LLM-Based Risk Aggregation** - -The `FraudRiskAggregatorExecutor` uses an LLM agent to: -- Synthesize findings from three specialist agents -- Calculate weighted overall risk score -- Determine risk level (low/medium/high/critical) -- Recommend action (clear/lock_account/refund_charges/both) -- Provide reasoning referencing specific findings - -### 6. **Checkpointing Support** - -```python -# Workflow state saved at each superstep -# Using UTF8FileCheckpointStorage wrapper for Windows compatibility -checkpoint_storage = UTF8FileCheckpointStorage("./checkpoints") -workflow = create_fraud_detection_workflow( - ..., - checkpoint_storage=checkpoint_storage -) - -# Resume from checkpoint after analyst decision -async for event in workflow.run_stream(checkpoint_id=checkpoint_id): - process_event(event) - -# Send responses and continue workflow -async for event in workflow.send_responses_streaming(responses): - process_event(event) -``` - -## 📁 Files Created - -``` -agentic_ai/workflow/examples/fraud_detection/ -├── fraud_detection_workflow.py (890 lines - main implementation) -├── README.md (comprehensive documentation) -├── .env.sample (environment configuration template) -└── IMPLEMENTATION.md (this file) -``` - -## 🎯 Key Features - -### Event-Driven Architecture -- Triggered by suspicious activity alerts from monitoring systems -- Supports multiple alert types: multi-country logins, data spikes, unusual charges - -### Parallel Processing -- Three specialist agents analyze simultaneously -- Utilizes `asyncio.gather` for concurrent execution -- Reduces total analysis time by ~70% - -### Intelligent Risk Scoring -- LLM-based aggregation considers all specialist findings -- Weighted scoring based on alert severity and patterns -- Context-aware recommendations - -### False Positive Prevention -- Human analyst review for high-risk cases -- Prevents legitimate customers from being harmed -- Audit trail for all decisions - -### Production-Ready -- Comprehensive error handling -- Detailed logging at each step -- Type-safe message passing -- Graceful degradation on failures - -## 🚀 Running the Example - -### 1. Prerequisites - -```bash -# Set environment variables -export AZURE_OPENAI_API_KEY="your-key" -export AZURE_OPENAI_CHAT_DEPLOYMENT="your-deployment" -export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/" -export MCP_SERVER_URI="http://localhost:8000" -``` - -### 2. Start MCP Server - -```bash -cd mcp -python mcp_service.py -``` - -### 3. Run Fraud Detection Workflow - -```bash -cd agentic_ai/workflow/fraud_detection -python fraud_detection_workflow.py -``` - -## 📊 Sample Output - -``` -================================================================================ -Contoso Fraud Detection & Escalation Workflow -================================================================================ -[Setup] Connecting to MCP server at http://localhost:8000 -[Setup] Connected to MCP server, loaded 20 tools -[Workflow] Fraud detection workflow built successfully - -Processing Alert: ALERT-001 (Multi-country login) -[AlertRouter] Routing to 3 analysis executors -[UsagePatternExecutor] Analyzing... risk_score=0.7 -[LocationAnalysisExecutor] Analyzing... risk_score=0.85 -[BillingChargeExecutor] Analyzing... risk_score=0.4 -[FraudRiskAggregator] Overall risk_score=0.65, action=lock_account - -ANALYST REVIEW REQUIRED -[Analyst] Decision: lock_account -[FraudAction] Account locked successfully -[FinalNotification] Customer notified, audit logged - -Workflow completed successfully! -``` - -## 🎓 Learning Objectives Achieved - -### ✅ Fan-Out Pattern -Learned how to route one message to multiple executors for parallel processing. - -### ✅ Fan-In Pattern -Learned how to wait for multiple inputs before proceeding (aggregation). - -### ✅ Switch/Case Routing -Learned conditional routing based on message content and business logic. - -### ✅ Human-in-the-Loop -Learned workflow pause/resume with external input using `ctx.request_info()` and `@response_handler` decorator. - -### ✅ Checkpointing -Learned persistent workflow state for long-running processes. - -### ✅ MCP Tool Integration -Learned how to integrate specialist agents with filtered tool access. - -### ✅ LLM Agent Executors -Learned how to create AI-powered executors for decision-making. - -### ✅ Type-Safe Messaging -Learned Pydantic model usage for message validation. - -## 🔧 Customization Examples - -### Adjust Risk Threshold - -```python -# More sensitive (catches more cases) -(lambda a: a.overall_risk_score >= 0.5, analyst_review) - -# Less sensitive (fewer false positives) -(lambda a: a.overall_risk_score >= 0.7, analyst_review) -``` - -### Add Fourth Specialist - -```python -class NetworkAnalysisExecutor(Executor): - """Analyzes network patterns for VPN/proxy usage.""" - # Implementation... - -builder.add_executor(network_executor) -builder.add_edge(alert_router, network_executor) -builder.add_fan_in_edge( - [usage, location, billing, network_executor], - aggregator -) -``` - -### Custom Alert Types - -```python -SuspiciousActivityAlert( - alert_id="ALERT-004", - customer_id=4, - alert_type="account_takeover", - description="Password changed followed by subscription upgrade", - timestamp=datetime.now().isoformat(), - severity="critical", -) -``` - -## 🏗️ Architecture Decisions - -### Why Fan-Out? -- **Parallelism**: All three analyses run simultaneously -- **Specialization**: Each agent focuses on one domain -- **Scalability**: Easy to add more specialist agents - -### Why LLM Aggregator? -- **Context-aware**: Considers nuances in specialist findings -- **Flexible reasoning**: Can handle complex scenarios -- **Explainable**: Provides reasoning for decisions - -### Why Human-in-the-Loop? -- **False positive prevention**: Critical for customer trust -- **Compliance**: Many fraud systems require human oversight -- **Learning**: Analyst decisions improve the system over time - -### Why Checkpointing? -- **Reliability**: Resume after failures or restarts -- **Async workflows**: Can wait hours/days for analyst -- **Audit trail**: Complete history of workflow execution - -## 🔍 Code Highlights - -### Specialist Agent with MCP Tools - -```python -class UsagePatternExecutor(Executor): - def __init__(self, mcp_tool, chat_client, model): - # Filter to domain-specific tools - allowed_tools = [ - "get_customer_detail", - "get_subscription_detail", - "get_data_usage", - "search_knowledge_base", - ] - - filtered_functions = [ - func for func in mcp_tool.functions - if func.name in allowed_tools - ] - - self._agent = ChatAgent( - name="UsagePatternAnalyst", - instructions="Analyze usage patterns...", - tools=filtered_functions, - ) -``` - -### LLM-Based Aggregation - -```python -class FraudRiskAggregatorExecutor(Executor): - async def handle_analysis_results( - self, ctx, results: list[AnalysisResult] - ): - # Synthesize all findings - prompt = f"Aggregate these analyses: {results}" - response = await self._agent.run_stream(prompt) - - # Parse and create assessment - assessment = FraudRiskAssessment(...) - await ctx.send_message(assessment) -``` - -### Workflow Pause/Resume - -```python -# Workflow automatically pauses when ctx.request_info() is called -async for event in workflow.run_stream(alert): - if isinstance(event, RequestInfoEvent): - # Workflow is now paused, waiting for analyst decision - request_id = event.request_id - - # ... Get analyst decision from UI ... - - # Resume workflow with the response - responses = {request_id: analyst_decision} - async for event in workflow.send_responses_streaming(responses): - # @response_handler processes the decision - process_event(event) -``` - -## 📈 Performance Characteristics - -### Sequential Processing (Hypothetical) -``` -Usage Analysis: ~10 seconds -Location Analysis: ~8 seconds -Billing Analysis: ~12 seconds -Aggregation: ~5 seconds -Total: ~35 seconds -``` - -### Parallel Processing (Actual) -``` -[Usage, Location, Billing]: ~12 seconds (parallel) -Aggregation: ~5 seconds -Total: ~17 seconds (51% faster!) -``` - -## 🚦 Next Steps - -### Short-term Enhancements -1. Add persistent checkpoint storage (Redis/Cosmos DB) -2. Implement real-time UI for analyst review -3. Add metrics and OpenTelemetry tracing -4. Improve response parsing (use structured output API) - -### Medium-term Enhancements -1. Multi-alert processing (parallel workflows) -2. Escalation chains for critical cases -3. Machine learning model integration -4. A/B testing for risk thresholds - -### Long-term Enhancements -1. Integration with actual fraud systems -2. Real-time monitoring dashboard -3. Automated retraining from analyst feedback -4. Geographic distribution for global scale - -## 🎉 Success Metrics - -✅ **Complete implementation** of all 9 executors from scenario -✅ **All workflow patterns** demonstrated (fan-out, fan-in, switch, HITL) -✅ **Type-safe messaging** with Pydantic models -✅ **MCP tool integration** with filtered access -✅ **LLM-based aggregation** for intelligent risk scoring -✅ **Checkpointing** for pause/resume capability -✅ **Comprehensive documentation** with examples -✅ **Production-ready** error handling and logging - -## 📚 Related Documentation - -- CHECK LINK [Workflow README](README.md) - Architecture overview -- DEAD LINK State Management Guide ../../../agents/agent_framework/STATE_MANAGEMENT.md - Persistence patterns -- [Human-in-the-Loop Examples](../human-in-the-loop.md) - HITL patterns - -- DEAD LINK Control Flow Examples ../control-flow/ - Switch/case routing -- DEAD LINK Parallelism Examples ../parallelism/ - Fan-out patterns - - ---- - -**Implementation Date**: October 7, 2025 -**Agent Framework Version**: Latest -**Status**: ✅ Complete and Ready for Testing diff --git a/agentic_ai/workflow/fraud_detection/QUICKSTART.md b/agentic_ai/workflow/fraud_detection/QUICKSTART.md deleted file mode 100644 index 213ab2e11..000000000 --- a/agentic_ai/workflow/fraud_detection/QUICKSTART.md +++ /dev/null @@ -1,263 +0,0 @@ -# Fraud Detection Workflow - Quick Start Guide - -## 🚀 5-Minute Setup - -### 1. Configure Environment - -```bash -# Copy sample env file -cd agentic_ai/workflow/fraud_detection -cp .env.sample .env - -# Edit .env with your Azure OpenAI credentials -nano .env -``` - -### 2. Start MCP Server - -```bash -# Terminal 1 -cd mcp -python mcp_service.py -``` - -### 3. Run Workflow - -```bash -# Terminal 2 -cd agentic_ai/workflow/fraud_detection -python fraud_detection_workflow.py -``` - -## 📋 Key Commands - -### Test with Different Alerts - -Edit `fraud_detection_workflow.py` and modify the `alerts` list: - -```python -# High-risk multi-country login -SuspiciousActivityAlert( - alert_id="ALERT-001", - customer_id=1, - alert_type="multi_country_login", - severity="high" -) - -# Medium-risk data spike -SuspiciousActivityAlert( - alert_id="ALERT-002", - customer_id=2, - alert_type="data_spike", - severity="medium" -) -``` - -### View Logs - -```bash -# Real-time log viewing -tail -f workflow.log - -# Search for specific events -grep "FraudRiskAggregator" workflow.log -``` - -## 🎯 Common Tasks - -### Change Risk Threshold - -**File**: `fraud_detection_workflow.py` -**Line**: ~810 - -```python -# Current: 0.6 (60% risk) -(lambda assessment: assessment.overall_risk_score >= 0.6, analyst_review) - -# Make more sensitive (catches more): -(lambda assessment: assessment.overall_risk_score >= 0.5, analyst_review) - -# Make less sensitive (fewer alerts): -(lambda assessment: assessment.overall_risk_score >= 0.7, analyst_review) -``` - -### Simulate Analyst Decisions - -**File**: `fraud_detection_workflow.py` -**Line**: ~890 - -```python -analyst_decision = AnalystDecision( - alert_id=alerts[0].alert_id, - approved_action="lock_account", # Change: "clear", "refund_charges", "both" - analyst_notes="Your notes here", - analyst_id="analyst_001", -) -``` - -### Test with Custom Customer - -```python -# Add to MCP database first via mcp/data/create_db.py -# Then use in alert: -SuspiciousActivityAlert( - alert_id="ALERT-999", - customer_id=999, # Your custom customer - alert_type="custom_test", - description="Testing with custom customer", - timestamp=datetime.now().isoformat(), - severity="medium", -) -``` - -## 🐛 Troubleshooting - -### Error: "Cannot connect to MCP server" -```bash -# Check if MCP server is running -ps aux | grep mcp_service - -# Restart MCP server -cd mcp -python mcp_service.py -``` - -### Error: "Azure OpenAI authentication failed" -```bash -# Verify environment variables -echo $AZURE_OPENAI_API_KEY -echo $AZURE_OPENAI_ENDPOINT - -# Re-export if needed -export AZURE_OPENAI_API_KEY="your-key" -``` - -### Workflow Hangs at Analyst Review -```bash -# Check logs for RequestInfoEvent -grep "ANALYST REVIEW" workflow.log - -# Verify analyst decision is being sent -# Check around line 890 in fraud_detection_workflow.py -``` - -### MCP Tools Not Found -```bash -# Verify MCP connection -curl http://localhost:8000/list_tools - -# Check filtered tools in logs -grep "filtered tools" workflow.log -``` - -## 📊 Understanding Output - -### Successful Flow (Low Risk) -``` -[AlertRouter] → [3 Analysts] → [Aggregator] -→ [AutoClear] → [Notification] ✅ -``` - -### Successful Flow (High Risk) -``` -[AlertRouter] → [3 Analysts] → [Aggregator] -→ [AnalystReview] ⏸️ → [FraudAction] → [Notification] ✅ -``` - -### Risk Score Interpretation -- **0.0 - 0.3**: Low risk (auto-clear) -- **0.3 - 0.6**: Medium risk (auto-clear by default) -- **0.6 - 0.8**: High risk (requires analyst review) -- **0.8 - 1.0**: Critical risk (requires analyst review) - -## 🔧 Quick Customizations - -### Add Custom Risk Indicator - -**Location**: `UsagePatternExecutor.handle_alert()` - -```python -# Add to prompt: -""" -Additional checks: -- Look for X pattern -- Check Y threshold -- Flag Z behavior -""" -``` - -### Change Analyst Instructions - -**Location**: `ReviewGatewayExecutor` class in `fraud_detection_workflow.py` - -```python -# The AnalystReviewRequest dataclass contains the request details -@dataclass -class AnalystReviewRequest: - alert_id: str - customer_id: int - risk_assessment: FraudRiskAssessment - instructions: str = "Review the risk assessment and decide on action" -``` - -### Modify Action Logic - -**Location**: `FraudActionExecutor.handle_decision()` - -```python -if "lock" in decision.approved_action: - # Add custom lock logic - custom_lock_account(decision) -``` - -## 📖 Learning Path - -### Beginner -1. ✅ Run the example as-is -2. ✅ Change risk threshold -3. ✅ Modify alert severity -4. ✅ Review logs to understand flow - -### Intermediate -1. ✅ Add custom risk indicators -2. ✅ Modify analyst instructions -3. ✅ Create custom alert types -4. ✅ Add fourth specialist agent - -### Advanced -1. ✅ Implement persistent checkpoint storage -2. ✅ Add real-time UI for analyst review -3. ✅ Integrate with external systems -4. ✅ Add OpenTelemetry tracing - -## 🎓 Key Concepts Checklist - -- [ ] Understand fan-out pattern (1 → N) -- [ ] Understand fan-in pattern (N → 1) -- [ ] Understand switch/case routing -- [ ] Understand human-in-the-loop -- [ ] Understand checkpointing -- [ ] Understand MCP tool filtering -- [ ] Understand LLM agent executors -- [ ] Understand type-safe messaging - -## 📞 Getting Help - -### Check Documentation -- [Full README](README.md) -- [Implementation Details](IMPLEMENTATION.md) -- [Workflow Architecture](../README.md) - -### Review Examples -- [Human-in-the-Loop](../human-in-the-loop.md) - -- DEAD LINK Control Flow ../control-flow/ - -- DEAD LINK Parallelism ../parallelism/ - -### Common Issues -See [Troubleshooting](#-troubleshooting) section above - ---- - -**Pro Tip**: Start with the default configuration and make one change at a time to understand each component's impact! diff --git a/agentic_ai/workflow/fraud_detection/README.md b/agentic_ai/workflow/fraud_detection/README.md deleted file mode 100644 index 9fa788cb0..000000000 --- a/agentic_ai/workflow/fraud_detection/README.md +++ /dev/null @@ -1,407 +0,0 @@ -# Contoso Fraud Detection & Escalation Workflow - -This example demonstrates a comprehensive fraud detection system using the Agent Framework's workflow capabilities. - -## Architecture Overview - -``` -┌─────────────────────────────────────────────────────────────────────┐ -│ Suspicious Activity Alert │ -│ (monitoring system trigger) │ -└────────────────────────────┬────────────────────────────────────────┘ - ↓ - ┌────────────────┐ - │ AlertRouter │ - └────────┬───────┘ - ↓ - ┌─────────────────┼─────────────────┐ - ↓ ↓ ↓ - ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ - │ Usage │ │ Location │ │ Billing │ - │ Pattern │ │ Analysis │ │ Charge │ - │ Executor │ │ Executor │ │ Executor │ - │ (MCP tools) │ │ (MCP tools) │ │ (MCP tools) │ - └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ - └─────────────────┼─────────────────┘ - ↓ - ┌────────────────┐ - │ Fraud Risk │ - │ Aggregator │ - │ (LLM Agent) │ - └────────┬───────┘ - ↓ - (Risk Score Switch) - ↓ - ┌────────────────┴────────────────┐ - ↓ ↓ - (High Risk ≥0.6) (Low Risk <0.6) - ↓ ↓ - ┌──────────────┐ ┌──────────────┐ - │ Review │ │ Auto Clear │ - │ Gateway │ │ Executor │ - │(Human Input) │ └──────┬───────┘ - └──────┬───────┘ │ - ↓ │ - ┌──────────────┐ │ - │ Fraud │ │ - │ Action │ │ - │ Executor │ │ - └──────┬───────┘ │ - └──────────────┬─────────────────┘ - ↓ - ┌────────────────┐ - │ Final │ - │ Notification │ - │ Executor │ - └────────────────┘ -``` - -## Key Features - -### 1. **Event-Driven Processing** -- Triggered by suspicious activity alerts from monitoring systems -- Supports multiple alert types: multi-country logins, data spikes, unusual charges - -### 2. **Fan-Out Pattern** -- Single alert routed to three specialist agents simultaneously -- Each agent uses filtered MCP tools for domain-specific analysis: - - **UsagePatternExecutor**: Analyzes data usage patterns - - **LocationAnalysisExecutor**: Checks geolocation anomalies - - **BillingChargeExecutor**: Reviews billing and charges - -### 3. **Fan-In Aggregation** -- **FraudRiskAggregatorExecutor** waits for all three analyses -- LLM-based agent synthesizes findings into single risk assessment -- Produces overall risk score (0.0-1.0) and recommended action - -### 4. **Switch/Case Routing** -- Routes based on risk score threshold: - - **High risk (≥0.6)**: Human analyst review required - - **Low risk (<0.6)**: Auto-clear - -### 5. **Human-in-the-Loop** -- Uses `ctx.request_info()` API for analyst review within `ReviewGatewayExecutor` -- Workflow pauses and creates checkpoint automatically -- Analyst provides decision (lock account, refund charges, clear, both) -- `@response_handler` decorator processes the response when workflow resumes - -### 6. **Checkpointing** -- Workflow state saved at each superstep -- Can pause indefinitely while waiting for analyst -- Resume from exact point after analyst decision - -### 7. **MCP Tool Integration** -- Each specialist agent has filtered access to relevant MCP tools -- Tools used: - - Usage: `get_customer_detail`, `get_subscription_detail`, `get_data_usage`, `search_knowledge_base` - - Location: `get_customer_detail`, `get_security_logs`, `search_knowledge_base` - - Billing: `get_customer_detail`, `get_billing_summary`, `get_subscription_detail`, `get_customer_orders`, `search_knowledge_base` - -## Message Flow - -1. **SuspiciousActivityAlert** → AlertRouter -2. **SuspiciousActivityAlert** → [UsagePattern, Location, Billing] (fan-out) -3. **[UsageAnalysisResult, LocationAnalysisResult, BillingAnalysisResult]** → Aggregator (fan-in) -4. **FraudRiskAssessment** → Switch (risk score check) -5. **FraudRiskAssessment** → ReviewGateway OR AutoClear -6. **AnalystDecision** → FraudAction (if high risk, after human review) -7. **ActionResult** → FinalNotification -8. **FinalNotification** → Workflow output - -## Setup - -### Prerequisites - -1. **Azure OpenAI Configuration**: - ```bash - export AZURE_OPENAI_API_KEY="your-key" - export AZURE_OPENAI_CHAT_DEPLOYMENT="your-deployment" - export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/" - export AZURE_OPENAI_API_VERSION="2024-10-01-preview" - ``` - -2. **MCP Server**: - ```bash - export MCP_SERVER_URI="http://localhost:8000/mcp" - ``` - -3. **Start MCP Server** (in separate terminal): - ```bash - cd mcp - python mcp_service.py - ``` - -### Running the Workflow with command line - -```bash -cd agentic_ai/workflow/fraud_detection -python fraud_detection_workflow.py -``` - -## Sample Alerts - -The example includes three sample alerts: - -### Alert 1: Multi-Country Login (High Severity) -```python -alert_id: "ALERT-001" -customer_id: 1 -alert_type: "multi_country_login" -description: "Login attempts from USA and Russia within 2 hours" -severity: "high" -``` - -### Alert 2: Data Spike (Medium Severity) -```python -alert_id: "ALERT-002" -customer_id: 2 -alert_type: "data_spike" -description: "Data usage increased by 500% in last 24 hours" -severity: "medium" -``` - -### Alert 3: Unusual Charges (High Severity) -```python -alert_id: "ALERT-003" -customer_id: 3 -alert_type: "unusual_charges" -description: "Three large purchases totaling $5,000 in 10 minutes" -severity: "high" -``` - -## Expected Output - -``` -================================================================================ -Contoso Fraud Detection & Escalation Workflow -================================================================================ -[Setup] Connecting to MCP server at http://localhost:8000 -[Setup] Connected to MCP server, loaded 20 tools -[Workflow] Fraud detection workflow built successfully - -================================================================================ -Processing Alert: ALERT-001 -================================================================================ -[Workflow] Starting fraud detection workflow... -[AlertRouter] Processing alert ALERT-001 for customer 1 -[AlertRouter] Alert routed to 3 analysis executors -[UsagePatternExecutor] Analyzing alert ALERT-001 -[LocationAnalysisExecutor] Analyzing alert ALERT-001 -[BillingChargeExecutor] Analyzing alert ALERT-001 -[UsagePatternExecutor] Sent analysis result (risk_score=0.7) -[LocationAnalysisExecutor] Sent analysis result (risk_score=0.85) -[BillingChargeExecutor] Sent analysis result (risk_score=0.4) -[FraudRiskAggregator] Aggregating 3 analysis results -[FraudRiskAggregator] Sent assessment (risk_score=0.65, action=lock_account) - -================================================================================ -ANALYST REVIEW REQUIRED -================================================================================ -Request: {'type': 'fraud_analyst_review', ...} - -Simulating analyst decision... -[Analyst] Decision: lock_account -[FraudAction] Executing action: lock_account for alert ALERT-001 -[FraudAction] Action executed for alert ALERT-001 -[FinalNotification] Alert ALERT-001 completed and logged - -================================================================================ -Workflow completed successfully! -================================================================================ -``` - -## Customization - -### Adjust Risk Threshold - -Change the risk threshold in the switch/case edge: - -```python -# Current: 0.6 threshold -(lambda assessment: assessment.overall_risk_score >= 0.6, analyst_review) - -# Example: 0.5 threshold (more sensitive) -(lambda assessment: assessment.overall_risk_score >= 0.5, analyst_review) -``` - -### Add Custom Analysis - -Create a new executor: - -```python -class CustomAnalysisExecutor(Executor): - async def handle_alert( - self, ctx: WorkflowContext[CustomAnalysisResult], alert: SuspiciousActivityAlert - ) -> None: - # Your analysis logic - pass -``` - -Add to workflow: - -```python -builder.add_executor(custom_executor) -builder.add_edge(alert_router, custom_executor) -builder.add_fan_in_edge([usage, location, billing, custom_executor], aggregator) -``` - -### Modify Agent Instructions - -Adjust agent instructions in executor constructors: - -```python -instructions=( - "Your custom instructions here..." -) -``` - -## Key Concepts Demonstrated - -1. **Fan-Out Pattern**: One message → multiple executors -2. **Fan-In Pattern**: Multiple messages → one executor (waits for all) -3. **Switch/Case Routing**: Conditional routing based on message content -4. **Human-in-the-Loop**: Workflow pause/resume with `ctx.request_info()` and `@response_handler` -5. **Checkpointing**: Persistent workflow state across restarts -6. **MCP Tool Integration**: Domain-specific tool filtering -7. **LLM Agent Executors**: AI-powered decision making -8. **Type-Safe Messaging**: Pydantic models for all messages -9. **Event Streaming**: Real-time workflow event monitoring - - -## Real-Time Workflow Visualizer UI - -### Overview - -A modern React + FastAPI dashboard for real-time workflow visualization and analyst interaction. - -**Features:** -- 🎯 Interactive workflow graph (React Flow) -- 📊 Real-time executor status updates -- 💬 Live event stream with WebSocket -- 🎨 Professional Material-UI design -- 👤 Human-in-the-loop analyst panel - -### Architecture - -``` -┌─────────────────────┐ ┌──────────────────────┐ -│ React Frontend │◄────────┤ FastAPI Backend │ -│ (Port 3000) │ WebSocket│ (Port 8001) │ -│ │◄────────┤ │ -│ - React Flow │ HTTP │ - Workflow Engine │ -│ - Material-UI │ │ - Event Streaming │ -│ - WebSocket Client │ │ - Azure OpenAI │ -└─────────────────────┘ └──────────────────────┘ - │ - ▼ - ┌──────────────────┐ - │ MCP Server │ - │ (Port 8000) │ - └──────────────────┘ -``` - -### Quick Start - -**1. Install Frontend Dependencies:** -```bash -cd ui -npm install -``` - -**2. Start All Services (3 terminals):** - -Terminal 1 - MCP Server: -```bash -cd mcp -uv run mcp_service.py -``` - -Terminal 2 - FastAPI Backend: -```bash -cd agentic_ai/workflow/fraud_detection -uv run --prerelease allow backend.py -``` - -Terminal 3 - React Frontend: -```bash -cd agentic_ai/workflow/fraud_detection/ui -npm run dev -``` - -**3. Open Browser:** -``` -http://localhost:3000 -``` - -### Using the UI - -1. **Select Alert**: Choose from 3 sample alerts (ALERT-001, ALERT-002, ALERT-003) -2. **Start Workflow**: Click button to begin processing -3. **Watch Live Updates**: Nodes change color as executors run - - 🔵 Blue = Running - - 🟢 Green = Completed - - ⚪ Gray = Idle -4. **Analyst Review**: When high-risk fraud detected, review panel appears -5. **Submit Decision**: Choose action and add notes -6. **Monitor Events**: Right panel shows complete event stream - -### API Endpoints - -**REST API:** -- `GET /api/alerts` - List sample alerts -- `POST /api/workflow/start` - Start workflow execution -- `POST /api/workflow/decision` - Submit analyst decision -- `GET /api/workflow/status/{alert_id}` - Get current status - -**WebSocket:** -- `WS /ws` - Real-time event stream - -### Technology Stack - -**Frontend:** - -- React 19.2 + Vite 7.2 -- React Flow 11.11 (workflow visualization) -- Material-UI 7.3 (components) -- WebSocket (real-time updates) - -**Backend:** - -- FastAPI (async web framework) -- Agent Framework (workflow engine) -- Pydantic (data validation) -- WebSocket (real-time events) - -### Troubleshooting - -**WebSocket Connection Failed:** - -- Ensure backend is running on port 8001 -- Check browser console for errors - -**Workflow Not Starting:** - -- Verify MCP server is running (port 8000) -- Check Azure OpenAI credentials in .env -- Check backend logs - -**UI Not Updating:** - -- Check WebSocket connection in DevTools -- Verify events are being sent from backend - -**Checkpoint Encoding Error (Windows):** - -- If you see `'charmap' codec can't encode character` errors, the backend uses `UTF8FileCheckpointStorage` wrapper to handle Unicode characters in LLM output -- Ensure you're using the latest backend.py which includes this fix - -**Analyst Decision Not Resuming Workflow:** - -- Check that the checkpoint was created successfully (no encoding errors in logs) -- Verify the `request_id` matches between the decision and the pending request -- Check backend logs for `continue_workflow` messages - - -## License - -Copyright (c) Microsoft. All rights reserved. diff --git a/agentic_ai/workflow/fraud_detection/backend.py b/agentic_ai/workflow/fraud_detection/backend.py deleted file mode 100644 index 2f64da70e..000000000 --- a/agentic_ai/workflow/fraud_detection/backend.py +++ /dev/null @@ -1,1176 +0,0 @@ -""" -FastAPI backend for Fraud Detection Workflow Visualization. - -Provides REST API endpoints and WebSocket for real-time workflow event streaming. -""" - -import asyncio -import logging -import os -import sys -from datetime import datetime -from pathlib import Path -from typing import Any - -from dotenv import load_dotenv - -# Load environment first so observability can read connection string -load_dotenv() - -# ------------------------------------------------------------------ -# Observability (must be before any agent imports) -# ------------------------------------------------------------------ -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) # agentic_ai/ - -try: - from observability import setup_observability - _observability_enabled = setup_observability( - service_name="contoso-fraud-detection", - enable_live_metrics=True, - enable_sensitive_data=os.getenv("ENABLE_SENSITIVE_DATA", "false").lower() in ("1", "true", "yes"), - ) -except ImportError: - _observability_enabled = False - -# ------------------------------------------------------------------ -from fastapi import FastAPI, WebSocket, WebSocketDisconnect, Request, status -from fastapi.middleware.cors import CORSMiddleware -from fastapi.exceptions import RequestValidationError -from fastapi.responses import JSONResponse -from pydantic import BaseModel - -from fraud_detection_workflow import ( - AnalystDecision, - SuspiciousActivityAlert, - create_fraud_detection_workflow, -) -from agent_framework import ( - FileCheckpointStorage, - MCPStreamableHTTPTool, - ExecutorInvokedEvent, - ExecutorCompletedEvent, - WorkflowOutputEvent, - WorkflowStatusEvent, - RequestInfoEvent, - SuperStepCompletedEvent, - WorkflowCheckpoint, - get_checkpoint_summary, -) -from agent_framework.azure import AzureOpenAIChatClient -from azure.identity import AzureCliCredential -import json -from dataclasses import asdict - - -# ============================================================================ -# UTF-8 Checkpoint Storage Wrapper (fixes Windows encoding issues) -# ============================================================================ - -class UTF8FileCheckpointStorage: - """ - Wrapper around FileCheckpointStorage that ensures UTF-8 encoding. - - This fixes the Windows 'charmap' codec error when LLM output contains - Unicode characters (like combining diacritical marks) that can't be - encoded with the default cp1252 encoding. - """ - - def __init__(self, storage_path: str | Path): - """Initialize the file storage with UTF-8 encoding.""" - self.storage_path = Path(storage_path) - self.storage_path.mkdir(parents=True, exist_ok=True) - logger.info(f"Initialized UTF-8 file checkpoint storage at {self.storage_path}") - - async def save_checkpoint(self, checkpoint: WorkflowCheckpoint) -> str: - """Save a checkpoint with UTF-8 encoding and return its ID.""" - file_path = self.storage_path / f"{checkpoint.checkpoint_id}.json" - checkpoint_dict = asdict(checkpoint) - - def _write_atomic() -> None: - tmp_path = file_path.with_suffix(".json.tmp") - with open(tmp_path, "w", encoding="utf-8") as f: - json.dump(checkpoint_dict, f, indent=2, ensure_ascii=False) - os.replace(tmp_path, file_path) - - await asyncio.to_thread(_write_atomic) - logger.info(f"Saved checkpoint {checkpoint.checkpoint_id} to {file_path}") - return checkpoint.checkpoint_id - - async def load_checkpoint(self, checkpoint_id: str) -> WorkflowCheckpoint | None: - """Load a checkpoint by ID with UTF-8 encoding.""" - file_path = self.storage_path / f"{checkpoint_id}.json" - - if not file_path.exists(): - return None - - def _read() -> dict[str, Any]: - with open(file_path, encoding="utf-8") as f: - return json.load(f) - - checkpoint_dict = await asyncio.to_thread(_read) - checkpoint = WorkflowCheckpoint(**checkpoint_dict) - logger.info(f"Loaded checkpoint {checkpoint_id} from {file_path}") - return checkpoint - - async def list_checkpoint_ids(self, workflow_id: str | None = None) -> list[str]: - """List checkpoint IDs with UTF-8 encoding.""" - def _list_ids() -> list[str]: - checkpoint_ids: list[str] = [] - for file_path in self.storage_path.glob("*.json"): - try: - with open(file_path, encoding="utf-8") as f: - data = json.load(f) - if workflow_id is None or data.get("workflow_id") == workflow_id: - checkpoint_ids.append(data.get("checkpoint_id", file_path.stem)) - except Exception as e: - logger.warning(f"Failed to read checkpoint file {file_path}: {e}") - return checkpoint_ids - - return await asyncio.to_thread(_list_ids) - - async def list_checkpoints(self, workflow_id: str | None = None) -> list[WorkflowCheckpoint]: - """List checkpoint objects with UTF-8 encoding.""" - def _list_checkpoints() -> list[WorkflowCheckpoint]: - checkpoints: list[WorkflowCheckpoint] = [] - for file_path in self.storage_path.glob("*.json"): - try: - with open(file_path, encoding="utf-8") as f: - data = json.load(f) - if workflow_id is None or data.get("workflow_id") == workflow_id: - checkpoints.append(WorkflowCheckpoint.from_dict(data)) - except Exception as e: - logger.warning(f"Failed to read checkpoint file {file_path}: {e}") - return checkpoints - - return await asyncio.to_thread(_list_checkpoints) - - async def delete_checkpoint(self, checkpoint_id: str) -> bool: - """Delete a checkpoint by ID.""" - file_path = self.storage_path / f"{checkpoint_id}.json" - - def _delete() -> bool: - if file_path.exists(): - file_path.unlink() - logger.info(f"Deleted checkpoint {checkpoint_id} from {file_path}") - return True - return False - - return await asyncio.to_thread(_delete) - -# Configure logging -logging.basicConfig(level=logging.INFO, force=True) -logger = logging.getLogger(__name__) -if _observability_enabled: - logger.info("✅ Application Insights observability enabled for fraud detection workflow") - -# Keep agent_framework at INFO level -logging.getLogger("agent_framework").setLevel(logging.INFO) - -# FastAPI app -app = FastAPI(title="Fraud Detection Workflow API") - -# CORS middleware -app.add_middleware( - CORSMiddleware, - allow_origins=["http://localhost:3000", "http://localhost:5173"], # React dev servers - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) - - -# ============================================================================ -# Exception Handlers -# ============================================================================ - - -@app.exception_handler(RequestValidationError) -async def validation_exception_handler(request: Request, exc: RequestValidationError): - """Log validation errors for debugging.""" - try: - body = await request.body() - body_str = body.decode('utf-8') - except Exception: - body_str = "" - - logger.error(f"Validation error on {request.url}: {exc.errors()}") - logger.error(f"Request body: {body_str}") - return JSONResponse( - status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, - content={"detail": exc.errors(), "body": body_str}, - ) - - -# ============================================================================ -# Request/Response Models -# ============================================================================ - - -class StartWorkflowRequest(BaseModel): - """Request to start a workflow with a specific alert.""" - - alert_id: str - customer_id: int - alert_type: str - description: str - severity: str - - -class AnalystDecisionRequest(BaseModel): - """Analyst decision submitted from UI.""" - - request_id: str - alert_id: str | None = None # Optional - tracked via request_id - customer_id: int | str | None = None # Optional - tracked via request_id - approved_action: str - analyst_notes: str - analyst_id: str = "analyst_ui" - - -class WorkflowStatus(BaseModel): - """Current workflow status.""" - - status: str - current_executor: str | None = None - pending_requests: list[dict[str, Any]] = [] - - -# ============================================================================ -# Global State -# ============================================================================ - -# Store active workflows and their states -active_workflows: dict[str, Any] = {} # Stores workflow state AND the workflow instance -workflow_events: dict[str, list[dict[str, Any]]] = {} -pending_decisions: dict[str, dict[str, Any]] = {} -pending_request_events: dict[str, RequestInfoEvent] = {} - -# WebSocket connections -active_connections: list[WebSocket] = [] - -# Pre-initialized resources (created once on startup) -mcp_tool: MCPStreamableHTTPTool | None = None -chat_client: AzureOpenAIChatClient | None = None -checkpoint_storage: UTF8FileCheckpointStorage | None = None - - -# ============================================================================ -# WebSocket Connection Manager -# ============================================================================ - - -class ConnectionManager: - """Manages WebSocket connections for real-time event streaming.""" - - def __init__(self): - self.active_connections: list[WebSocket] = [] - - async def connect(self, websocket: WebSocket): - await websocket.accept() - self.active_connections.append(websocket) - logger.info(f"WebSocket connected. Total connections: {len(self.active_connections)}") - - def disconnect(self, websocket: WebSocket): - self.active_connections.remove(websocket) - logger.info(f"WebSocket disconnected. Total connections: {len(self.active_connections)}") - - async def broadcast(self, message: dict): - """Broadcast message to all connected clients.""" - msg_type = message.get('type', message.get('event_type', 'unknown')) - logger.info(f"[BROADCAST] Sending message type={msg_type} to {len(self.active_connections)} connections") - - if not self.active_connections: - logger.warning(f"No active WebSocket connections to broadcast to. Message type: {msg_type}") - return - - disconnected = [] - for connection in self.active_connections: - try: - await connection.send_json(message) - logger.info(f"[BROADCAST] Successfully sent {msg_type}") - except Exception as e: - logger.error(f"Error sending to WebSocket: {e}") - disconnected.append(connection) - - # Remove disconnected clients - for conn in disconnected: - self.disconnect(conn) - - -manager = ConnectionManager() - - -# ============================================================================ -# Sample Alerts -# ============================================================================ - -SAMPLE_ALERTS = { - "ALERT-001": SuspiciousActivityAlert( - alert_id="ALERT-001", - customer_id=1, - alert_type="multi_country_login", - description="Login attempts from USA and Russia within 2 hours", - timestamp=datetime.now().isoformat(), - severity="high", - ), - "ALERT-002": SuspiciousActivityAlert( - alert_id="ALERT-002", - customer_id=2, - alert_type="data_spike", - description="Data usage increased by 500% in last 24 hours", - timestamp=datetime.now().isoformat(), - severity="medium", - ), - "ALERT-003": SuspiciousActivityAlert( - alert_id="ALERT-003", - customer_id=3, - alert_type="unusual_charges", - description="Three large purchases totaling $5,000 in 10 minutes", - timestamp=datetime.now().isoformat(), - severity="high", - ), - "ALERT-004": SuspiciousActivityAlert( - alert_id="ALERT-004", - customer_id=4, - alert_type="routine_check", - description="Routine security check - password changed from usual device", - timestamp=datetime.now().isoformat(), - severity="low", - ), -} - - -def _serialize_analyst_request(event: RequestInfoEvent) -> dict[str, Any]: - """Convert a RequestInfoEvent into a UI-friendly payload.""" - from dataclasses import is_dataclass, asdict - - request_data = getattr(event, "data", None) - assessment = getattr(request_data, "assessment", None) - - def serialize_assessment(data: Any) -> dict[str, Any]: - if data is None: - return {} - # Handle dataclasses first (FraudRiskAssessment is a dataclass) - if is_dataclass(data): - return asdict(data) - # Handle Pydantic models - if hasattr(data, "model_dump"): - return data.model_dump() - if hasattr(data, "dict"): - return data.dict() - return {} - - assessment_payload = serialize_assessment(assessment) - - return { - "request_id": event.request_id, - "alert_id": assessment_payload.get("alert_id") or getattr(request_data, "alert_id", None), - "customer_id": assessment_payload.get("customer_id"), - "risk_score": assessment_payload.get("overall_risk_score", 0.0), - "risk_level": assessment_payload.get("risk_level"), - "recommended_action": assessment_payload.get("recommended_action"), - "reasoning": assessment_payload.get("reasoning", ""), - "analysis_summaries": assessment_payload.get("analysis_summaries", []), - "prompt": getattr(request_data, "prompt", ""), - } - - -async def cleanup_checkpoints(workflow_id: str | None): - """Clean up checkpoint files for a completed workflow.""" - if not checkpoint_storage or not workflow_id: - return - - try: - import pathlib - checkpoint_dir = pathlib.Path("./checkpoints") - if not checkpoint_dir.exists(): - return - - # List all checkpoints for this workflow - checkpoints = await checkpoint_storage.list_checkpoints(workflow_id=workflow_id) - deleted_count = 0 - - for checkpoint in checkpoints: - try: - checkpoint_file = checkpoint_dir / f"{checkpoint.checkpoint_id}.json" - if checkpoint_file.exists(): - checkpoint_file.unlink() - deleted_count += 1 - except Exception as e: - logger.warning(f"Failed to delete checkpoint {checkpoint.checkpoint_id}: {e}") - - if deleted_count > 0: - logger.info(f"Cleaned up {deleted_count} checkpoint file(s) for workflow {workflow_id}") - except Exception as e: - logger.error(f"Error cleaning up checkpoints: {e}") - - -async def _resolve_checkpoint_for_request(alert_id: str, request_id: str) -> tuple[str | None, int | None]: - """Locate the checkpoint that contains the pending request.""" - - if checkpoint_storage is None: - return None, None - - workflow_state = active_workflows.get(alert_id) - if not workflow_state: - return None, None - - workflow_id = workflow_state.get("workflow_id") - if not workflow_id: - return None, None - - async def _lookup() -> tuple[str | None, int | None]: - try: - checkpoints = await checkpoint_storage.list_checkpoints(workflow_id=workflow_id) - except Exception as exc: # pragma: no cover - defensive logging - logger.warning( - "Failed to list checkpoints for alert %s (workflow_id=%s): %s", - alert_id, - workflow_id, - exc, - ) - return None, None - - checkpoints_sorted = sorted( - checkpoints, - key=lambda cp: getattr(cp, "timestamp", "") or "", - reverse=True, - ) - - for checkpoint in checkpoints_sorted: - try: - # Use the new get_checkpoint_summary API instead of RequestInfoExecutor - summary = get_checkpoint_summary(checkpoint) - pending_events = summary.pending_request_info_events - except Exception as exc: # pragma: no cover - defensive logging - logger.debug( - "Unable to inspect checkpoint %s for alert %s: %s", - getattr(checkpoint, "checkpoint_id", ""), - alert_id, - exc, - ) - continue - - for pending in pending_events: - if pending.request_id == request_id: - return checkpoint.checkpoint_id, checkpoint.iteration_count - - return None, None - - # Retry multiple times in case the checkpoint is written just after the event - # The checkpoint is written AFTER the superstep completes, so we need to wait - max_attempts = 20 # Increased from 10 - for attempt in range(max_attempts): - checkpoint_id, iteration = await _lookup() - if checkpoint_id: - logger.info( - "Found checkpoint %s for alert %s request %s after %d attempt(s)", - checkpoint_id, - alert_id, - request_id, - attempt + 1, - ) - return checkpoint_id, iteration - if attempt < max_attempts - 1: - # Exponential backoff: 0.5s, 1s, 1.5s, 2s, 2.5s, etc. (max ~10 seconds total) - await asyncio.sleep(0.5 * (attempt + 1)) - if attempt % 5 == 4: # Log every 5 attempts - logger.debug( - "Still waiting for checkpoint for alert %s request %s (attempt %d/%d)", - alert_id, - request_id, - attempt + 1, - max_attempts, - ) - - logger.warning( - "Checkpoint not found for alert %s request %s (workflow_id=%s) after %s attempts (~%.1f seconds)", - alert_id, - request_id, - workflow_state.get("workflow_id"), - max_attempts, - sum(0.5 * (i + 1) for i in range(max_attempts)), - ) - return None, None - - -# ============================================================================ -# API Endpoints -# ============================================================================ - - -@app.get("/") -async def root(): - """Root endpoint.""" - return {"message": "Fraud Detection Workflow API", "status": "running"} - - -@app.get("/api/alerts") -async def get_sample_alerts(): - """Get list of sample alerts.""" - return { - "alerts": [ - { - "alert_id": alert.alert_id, - "customer_id": alert.customer_id, - "alert_type": alert.alert_type, - "description": alert.description, - "severity": alert.severity, - } - for alert in SAMPLE_ALERTS.values() - ] - } - - -@app.post("/api/workflow/start") -async def start_workflow(request: StartWorkflowRequest): - """Start a new workflow with the specified alert.""" - alert_id = request.alert_id - - # Check if alert exists in samples or create new one - if alert_id in SAMPLE_ALERTS: - alert = SAMPLE_ALERTS[alert_id] - else: - alert = SuspiciousActivityAlert( - alert_id=alert_id, - customer_id=request.customer_id, - alert_type=request.alert_type, - description=request.description, - timestamp=datetime.now().isoformat(), - severity=request.severity, - ) - - # Initialize workflow events storage - workflow_events[alert_id] = [] - pending_decisions[alert_id] = {} - pending_request_events.pop(alert_id, None) - - # Start workflow in background - asyncio.create_task(run_workflow(alert)) - - return {"status": "started", "alert_id": alert_id, "message": "Workflow started successfully"} - - -@app.post("/api/workflow/decision") -async def submit_decision(decision: AnalystDecisionRequest): - """Submit analyst decision for pending review.""" - logger.info(f"Received decision request: {decision}") - request_id = decision.request_id - - # Find the alert_id for this request_id - alert_id = None - for aid, pending_info in pending_decisions.items(): - if pending_info.get("request_id") == request_id: - alert_id = aid - break - - if not alert_id: - logger.error(f"No pending decision found for request_id {request_id}") - logger.error(f"Available pending decisions: {list(pending_decisions.keys())}") - return {"status": "error", "message": f"No pending decision found for request {request_id}"} - - if alert_id not in active_workflows: - return {"status": "error", "message": f"No active workflow for alert {alert_id}"} - - pending_info = pending_decisions.get(alert_id) - pending_event = pending_request_events.get(alert_id) - if not pending_info: - return {"status": "error", "message": f"No pending analyst review for alert {alert_id}"} - - checkpoint_id = pending_info.get("checkpoint_id") - if not checkpoint_id: - checkpoint_id, iteration = await _resolve_checkpoint_for_request(alert_id, request_id) - if checkpoint_id: - pending_info["checkpoint_id"] = checkpoint_id - pending_info["checkpoint_iteration"] = iteration - workflow_state = active_workflows.get(alert_id) - if workflow_state is not None: - workflow_state["pending_checkpoint_id"] = checkpoint_id - workflow_state["last_checkpoint_id"] = checkpoint_id - else: - checkpoint_id = None - - checkpoint_id = pending_info.get("checkpoint_id") - - if not checkpoint_id: - return { - "status": "error", - "message": ( - "No checkpoint available for this decision yet. Please wait a moment and try again, or restart the workflow." - ), - } - - # Normalize customer_id to int - customer_id = decision.customer_id or pending_info.get("customer_id") or 0 - if isinstance(customer_id, str): - try: - customer_id = int(customer_id) - except (ValueError, TypeError): - customer_id = 0 - - # Create AnalystDecision object - analyst_decision = AnalystDecision( - alert_id=alert_id, - customer_id=customer_id, - approved_action=decision.approved_action, - analyst_notes=decision.analyst_notes, - analyst_id=decision.analyst_id, - ) - - responses = {request_id: analyst_decision} - - # Continue workflow execution - logger.info(f"About to continue workflow for alert {alert_id} with checkpoint {checkpoint_id}") - logger.info(f"Request ID for response: {request_id}") - logger.info(f"Analyst decision: {analyst_decision}") - logger.info(f"Responses dict keys: {list(responses.keys())}") - task = asyncio.create_task(continue_workflow(alert_id, responses, checkpoint_id=checkpoint_id)) - - # Add error handler to catch exceptions in the background task - def task_done_callback(t): - try: - t.result() - except Exception as e: - logger.error(f"Error in continue_workflow task: {e}", exc_info=True) - - task.add_done_callback(task_done_callback) - - return {"status": "submitted", "message": "Decision submitted successfully"} - - -@app.get("/api/workflow/status/{alert_id}") -async def get_workflow_status(alert_id: str): - """Get current workflow status.""" - if alert_id not in active_workflows: - return {"status": "not_found", "message": f"No workflow found for alert {alert_id}"} - - workflow_state = active_workflows[alert_id] - return { - "status": workflow_state.get("status", "unknown"), - "current_executor": workflow_state.get("current_executor"), - "events": workflow_events.get(alert_id, []), - "pending_decision": pending_decisions.get(alert_id), - "pending_checkpoint_id": workflow_state.get("pending_checkpoint_id"), - "last_checkpoint_id": workflow_state.get("last_checkpoint_id"), - "workflow_id": workflow_state.get("workflow_id"), - } - - -# ============================================================================ -# WebSocket Endpoint -# ============================================================================ - - -@app.websocket("/ws") -async def websocket_endpoint(websocket: WebSocket): - """WebSocket endpoint for real-time event streaming.""" - await manager.connect(websocket) - try: - while True: - # Keep connection alive and receive any messages from client - data = await websocket.receive_text() - logger.info(f"Received from client: {data}") - except WebSocketDisconnect: - manager.disconnect(websocket) - - -# ============================================================================ -# Workflow Execution -# ============================================================================ - - -async def run_workflow(alert: SuspiciousActivityAlert): - """Run the fraud detection workflow and stream events.""" - alert_id = alert.alert_id - - try: - # Broadcast workflow initialization started - await manager.broadcast( - { - "type": "workflow_initializing", - "alert_id": alert_id, - "message": "Initializing workflow...", - "timestamp": datetime.now().isoformat(), - } - ) - - logger.info(f"Creating workflow for alert {alert_id}...") - - # Send periodic updates to keep connection alive during initialization - async def send_progress_updates(): - messages = [ - "Creating workflow graph...", - "Initializing executors...", - "Setting up agent tools...", - "Workflow ready!" - ] - for i, msg in enumerate(messages): - await asyncio.sleep(2) # Wait 2 seconds between updates - await manager.broadcast({ - "type": "workflow_initializing", - "alert_id": alert_id, - "message": msg, - "progress": (i + 1) / len(messages) * 100, - "timestamp": datetime.now().isoformat(), - }) - - # Create workflow using pre-initialized resources (this takes time!) - progress_task = asyncio.create_task(send_progress_updates()) - - try: - workflow = await create_fraud_detection_workflow( - mcp_tool=mcp_tool, - chat_client=chat_client, - model=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4"), - checkpoint_storage=checkpoint_storage, - ) - progress_task.cancel() # Stop progress updates - except asyncio.CancelledError: - pass - - logger.info(f"✓ Workflow created for alert {alert_id}") - - # Broadcast workflow ready - await manager.broadcast( - { - "type": "workflow_started", - "alert_id": alert_id, - "message": "Workflow ready, starting execution...", - "timestamp": datetime.now().isoformat(), - } - ) - - # Store workflow reference AND instance - active_workflows[alert_id] = { - "status": "running", - "workflow_id": workflow.id, - "workflow_instance": workflow, # Store the workflow instance for reuse - "current_executor": None, - "pending_checkpoint_id": None, - "last_checkpoint_id": None, - } - - # Small delay to ensure WebSocket connection is fully established - await asyncio.sleep(0.1) - - # Track if we've seen a RequestInfoEvent (workflow is awaiting decision) - seen_request_info = False - request_info_event_data = None - - # Run workflow and stream events - async for event in workflow.run_stream(alert): - logger.info(f"[DEBUG] Received event: {type(event).__name__}") - await process_event(alert_id, event) - - # Check for human-in-the-loop request - if isinstance(event, RequestInfoEvent): - logger.info(f"[DEBUG] RequestInfoEvent detected! request_id={event.request_id}, source={event.source_executor_id}") - request_payload = _serialize_analyst_request(event) - logger.info(f"[DEBUG] Serialized request payload keys: {list(request_payload.keys())}") - - timestamp = datetime.now().isoformat() - - # Store the pending decision immediately - checkpoint will be resolved later - pending_decisions[alert_id] = { - **request_payload, - "timestamp": timestamp, - "source_executor_id": event.source_executor_id, - "checkpoint_id": None, # Will be resolved after superstep completes - "checkpoint_iteration": None, - } - pending_request_events[alert_id] = event - active_workflows[alert_id]["status"] = "awaiting_decision" - - # Broadcast decision_required immediately - don't wait for checkpoint - await manager.broadcast( - { - "type": "decision_required", - "alert_id": alert_id, - "request_id": event.request_id, - "data": request_payload, - "checkpoint_id": None, # Will be resolved after superstep completes - "checkpoint_iteration": None, - "timestamp": timestamp, - } - ) - - logger.info(f"[DEBUG] Broadcast decision_required for {alert_id}") - logger.info(f"Workflow {alert_id} awaiting analyst decision") - - # Mark that we've seen the request - continue processing to get checkpoint - seen_request_info = True - request_info_event_data = event - # DON'T return here - continue to let the superstep complete and checkpoint be created - - # After a superstep completes following a RequestInfoEvent, resolve the checkpoint - elif seen_request_info and isinstance(event, SuperStepCompletedEvent): - logger.info(f"[DEBUG] SuperStepCompleted after RequestInfoEvent - resolving checkpoint") - checkpoint_id, checkpoint_iteration = await _resolve_checkpoint_for_request( - alert_id, - request_info_event_data.request_id, - ) - logger.info(f"[DEBUG] Resolved checkpoint: id={checkpoint_id}, iteration={checkpoint_iteration}") - - # Update stored values with resolved checkpoint - if checkpoint_id: - pending_decisions[alert_id]["checkpoint_id"] = checkpoint_id - pending_decisions[alert_id]["checkpoint_iteration"] = checkpoint_iteration - active_workflows[alert_id]["pending_checkpoint_id"] = checkpoint_id - active_workflows[alert_id]["last_checkpoint_id"] = checkpoint_id - else: - logger.warning( - "No checkpoint recorded for alert %s request %s; resume will be unavailable until one is created.", - alert_id, - request_info_event_data.request_id, - ) - - # Now we can exit - workflow is paused awaiting decision - return - - # If we exit the loop naturally, mark as completed - if alert_id in active_workflows: - active_workflows[alert_id]["status"] = "completed" - active_workflows[alert_id]["current_executor"] = None - active_workflows[alert_id]["pending_checkpoint_id"] = None - pending_decisions.pop(alert_id, None) - pending_request_events.pop(alert_id, None) - - await manager.broadcast( - { - "type": "workflow_completed", - "alert_id": alert_id, - "timestamp": datetime.now().isoformat(), - } - ) - - logger.info(f"✓ Workflow completed for alert {alert_id}") - - # Clean up checkpoint files for this completed workflow - if alert_id in active_workflows: - await cleanup_checkpoints(active_workflows[alert_id].get("workflow_id")) - - except Exception as e: - logger.error(f"Error in workflow execution: {e}", exc_info=True) - await manager.broadcast( - { - "type": "workflow_error", - "alert_id": alert_id, - "error": str(e), - "timestamp": datetime.now().isoformat(), - } - ) - - -async def continue_workflow(alert_id: str, responses: dict[str, Any], checkpoint_id: str | None = None): - """Continue workflow execution after analyst decision.""" - logger.info(f"=== continue_workflow called for alert {alert_id} ===") - logger.info(f"Checkpoint ID: {checkpoint_id}") - logger.info(f"Responses: {responses}") - logger.info(f"Active workflows: {list(active_workflows.keys())}") - - if alert_id not in active_workflows: - logger.warning("Attempted to continue unknown workflow for alert %s", alert_id) - return - - workflow_state = active_workflows[alert_id] - logger.info(f"Workflow state: {workflow_state}") - pending_info = pending_decisions.get(alert_id) - effective_checkpoint_id = checkpoint_id or workflow_state.get("pending_checkpoint_id") - if not effective_checkpoint_id and pending_info: - effective_checkpoint_id = pending_info.get("checkpoint_id") - - if not effective_checkpoint_id: - logger.error("No checkpoint available to resume workflow for alert %s", alert_id) - await manager.broadcast( - { - "type": "workflow_error", - "alert_id": alert_id, - "error": "Missing checkpoint for analyst decision", - "timestamp": datetime.now().isoformat(), - } - ) - return - - # Clear pending state before resuming - pending_decisions.pop(alert_id, None) - pending_request_events.pop(alert_id, None) - workflow_state["pending_checkpoint_id"] = None - workflow_state["last_checkpoint_id"] = effective_checkpoint_id - - try: - # Create a NEW workflow instance for resuming (like the reference example) - # The checkpoint contains all the state needed to restore everything - logger.info(f"Creating new workflow instance for resume") - workflow = await create_fraud_detection_workflow( - mcp_tool=mcp_tool, - chat_client=chat_client, - model=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4"), - checkpoint_storage=checkpoint_storage, - ) - - workflow_state["workflow_instance"] = workflow - workflow_state.setdefault("workflow_id", workflow.id) - workflow_state["status"] = "running" - - # Debug: Check what's in the checkpoint using get_checkpoint_summary - checkpoint = await checkpoint_storage.load_checkpoint(effective_checkpoint_id) - if checkpoint: - summary = get_checkpoint_summary(checkpoint) - logger.info(f"Checkpoint status: {summary.status}") - logger.info(f"Pending request events: {len(summary.pending_request_info_events)}") - for pending in summary.pending_request_info_events: - logger.info(f" - Request ID: {pending.request_id}, Source: {pending.source_executor_id}") - - logger.info(f"Starting workflow resume with responses keys: {list(responses.keys())}") - logger.info(f"Checkpoint ID: {effective_checkpoint_id}") - - # Immediately broadcast review_gateway completion since the framework may not emit it - # The executor using ctx.request_info was waiting, and when we provide the response, it completes - # but this completion event is not always emitted in the event stream during resume - await manager.broadcast({ - "alert_id": alert_id, - "type": "ExecutorCompletedEvent", - "event_type": "executor_completed", - "executor_id": "review_gateway", - "timestamp": datetime.now().isoformat(), - }) - logger.info("Broadcast review_gateway completion event") - - # Track request info executors that completed during resume - completed_request_executors = set() - - # First, restore from checkpoint - logger.info(f"Restoring workflow from checkpoint {effective_checkpoint_id}") - async for event in workflow.run_stream( - checkpoint_id=effective_checkpoint_id, - checkpoint_storage=checkpoint_storage, - ): - logger.info(f"Restore event received: {type(event).__name__}") - await process_event(alert_id, event) - - # Now send the responses to continue the workflow - logger.info(f"Sending responses to continue workflow: {list(responses.keys())}") - async for event in workflow.send_responses_streaming(responses): - logger.info(f"Event received: {type(event).__name__}") - - # Track when review_gateway completes so we can re-broadcast at the end - if isinstance(event, ExecutorCompletedEvent) and event.executor_id == "review_gateway": - completed_request_executors.add(event.executor_id) - - await process_event(alert_id, event) - - if isinstance(event, RequestInfoEvent): - # Skip RequestInfoEvents that we already provided responses for - if event.request_id in responses: - logger.info(f"Skipping already-responded RequestInfoEvent {event.request_id}") - continue - request_payload = _serialize_analyst_request(event) - checkpoint_id_next, checkpoint_iteration = await _resolve_checkpoint_for_request( - alert_id, - event.request_id, - ) - - timestamp = datetime.now().isoformat() - pending_decisions[alert_id] = { - **request_payload, - "timestamp": timestamp, - "source_executor_id": event.source_executor_id, - "checkpoint_id": checkpoint_id_next, - "checkpoint_iteration": checkpoint_iteration, - } - pending_request_events[alert_id] = event - workflow_state["status"] = "awaiting_decision" - workflow_state["pending_checkpoint_id"] = checkpoint_id_next - if checkpoint_id_next: - workflow_state["last_checkpoint_id"] = checkpoint_id_next - else: - logger.warning( - "No checkpoint found after resuming workflow for alert %s request %s", - alert_id, - event.request_id, - ) - - await manager.broadcast( - { - "type": "decision_required", - "alert_id": alert_id, - "request_id": event.request_id, - "data": request_payload, - "checkpoint_id": checkpoint_id_next, - "checkpoint_iteration": checkpoint_iteration, - "timestamp": timestamp, - } - ) - - logger.info(f"Workflow {alert_id} awaiting additional analyst decision") - return - - # Workflow completed - logger.info(f"Workflow loop finished for alert {alert_id}") - logger.info(f"Pending decisions remaining: {list(pending_decisions.keys())}") - - # If workflow finished without another decision request, mark as completed - if alert_id not in pending_decisions: - workflow_state["status"] = "completed" - logger.info(f"Workflow {alert_id} completed successfully") - - # Small delay to ensure all events are sent to UI before completion message - await asyncio.sleep(0.5) - - # Broadcast completion to UI - await manager.broadcast( - { - "type": "workflow_completed", - "alert_id": alert_id, - "message": "Workflow completed successfully", - "timestamp": datetime.now().isoformat(), - } - ) - - # Clean up checkpoint files for this completed workflow - await cleanup_checkpoints(workflow_state.get("workflow_id")) - workflow_state["status"] = "awaiting_decision" - else: - workflow_state["status"] = "completed" - workflow_state["current_executor"] = None - - await manager.broadcast( - { - "type": "workflow_completed", - "alert_id": alert_id, - "timestamp": datetime.now().isoformat(), - } - ) - - logger.info(f"Workflow {alert_id} completed after resuming checkpoint {effective_checkpoint_id}") - - except Exception as e: - logger.error(f"Error continuing workflow: {e}", exc_info=True) - workflow_state["status"] = "error" - await manager.broadcast( - { - "type": "workflow_error", - "alert_id": alert_id, - "error": str(e), - "timestamp": datetime.now().isoformat(), - } - ) - - -async def process_event(alert_id: str, event: Any): - """Process and broadcast workflow event.""" - event_data = { - "alert_id": alert_id, - "type": event.__class__.__name__, - "timestamp": datetime.now().isoformat(), - } - - if isinstance(event, ExecutorInvokedEvent): - event_data.update( - { - "event_type": "executor_invoked", - "executor_id": event.executor_id, - } - ) - active_workflows[alert_id]["current_executor"] = event.executor_id - - elif isinstance(event, ExecutorCompletedEvent): - event_data.update( - { - "event_type": "executor_completed", - "executor_id": event.executor_id, - } - ) - logger.info(f"Broadcasting executor_completed for {event.executor_id}") - - elif isinstance(event, WorkflowStatusEvent): - event_data.update( - { - "event_type": "status_change", - "status": str(event.state) if hasattr(event, "state") else "unknown", - } - ) - - elif isinstance(event, WorkflowOutputEvent): - event_data.update( - { - "event_type": "workflow_output", - "output": str(event.data) if hasattr(event, "data") else None, - } - ) - - # Store event - if alert_id not in workflow_events: - workflow_events[alert_id] = [] - workflow_events[alert_id].append(event_data) - - # Broadcast to all connected clients - await manager.broadcast(event_data) - - -# ============================================================================ -# Startup/Shutdown Events -# ============================================================================ - - -@app.on_event("startup") -async def startup_event(): - """Initialize resources on startup.""" - global mcp_tool, chat_client, checkpoint_storage - - logger.info("Initializing backend resources...") - - try: - # Initialize MCP tool - mcp_server_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") - mcp_tool = MCPStreamableHTTPTool( - name="contoso_mcp", - url=mcp_server_uri, - headers={"Content-Type": "application/json"}, - timeout=30, - request_timeout=30, - ) - await mcp_tool.__aenter__() - logger.info(f"✓ MCP tool initialized at {mcp_server_uri}") - - # Initialize Azure OpenAI client - chat_client = AzureOpenAIChatClient(credential=AzureCliCredential(), deployment_name=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT")) - logger.info("✓ Azure OpenAI client initialized") - - # Initialize checkpoint storage with UTF-8 encoding wrapper - import pathlib - checkpoint_dir = pathlib.Path("./checkpoints") - checkpoint_dir.mkdir(parents=True, exist_ok=True) - checkpoint_storage = UTF8FileCheckpointStorage(str(checkpoint_dir)) - logger.info(f"✓ Checkpoint storage initialized at {checkpoint_dir.absolute()}") - - logger.info("Backend ready! 🚀") - - except Exception as e: - logger.error(f"Failed to initialize backend resources: {e}", exc_info=True) - raise - - -@app.on_event("shutdown") -async def shutdown_event(): - """Cleanup resources on shutdown.""" - global mcp_tool - - logger.info("Shutting down backend...") - - if mcp_tool: - try: - await mcp_tool.__aexit__(None, None, None) - logger.info("✓ MCP tool cleaned up") - except Exception as e: - logger.error(f"Error cleaning up MCP tool: {e}") - - -# ============================================================================ -# Main Entry Point -# ============================================================================ - -if __name__ == "__main__": - import uvicorn - - uvicorn.run(app, host="0.0.0.0", port=8001) diff --git a/agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py b/agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py deleted file mode 100644 index da36abf1e..000000000 --- a/agentic_ai/workflow/fraud_detection/fraud_detection_workflow.py +++ /dev/null @@ -1,1195 +0,0 @@ -# Copyright (c) Microsoft. All rights reserved. - -""" -Contoso Fraud Detection & Escalation Workflow - -Business Context: -Contoso's automated systems flag suspicious account activity, but certain cases require -a fraud analyst to review before taking action (e.g., locking account, reversing charges). - -Flow: -1. Event Trigger: Backend monitoring system sends "Suspicious Activity Alert" -2. AlertRouterExecutor → routes alert to multiple analysis executors: - - UsagePatternExecutor → checks recent usage against historical baseline (uses MCP tools) - - LocationAnalysisExecutor → checks geolocation anomalies (uses MCP tools) - - BillingChargeExecutor → checks for unusual purchases or charges (uses MCP tools) -3. Fan-In to FraudRiskAggregatorExecutor (LLM-based agent): - - Produces FraudRiskScore and recommended action (lock account, refund charges, ignore) -4. SwitchCaseEdgeRunner: - - If risk score ≥ threshold → route to ReviewGatewayExecutor for human fraud analyst review - - Else → route to AutoClearExecutor -5. ReviewGatewayExecutor → calls ctx.request_info() to request analyst decision, workflow pauses -6. Checkpoint saved — workflow awaits analyst response -7. Analyst decides (approve lock/refund or clear) -8. Workflow resumes: - - ReviewGatewayExecutor's @response_handler processes analyst decision - - FraudActionExecutor → performs chosen action (e.g., lock account, reverse charges) -9. FinalNotificationExecutor → informs customer and logs audit trail - -Demonstrates: -- Event-driven workflow triggered by backend system -- Fan-out pattern to multiple specialist agents with MCP tools -- Fan-in aggregation to produce single risk assessment -- LLM-based risk scoring -- Human-in-the-loop via ctx.request_info() and @response_handler -- Checkpointing for workflow pause/resume -""" - -import asyncio -import logging -import os -from dataclasses import dataclass, field -from datetime import datetime -from typing import Any -from dotenv import load_dotenv - -load_dotenv() - -from agent_framework import ( - Case, - ChatAgent, - Default, - Executor, - ExecutorCompletedEvent, - ExecutorInvokedEvent, - FileCheckpointStorage, - MCPStreamableHTTPTool, - RequestInfoEvent, - WorkflowBuilder, - WorkflowContext, - WorkflowOutputEvent, - WorkflowStatusEvent, - handler, - response_handler, -) -from agent_framework.azure import AzureOpenAIChatClient -from pydantic import BaseModel, Field - -# Configure logging -logging.basicConfig(force=True, level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") -logger = logging.getLogger(__name__) - - -# ============================================================================ -# Message Models -# NOTE: Using dataclasses instead of Pydantic for checkpoint compatibility -# The framework's checkpoint serialization doesn't support Pydantic v2's -# model_dump()/model_dump_json() methods yet (it looks for to_dict/to_json). -# ============================================================================ - - -@dataclass -class SuspiciousActivityAlert: - """Initial alert from monitoring system.""" - - alert_id: str - customer_id: int - alert_type: str # "multi_country_login", "data_spike", "unusual_charges" - description: str - timestamp: str - severity: str # "low", "medium", "high" - - -@dataclass -class UsageAnalysisResult: - """Result from usage pattern analysis.""" - - alert_id: str - customer_id: int - analysis_type: str = "usage_pattern" - findings: str = "" - risk_indicators: list[str] = field(default_factory=list) - risk_score: float = 0.5 # 0-1 risk score - - -@dataclass -class LocationAnalysisResult: - """Result from location analysis.""" - - alert_id: str - customer_id: int - analysis_type: str = "location" - findings: str = "" - risk_indicators: list[str] = field(default_factory=list) - risk_score: float = 0.5 # 0-1 risk score - - -@dataclass -class BillingAnalysisResult: - """Result from billing charge analysis.""" - - alert_id: str - customer_id: int - analysis_type: str = "billing" - findings: str = "" - risk_indicators: list[str] = field(default_factory=list) - risk_score: float = 0.5 # 0-1 risk score - - -@dataclass -class FraudRiskAssessment: - """Aggregated fraud risk assessment.""" - - alert_id: str - customer_id: int - overall_risk_score: float # 0-1 risk score - risk_level: str # "low", "medium", "high", "critical" - recommended_action: str # "clear", "lock_account", "refund_charges", "both" - reasoning: str - analysis_summaries: list[str] = field(default_factory=list) - - -@dataclass -class AnalystReviewRequest: - """Request for analyst review - used with ctx.request_info().""" - - assessment: FraudRiskAssessment | None = None - prompt: str = "" - - -@dataclass -class AnalystDecision: - """Decision from human fraud analyst.""" - - alert_id: str - customer_id: int - approved_action: str # "clear", "lock_account", "refund_charges", "both" - analyst_notes: str - analyst_id: str - - -@dataclass -class ActionResult: - """Result of fraud action execution.""" - - alert_id: str - customer_id: int - action_taken: str - success: bool - details: str - timestamp: str - - -@dataclass -class FinalNotification: - """Final notification to customer and audit log.""" - - alert_id: str - customer_id: int - resolution: str - customer_notified: bool - audit_logged: bool - - -# ============================================================================ -# Executors -# ============================================================================ - - -class AlertRouterExecutor(Executor): - """Routes suspicious activity alerts to multiple analysis executors (fan-out).""" - - def __init__(self, id: str = "alert_router") -> None: - super().__init__(id=id) - - @handler - async def handle_alert( - self, - alert: SuspiciousActivityAlert, - ctx: WorkflowContext[SuspiciousActivityAlert], - ) -> None: - try: - logger.info(f"[AlertRouter] Processing alert {alert.alert_id} for customer {alert.customer_id}") - logger.info(f"[AlertRouter] Alert type: {alert.alert_type}, Severity: {alert.severity}") - - print(f"\n[DEBUG] AlertRouter: Fan-out alert to all connected analysts") - print(f"[DEBUG] Alert data: {alert}") - print(f"[DEBUG] Context type: {type(ctx)}") - - # Fan-out: Send the same alert to all three analysis executors - # The add_fan_out_edges in the workflow builder will automatically route this - # message to all connected downstream executors - result = await ctx.send_message(alert) - print(f"[DEBUG] send_message result: {result}") - - logger.info(f"[AlertRouter] Alert {alert.alert_id} sent for fan-out routing") - print(f"[DEBUG] AlertRouter: Message sent (fan-out edges will handle distribution)") - except Exception as e: - print(f"[ERROR] Exception in AlertRouter: {e}") - import traceback - traceback.print_exc() - raise - - -class UsagePatternExecutor(Executor): - """Analyzes usage patterns using MCP tools.""" - - def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient, model: str, id: str = "usage_pattern_executor") -> None: - - super().__init__(id=id) - self._agent: ChatAgent | None = None - self._mcp_tool = mcp_tool - self._chat_client = chat_client - self._model = model - self._allowed_tools = { - "get_customer_detail", - "get_subscription_detail", - "get_data_usage", - "search_knowledge_base", - } - - async def _ensure_agent(self) -> None: - if self._agent is not None: - return - - logger.info("[UsagePatternExecutor] Initializing agent...") - filtered_functions = [func for func in self._mcp_tool.functions if func.name in self._allowed_tools] - self._agent = ChatAgent( - name="UsagePatternAnalyst", - chat_client=self._chat_client, - model=self._model, - instructions=( - "You are a usage pattern fraud analyst for Contoso. " - "Analyze customer data usage patterns to detect anomalies that may indicate fraud. " - "Use the provided tools to retrieve customer details, subscription info, and data usage history. " - "Look for: sudden spikes in data usage, usage patterns inconsistent with subscription tier, " - "usage from unusual times/locations. " - "Provide a risk score (0.0-1.0) and specific risk indicators." - ), - tools=filtered_functions, - ) - await self._agent.__aenter__() - logger.info("[UsagePatternExecutor] Agent initialized with filtered MCP tools") - - @handler - async def handle_alert( - self, alert: SuspiciousActivityAlert, ctx: WorkflowContext[UsageAnalysisResult] - ) -> None: - logger.info(f"[UsagePatternExecutor] Analyzing alert {alert.alert_id}") - - await self._ensure_agent() - assert self._agent is not None - - # Create analysis prompt - prompt = f"""Analyze potential fraud for customer ID {alert.customer_id}. - -Alert Details: -- Type: {alert.alert_type} -- Description: {alert.description} -- Severity: {alert.severity} - -Tasks: -1. Retrieve customer details and subscription information -2. Get recent data usage history (last 30 days if available) -3. Identify any unusual patterns or anomalies -4. Provide risk indicators and a risk score (0.0-1.0) - -Respond in this format: -FINDINGS: [Your detailed findings] -RISK_INDICATORS: [List specific red flags] -RISK_SCORE: [0.0-1.0] -""" - - # Run agent to analyze - thread = self._agent.get_new_thread() - response_parts = [] - async for chunk in self._agent.run_stream(prompt, thread=thread): - if hasattr(chunk, "text") and chunk.text: - response_parts.append(chunk.text) - - response = "".join(response_parts) - logger.info(f"[UsagePatternExecutor] Analysis complete: {response[:200]}...") - - # Parse response (simple parsing - in production use structured output) - risk_score = 0.5 # Default - risk_indicators = [] - findings = response - - if "RISK_SCORE:" in response: - try: - score_line = [line for line in response.split("\n") if "RISK_SCORE:" in line][0] - risk_score = float(score_line.split("RISK_SCORE:")[1].strip()) - except (IndexError, ValueError): - pass - - if "RISK_INDICATORS:" in response: - try: - indicators_line = [line for line in response.split("\n") if "RISK_INDICATORS:" in line][0] - indicators_text = indicators_line.split("RISK_INDICATORS:")[1].strip() - risk_indicators = [ind.strip() for ind in indicators_text.split(",")] - except IndexError: - pass - - # Send result to aggregator - result = UsageAnalysisResult( - alert_id=alert.alert_id, - customer_id=alert.customer_id, - findings=findings, - risk_indicators=risk_indicators, - risk_score=risk_score, - ) - - await ctx.send_message(result) - logger.info(f"[UsagePatternExecutor] Sent analysis result (risk_score={risk_score})") - - -class LocationAnalysisExecutor(Executor): - """Analyzes location anomalies using MCP tools.""" - - def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient, model: str, id: str = "location_analysis_executor") -> None: - super().__init__(id=id) - print("[LocationAnalysisExecutor] Initializing...") - self._agent: ChatAgent | None = None - self._mcp_tool = mcp_tool - self._chat_client = chat_client - self._model = model - self._allowed_tools = { - "get_customer_detail", - "get_security_logs", - "search_knowledge_base", - } - - async def _ensure_agent(self) -> None: - if self._agent is not None: - return - - logger.info("[LocationAnalysisExecutor] Initializing agent...") - filtered_functions = [func for func in self._mcp_tool.functions if func.name in self._allowed_tools] - self._agent = ChatAgent( - name="LocationAnalyst", - chat_client=self._chat_client, - model=self._model, - instructions=( - "You are a geolocation fraud analyst for Contoso. " - "Analyze customer security logs and authentication patterns to detect location-based fraud. " - "Use the provided tools to retrieve customer details and security logs. " - "Look for: logins from multiple countries in short timeframes, impossible travel scenarios, " - "access from high-risk regions, VPN/proxy usage patterns. " - "Provide a risk score (0.0-1.0) and specific risk indicators." - ), - tools=filtered_functions, - ) - await self._agent.__aenter__() - logger.info("[LocationAnalysisExecutor] Agent initialized") - - @handler - async def handle_alert( - self, alert: SuspiciousActivityAlert, ctx: WorkflowContext[LocationAnalysisResult] - ) -> None: - logger.info(f"[LocationAnalysisExecutor] Analyzing alert {alert.alert_id}") - - await self._ensure_agent() - assert self._agent is not None - - prompt = f"""Analyze location-based fraud indicators for customer ID {alert.customer_id}. - -Alert Details: -- Type: {alert.alert_type} -- Description: {alert.description} - -Tasks: -1. Retrieve customer details -2. Get security logs (authentication events, login locations) -3. Identify suspicious location patterns -4. Provide risk indicators and a risk score (0.0-1.0) - -Respond in this format: -FINDINGS: [Your detailed findings] -RISK_INDICATORS: [List specific red flags] -RISK_SCORE: [0.0-1.0] -""" - - thread = self._agent.get_new_thread() - response_parts = [] - async for chunk in self._agent.run_stream(prompt, thread=thread): - if hasattr(chunk, "text") and chunk.text: - response_parts.append(chunk.text) - - response = "".join(response_parts) - logger.info(f"[LocationAnalysisExecutor] Analysis complete") - - # Parse response - risk_score = 0.5 - risk_indicators = [] - findings = response - - if "RISK_SCORE:" in response: - try: - score_line = [line for line in response.split("\n") if "RISK_SCORE:" in line][0] - risk_score = float(score_line.split("RISK_SCORE:")[1].strip()) - except (IndexError, ValueError): - pass - - if "RISK_INDICATORS:" in response: - try: - indicators_line = [line for line in response.split("\n") if "RISK_INDICATORS:" in line][0] - indicators_text = indicators_line.split("RISK_INDICATORS:")[1].strip() - risk_indicators = [ind.strip() for ind in indicators_text.split(",")] - except IndexError: - pass - - result = LocationAnalysisResult( - alert_id=alert.alert_id, - customer_id=alert.customer_id, - findings=findings, - risk_indicators=risk_indicators, - risk_score=risk_score, - ) - - await ctx.send_message(result) - logger.info(f"[LocationAnalysisExecutor] Sent analysis result (risk_score={risk_score})") - - -class BillingChargeExecutor(Executor): - """Analyzes billing and charge anomalies using MCP tools.""" - - def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient, model: str, id: str = "billing_charge_executor") -> None: - super().__init__(id=id) - self._agent: ChatAgent | None = None - self._mcp_tool = mcp_tool - self._chat_client = chat_client - self._model = model - self._allowed_tools = { - "get_customer_detail", - "get_billing_summary", - "get_subscription_detail", - "get_customer_orders", - "search_knowledge_base", - } - - async def _ensure_agent(self) -> None: - if self._agent is not None: - return - - logger.info("[BillingChargeExecutor] Initializing agent...") - filtered_functions = [func for func in self._mcp_tool.functions if func.name in self._allowed_tools] - self._agent = ChatAgent( - name="BillingAnalyst", - chat_client=self._chat_client, - model=self._model, - instructions=( - "You are a billing fraud analyst for Contoso. " - "Analyze customer billing patterns and charges to detect fraudulent activity. " - "Use the provided tools to retrieve customer details, billing summaries, and order history. " - "Look for: unusual charge amounts, unexpected subscription changes, " - "orders inconsistent with customer profile, rapid succession of charges. " - "Provide a risk score (0.0-1.0) and specific risk indicators." - ), - tools=filtered_functions, - ) - await self._agent.__aenter__() - logger.info("[BillingChargeExecutor] Agent initialized") - - @handler - async def handle_alert( - self, alert: SuspiciousActivityAlert, ctx: WorkflowContext[BillingAnalysisResult] - ) -> None: - logger.info(f"[BillingChargeExecutor] Analyzing alert {alert.alert_id}") - - await self._ensure_agent() - assert self._agent is not None - - prompt = f"""Analyze billing fraud indicators for customer ID {alert.customer_id}. - -Alert Details: -- Type: {alert.alert_type} -- Description: {alert.description} - -Tasks: -1. Retrieve customer details and subscription information -2. Get billing summary and recent orders -3. Identify unusual billing patterns or charges -4. Provide risk indicators and a risk score (0.0-1.0) - -Respond in this format: -FINDINGS: [Your detailed findings] -RISK_INDICATORS: [List specific red flags] -RISK_SCORE: [0.0-1.0] -""" - - thread = self._agent.get_new_thread() - response_parts = [] - async for chunk in self._agent.run_stream(prompt, thread=thread): - if hasattr(chunk, "text") and chunk.text: - response_parts.append(chunk.text) - - response = "".join(response_parts) - logger.info(f"[BillingChargeExecutor] Analysis complete") - - # Parse response - risk_score = 0.5 - risk_indicators = [] - findings = response - - if "RISK_SCORE:" in response: - try: - score_line = [line for line in response.split("\n") if "RISK_SCORE:" in line][0] - risk_score = float(score_line.split("RISK_SCORE:")[1].strip()) - except (IndexError, ValueError): - pass - - if "RISK_INDICATORS:" in response: - try: - indicators_line = [line for line in response.split("\n") if "RISK_INDICATORS:" in line][0] - indicators_text = indicators_line.split("RISK_INDICATORS:")[1].strip() - risk_indicators = [ind.strip() for ind in indicators_text.split(",")] - except IndexError: - pass - - result = BillingAnalysisResult( - alert_id=alert.alert_id, - customer_id=alert.customer_id, - findings=findings, - risk_indicators=risk_indicators, - risk_score=risk_score, - ) - - await ctx.send_message(result) - logger.info(f"[BillingChargeExecutor] Sent analysis result (risk_score={risk_score})") - - -class FraudRiskAggregatorExecutor(Executor): - """ - LLM-based agent that aggregates analysis results and produces final fraud risk assessment. - This is a fan-in executor that waits for all three analysis results. - """ - - def __init__(self, chat_client: AzureOpenAIChatClient, model: str, id: str = "fraud_risk_aggregator") -> None: - super().__init__(id=id) - self._agent: ChatAgent | None = None - self._chat_client = chat_client - self._model = model - async def _ensure_agent(self) -> None: - if self._agent is not None: - return - - logger.info("[FraudRiskAggregator] Initializing agent...") - self._agent = ChatAgent( - name="FraudRiskAggregator", - chat_client=self._chat_client, - model=self._model, - instructions=( - "You are the lead fraud risk aggregator for Contoso. " - "You receive analysis results from three specialist agents: " - "usage pattern analyst, location analyst, and billing analyst. " - "Your job is to synthesize their findings into a single fraud risk assessment. " - "Calculate an overall risk score (0.0-1.0) based on the three input scores. " - "Determine risk level (low, medium, high, critical) and recommend action: " - "- clear: No fraud detected, close alert " - "- lock_account: Lock customer account to prevent further activity " - "- refund_charges: Reverse fraudulent charges " - "- both: Lock account AND refund charges " - "Provide clear reasoning for your decision." - ), - ) - await self._agent.__aenter__() - logger.info("[FraudRiskAggregator] Agent initialized") - - @handler - async def handle_analysis_results( - self, - results: list[UsageAnalysisResult | LocationAnalysisResult | BillingAnalysisResult], - ctx: WorkflowContext[FraudRiskAssessment], - ) -> None: - logger.info(f"[FraudRiskAggregator] Aggregating {len(results)} analysis results") - - await self._ensure_agent() - assert self._agent is not None - - # Build comprehensive prompt with all analysis results - alert_id = results[0].alert_id - customer_id = results[0].customer_id - - summaries = [] - for result in results: - summary = f""" -Analysis Type: {result.analysis_type} -Risk Score: {result.risk_score} -Risk Indicators: {', '.join(result.risk_indicators) if result.risk_indicators else 'None'} -Findings: {result.findings} -""" - summaries.append(summary) - - prompt = f"""Aggregate fraud risk assessment for Alert ID: {alert_id}, Customer ID: {customer_id} - -You have received analysis results from three specialists: - -{chr(10).join(summaries)} - -Tasks: -1. Calculate an overall risk score (weighted average or your best judgment) -2. Determine risk level: low (<0.3), medium (0.3-0.6), high (0.6-0.8), critical (>0.8) -3. Recommend action: clear, lock_account, refund_charges, or both -4. Provide reasoning that references specific findings from the analysts - -Respond in this format: -OVERALL_RISK_SCORE: [0.0-1.0] -RISK_LEVEL: [low/medium/high/critical] -RECOMMENDED_ACTION: [clear/lock_account/refund_charges/both] -REASONING: [Your comprehensive reasoning] -""" - - thread = self._agent.get_new_thread() - response_parts = [] - async for chunk in self._agent.run_stream(prompt, thread=thread): - if hasattr(chunk, "text") and chunk.text: - response_parts.append(chunk.text) - - response = "".join(response_parts) - logger.info(f"[FraudRiskAggregator] Assessment complete") - - # Parse response - overall_risk_score = 0.5 - risk_level = "medium" - recommended_action = "clear" - reasoning = response - - try: - if "OVERALL_RISK_SCORE:" in response: - score_line = [line for line in response.split("\n") if "OVERALL_RISK_SCORE:" in line][0] - overall_risk_score = float(score_line.split("OVERALL_RISK_SCORE:")[1].strip()) - - if "RISK_LEVEL:" in response: - level_line = [line for line in response.split("\n") if "RISK_LEVEL:" in line][0] - risk_level = level_line.split("RISK_LEVEL:")[1].strip().lower() - - if "RECOMMENDED_ACTION:" in response: - action_line = [line for line in response.split("\n") if "RECOMMENDED_ACTION:" in line][0] - recommended_action = action_line.split("RECOMMENDED_ACTION:")[1].strip().lower() - except (IndexError, ValueError) as e: - logger.warning(f"[FraudRiskAggregator] Error parsing response: {e}") - - # Create assessment - assessment = FraudRiskAssessment( - alert_id=alert_id, - customer_id=customer_id, - overall_risk_score=overall_risk_score, - risk_level=risk_level, - recommended_action=recommended_action, - reasoning=reasoning, - analysis_summaries=[f"{r.analysis_type}: {r.risk_score}" for r in results], - ) - - await ctx.send_message(assessment) - logger.info( - f"[FraudRiskAggregator] Sent assessment (risk_score={overall_risk_score}, action={recommended_action})" - ) - - -class ReviewGatewayExecutor(Executor): - """ - Gateway that handles high-risk assessments with human-in-the-loop review. - - Uses the new request_info API: - 1. Receives high-risk assessment - 2. Calls ctx.request_info() to pause workflow and request analyst input - 3. @response_handler receives the analyst's decision - 4. Forwards decision to FraudActionExecutor - """ - - def __init__(self, fraud_action_id: str, id: str = "review_gateway") -> None: - super().__init__(id=id) - self._fraud_action_id = fraud_action_id - # Store the assessment for use in the response handler - self._pending_assessment: FraudRiskAssessment | None = None - - @handler - async def handle_assessment( - self, assessment: FraudRiskAssessment, ctx: WorkflowContext[AnalystDecision] - ) -> None: - logger.info(f"[ReviewGateway] Routing high-risk assessment {assessment.alert_id} to analyst") - - # Store assessment for response handler - self._pending_assessment = assessment - - # Create analyst review request - request = AnalystReviewRequest( - assessment=assessment, - prompt=f"Review fraud case for alert {assessment.alert_id}. Risk score: {assessment.overall_risk_score:.2f}. Recommended action: {assessment.recommended_action}", - ) - - # Request info from external analyst - workflow will pause here - # The response will be handled by the @response_handler below - await ctx.request_info(request, AnalystDecision) - logger.info(f"[ReviewGateway] Waiting for analyst decision on {assessment.alert_id}") - - @response_handler - async def handle_analyst_response( - self, - original_request: AnalystReviewRequest, - response: AnalystDecision, - ctx: WorkflowContext[AnalystDecision], - ) -> None: - """Handle the analyst's decision and forward to fraud action executor.""" - logger.info(f"[ReviewGateway] Received analyst decision: {response.approved_action}") - - # Ensure decision has customer_id from original assessment - if original_request.assessment and getattr(response, "customer_id", None) in (None, 0): - from dataclasses import replace - response = replace( - response, - customer_id=original_request.assessment.customer_id, - alert_id=original_request.assessment.alert_id, - ) - - # Forward the analyst decision to fraud action executor - await ctx.send_message(response, target_id=self._fraud_action_id) - logger.info(f"[ReviewGateway] Forwarded decision to fraud action executor") - - -class AutoClearExecutor(Executor): - """Automatically clears low-risk alerts.""" - - def __init__(self, id: str = "auto_clear_executor") -> None: - super().__init__(id=id) - - @handler - async def handle_assessment( - self, assessment: FraudRiskAssessment, ctx: WorkflowContext[ActionResult] - ) -> None: - logger.info(f"[AutoClear] Auto-clearing alert {assessment.alert_id} (low risk)") - - result = ActionResult( - alert_id=assessment.alert_id, - customer_id=assessment.customer_id, - action_taken="cleared", - success=True, - details=f"Alert auto-cleared. Risk score: {assessment.overall_risk_score}", - timestamp=datetime.now().isoformat(), - ) - - await ctx.send_message(result) - logger.info(f"[AutoClear] Alert {assessment.alert_id} cleared") - - -class FraudActionExecutor(Executor): - """Executes fraud mitigation actions based on analyst decision.""" - - def __init__(self, id: str = "fraud_action_executor") -> None: - super().__init__(id=id) - - @handler - async def handle_decision(self, decision: AnalystDecision, ctx: WorkflowContext[ActionResult]) -> None: - logger.info(f"[FraudAction] Executing action: {decision.approved_action} for alert {decision.alert_id}") - - # Add delay to make workflow progression visible in UI - await asyncio.sleep(2) - - # Simulate action execution - action_details = [] - if "lock" in decision.approved_action: - action_details.append("Account locked") - if "refund" in decision.approved_action: - action_details.append("Charges reversed") - if decision.approved_action == "clear": - action_details.append("Alert cleared, no action taken") - - result = ActionResult( - alert_id=decision.alert_id, - customer_id=decision.customer_id, - action_taken=decision.approved_action, - success=True, - details=f"{', '.join(action_details)}. Analyst: {decision.analyst_id}. Notes: {decision.analyst_notes}", - timestamp=datetime.now().isoformat(), - ) - - await ctx.send_message(result) - logger.info(f"[FraudAction] Action executed for alert {decision.alert_id}") - - -class FinalNotificationExecutor(Executor): - """Sends final notification and logs audit trail.""" - - def __init__(self, id: str = "final_notification_executor") -> None: - super().__init__(id=id) - - @handler - async def handle_result( - self, result: ActionResult, ctx: WorkflowContext[None, FinalNotification] - ) -> None: - logger.info(f"[FinalNotification] Processing result for alert {result.alert_id}") - - # Add delay to make workflow progression visible in UI - await asyncio.sleep(2) - - # Simulate notification and audit logging - notification = FinalNotification( - alert_id=result.alert_id, - customer_id=result.customer_id, - resolution=result.details, - customer_notified=True, - audit_logged=True, - ) - - # Yield as workflow output - await ctx.yield_output(notification) - - logger.info(f"[FinalNotification] Alert {result.alert_id} completed and logged") - - -# ============================================================================ -# Workflow Builder -# ============================================================================ - - -async def create_fraud_detection_workflow( - mcp_tool: MCPStreamableHTTPTool, - chat_client: AzureOpenAIChatClient, - model: str, - checkpoint_storage: Any | None = None, -) -> Any: - """ - Build the fraud detection workflow. - - Topology (updated for new request_info API): - AlertRouter → [UsagePattern, Location, Billing] → FraudRiskAggregator - ↓ - (Switch based on risk score) - ↓ - ┌───────────────────────┴──────────────────────┐ - ↓ ↓ - (High Risk) (Low Risk) - ReviewGateway AutoClearExecutor - (uses ctx.request_info ↓ - for human-in-the-loop) ↓ - ↓ ↓ - FraudActionExecutor ────────────────────→ FinalNotificationExecutor - - Note: ReviewGateway now uses ctx.request_info() and @response_handler - instead of the deprecated RequestInfoExecutor. - """ - - # Create executors - alert_router = AlertRouterExecutor() - usage_executor = UsagePatternExecutor(mcp_tool, chat_client, model) - location_executor = LocationAnalysisExecutor(mcp_tool, chat_client, model) - billing_executor = BillingChargeExecutor(mcp_tool, chat_client, model) - aggregator = FraudRiskAggregatorExecutor(chat_client, model) - auto_clear = AutoClearExecutor() - fraud_action = FraudActionExecutor() - final_notification = FinalNotificationExecutor() - - # Create human-in-the-loop executor (now uses ctx.request_info internally) - review_gateway = ReviewGatewayExecutor( - fraud_action_id=fraud_action.id, - ) - - # Build workflow - builder = WorkflowBuilder() - - # Fan-out edges: AlertRouter → 3 analysts (parallel branches) - builder.add_fan_out_edges(alert_router, [usage_executor, location_executor, billing_executor]) - - # Fan-in edge: 3 analysts → Aggregator (waits for all 3) - builder.add_fan_in_edges([usage_executor, location_executor, billing_executor], aggregator) - - # Switch/case edges: Aggregator → High risk OR Low risk - builder.add_switch_case_edge_group( - aggregator, - [ - # High risk → Review Gateway (will request human input via ctx.request_info) - Case(condition=lambda assessment: assessment.overall_risk_score >= 0.6, target=review_gateway), - # Low risk → Auto clear - Default(target=auto_clear), - ], - ) - - # Review gateway → Fraud action (after analyst decision via @response_handler) - builder.add_edge(review_gateway, fraud_action) - - # Both paths → Final notification - builder.add_edge(auto_clear, final_notification) - builder.add_edge(fraud_action, final_notification) - - # Set start executor - builder.set_start_executor(alert_router) - - if checkpoint_storage: - builder = builder.with_checkpointing(checkpoint_storage=checkpoint_storage) - - workflow = builder.build() - - print("\n" + "=" * 80) - print(f"[WORKFLOW STRUCTURE]") - print(f"Total executors: {len(workflow.executors)}") - print(f"Executor IDs: {list(workflow.executors.keys())}") - print("=" * 80 + "\n") - - logger.info("[Workflow] Fraud detection workflow built successfully") - - return workflow - - -# ============================================================================ -# Main Console Application -# ============================================================================ - - -async def main() -> None: - """Main console application.""" - # Load environment variables from .env file - - logger.info("=" * 80) - logger.info("Contoso Fraud Detection & Escalation Workflow") - logger.info("=" * 80) - - # Load configuration - azure_openai_key = os.getenv("AZURE_OPENAI_API_KEY") - azure_deployment = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT") - azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") - mcp_server_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") - if not all([azure_openai_key, azure_deployment, azure_endpoint]): - logger.error("Missing required environment variables. Please set:") - logger.error(" AZURE_OPENAI_API_KEY") - logger.error(" AZURE_OPENAI_CHAT_DEPLOYMENT") - logger.error(" AZURE_OPENAI_ENDPOINT") - logger.error(" MCP_SERVER_URI (optional, defaults to http://localhost:8000)") - return - - # Create Azure OpenAI client - chat_client = AzureOpenAIChatClient( - api_key=azure_openai_key, - deployment_name=azure_deployment, - endpoint=azure_endpoint, - ) - - # Create MCP tool - logger.info(f"[Setup] Connecting to MCP server at {mcp_server_uri}") - mcp_tool = MCPStreamableHTTPTool( - name="contoso_mcp", - url=mcp_server_uri, - headers={"Content-Type": "application/json"}, - timeout=30, - request_timeout=30, - ) - - # Connect to MCP server - await mcp_tool.__aenter__() - logger.info(f"[Setup] Connected to MCP server, loaded {len(mcp_tool.functions)} tools") - - # Create checkpoint storage - # checkpoint_storage=None - checkpoint_storage = FileCheckpointStorage("./checkpoints/fraud_detection") - - # Build workflow - workflow = await create_fraud_detection_workflow( - mcp_tool=mcp_tool, - chat_client=chat_client, - model=azure_deployment, - checkpoint_storage=checkpoint_storage, - ) - - # Sample suspicious activity alerts - alerts = [ - SuspiciousActivityAlert( - alert_id="ALERT-001", - customer_id=1, - alert_type="multi_country_login", - description="Login attempts from USA and Russia within 2 hours", - timestamp=datetime.now().isoformat(), - severity="high", - ), - SuspiciousActivityAlert( - alert_id="ALERT-002", - customer_id=2, - alert_type="data_spike", - description="Data usage increased by 500% in last 24 hours", - timestamp=datetime.now().isoformat(), - severity="medium", - ), - SuspiciousActivityAlert( - alert_id="ALERT-003", - customer_id=3, - alert_type="unusual_charges", - description="Three large purchases totaling $5,000 in 10 minutes", - timestamp=datetime.now().isoformat(), - severity="high", - ), - ] - - # Process first alert - logger.info("\n" + "=" * 80) - logger.info(f"Processing Alert: {alerts[0].alert_id}") - logger.info("=" * 80) - - try: - # Run workflow with streaming - logger.info("[Workflow] Starting fraud detection workflow...") - - event_count = 0 - final_output: FinalNotification | None = None - last_checkpoint_id: str | None = None - - async def consume_stream(stream: Any) -> tuple[list[RequestInfoEvent], FinalNotification | None, str | None]: - nonlocal event_count - pending_requests: list[RequestInfoEvent] = [] - output: FinalNotification | None = None - checkpoint_id: str | None = None - - async for event in stream: - event_count += 1 - logger.info(f"[Workflow] Event #{event_count}: {event.__class__.__name__}") - print(f"\n[Event Type: {event.__class__.__name__}]") - - if isinstance(event, ExecutorInvokedEvent): - print(f" ▶ Executor '{event.executor_id}' invoked") - if getattr(event, "message", None): - print(f" 📥 Input: {event.message}") - elif isinstance(event, ExecutorCompletedEvent): - print(f" ✓ Executor '{event.executor_id}' completed") - if getattr(event, "data", None): - print(" 📤 Data:") - if isinstance(event.data, dict): - for key, value in event.data.items(): - print(f" • {key}: {value}") - elif hasattr(event.data, "__dict__"): - for key, value in event.data.__dict__.items(): - if not key.startswith("_"): - print(f" • {key}: {value}") - else: - print(f" {event.data}") - elif isinstance(event, RequestInfoEvent) and isinstance(event.data, AnalystReviewRequest): - pending_requests.append(event) - logger.info("\n" + "=" * 80) - logger.info("ANALYST REVIEW REQUIRED") - logger.info("=" * 80) - request = event.data - logger.info(f"Request ID: {event.request_id}") - risk_level = request.assessment.risk_level if request.assessment else "N/A" - logger.info(f"Assessment Risk Level: {risk_level}") - print(" ⚠ Analyst review required") - if request.assessment: - print( - f" • Alert: {request.assessment.alert_id} | Risk Score: {request.assessment.overall_risk_score:.2f}" - ) - print(f" • Recommended Action: {request.assessment.recommended_action}") - if request.prompt: - print(f" • Prompt: {request.prompt}") - - # Capture the checkpoint ID from the workflow context - if hasattr(workflow, '_runner_context') and hasattr(workflow._runner_context, '_last_checkpoint_id'): - checkpoint_id = workflow._runner_context._last_checkpoint_id - elif isinstance(event, WorkflowOutputEvent): - if isinstance(event.data, FinalNotification): - output = event.data - print(f"\n{'=' * 80}") - print(" 🎯 WORKFLOW COMPLETED") - print(f"{'=' * 80}") - if output: - print(" Final Output:") - if hasattr(output, "__dict__"): - for key, value in output.__dict__.items(): - if not key.startswith("_"): - print(f" • {key}: {value}") - else: - print(f" {output}") - elif isinstance(event, WorkflowStatusEvent): - print(f" ℹ Status: {event.state.name}") - - logger.info(f"[Event] {event}") - - return pending_requests, output, checkpoint_id - - pending_requests, final_output, last_checkpoint_id = await consume_stream(workflow.run_stream(alerts[0])) - - while final_output is None and pending_requests: - logger.info("\n" + "=" * 80) - logger.info("CHECKPOINT SAVED - Workflow paused for analyst input") - logger.info("=" * 80) - - if last_checkpoint_id: - logger.info(f"Checkpoint ID: {last_checkpoint_id}") - - # Collect analyst decisions from command line - responses: dict[str, AnalystDecision] = {} - for request_event in pending_requests: - assessment = request_event.data.assessment if request_event.data else None - alert_id = assessment.alert_id if assessment else alerts[0].alert_id - customer_id = assessment.customer_id if assessment else alerts[0].customer_id - - print("\n" + "=" * 80) - print(f"ANALYST DECISION REQUIRED") - print("=" * 80) - print(f"Request ID: {request_event.request_id}") - if assessment: - print(f"Alert ID: {assessment.alert_id}") - print(f"Customer ID: {assessment.customer_id}") - print(f"Risk Score: {assessment.overall_risk_score:.2f}") - print(f"Risk Level: {assessment.risk_level}") - print(f"Recommended Action: {assessment.recommended_action}") - print(f"\nReasoning:\n{assessment.reasoning[:500]}...") - - print("\n" + "-" * 80) - print("Available actions:") - print(" 1. clear - No fraud detected, close alert") - print(" 2. lock_account - Lock customer account") - print(" 3. refund_charges - Reverse fraudulent charges") - print(" 4. both - Lock account AND refund charges") - print("-" * 80) - - # Get analyst decision from command line - while True: - action_choice = input("\nEnter action (1-4): ").strip() - action_map = { - "1": "clear", - "2": "lock_account", - "3": "refund_charges", - "4": "both" - } - if action_choice in action_map: - approved_action = action_map[action_choice] - break - print("Invalid choice. Please enter 1, 2, 3, or 4.") - - analyst_notes = input("Enter analyst notes: ").strip() - if not analyst_notes: - analyst_notes = f"Analyst decision: {approved_action}" - - analyst_id = input("Enter analyst ID (default: analyst_cli): ").strip() - if not analyst_id: - analyst_id = "analyst_cli" - - decision = AnalystDecision( - alert_id=alert_id, - customer_id=customer_id, - approved_action=approved_action, - analyst_notes=analyst_notes, - analyst_id=analyst_id, - ) - responses[request_event.request_id] = decision - logger.info(f"[Analyst] Decision for {request_event.request_id}: {decision.approved_action}") - - # Resume workflow with analyst responses - logger.info("\n" + "=" * 80) - logger.info("RESUMING WORKFLOW with analyst decisions") - logger.info("=" * 80) - - pending_requests, final_output, last_checkpoint_id = await consume_stream(workflow.send_responses_streaming(responses)) - - if final_output is None and not pending_requests: - logger.warning( - "Workflow reached an idle state without producing a final notification or pending analyst review." - ) - - logger.info("\n" + "=" * 80) - logger.info("Workflow completed successfully!" if final_output else "Workflow ended without final output.") - if final_output: - logger.info( - "Final notification: alert_id=%s customer_id=%s resolution=%s", - final_output.alert_id, - final_output.customer_id, - final_output.resolution, - ) - logger.info("=" * 80) - - except Exception as e: - logger.error(f"[Error] Workflow failed: {e}", exc_info=True) - - finally: - # Cleanup - await mcp_tool.__aexit__(None, None, None) - logger.info("[Cleanup] MCP connection closed") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/agentic_ai/workflow/fraud_detection/pyproject.toml b/agentic_ai/workflow/fraud_detection/pyproject.toml deleted file mode 100644 index c91139aa2..000000000 --- a/agentic_ai/workflow/fraud_detection/pyproject.toml +++ /dev/null @@ -1,25 +0,0 @@ -[project] -name = "applications" -version = "0.1.0" -description = "Add your description here" -readme = "README.md" -requires-python = ">=3.12" -dependencies = [ - "fastapi==0.115.12", - "agent-framework==1.0.0b260130", - "azure-monitor-opentelemetry>=1.8.5", - "fastmcp==2.7.1", - "flasgger==0.9.7.1", - "flask==3.0.3", - "httpx==0.28.1", - "msal==1.31.0", - "openai>=1.90.0,<1.110.0", - "pydantic==2.11.4", - "requests==2.32.4", - "tenacity==8.5.0", - "uvicorn>=0.25.0", - "websockets>=15.0.1", -] - -[tool.uv] -prerelease = "allow" \ No newline at end of file diff --git a/agentic_ai/workflow/fraud_detection/scenario.md b/agentic_ai/workflow/fraud_detection/scenario.md deleted file mode 100644 index 70abc4f6b..000000000 --- a/agentic_ai/workflow/fraud_detection/scenario.md +++ /dev/null @@ -1,29 +0,0 @@ - -## **Contoso Fraud Detection & Escalation Workflow** - -**Business Context:** -Contoso’s automated systems flag suspicious account activity, but certain cases require a fraud analyst to review before taking action (e.g., locking account, reversing charges). - -**Flow:** -1. **Event Trigger:** Backend monitoring system sends “Suspicious Activity Alert” (login from multiple countries, large data usage spike). -2. **AlertRouterExecutor** → routes alert to multiple analysis executors: - - `UsagePatternExecutor` → checks recent usage against historical baseline. - - `LocationAnalysisExecutor` → checks geolocation anomalies. - - `BillingChargeExecutor` → checks for unusual purchases or charges. -3. **Fan-In** to `FraudRiskAggregatorExecutor`: - - Produces `FraudRiskScore` and recommended action (lock account, refund charges, ignore). -4. **SwitchCaseEdgeRunner**: - - If risk score ≥ threshold → route to `ReviewGatewayExecutor` for human fraud analyst review. - - Else → route to `AutoClearExecutor`. -5. **ReviewGatewayExecutor** → uses `ctx.request_info()` to request analyst review with full context. -6. **Workflow pauses** — checkpoint saved automatically. -7. **Analyst decides** (approve lock/refund or clear). -8. Workflow resumes: - - `FraudActionExecutor` → performs chosen action (e.g., lock account, reverse charges). -9. **FinalNotificationExecutor** → informs customer and logs audit trail. - -**Highlight:** -- Demonstrates **event-driven workflow** triggered by backend system. -- Multiple data sources fan-out and aggregate to produce a single risk assessment. -- Human analyst gating ensures **false positives** don’t harm legitimate customers. -- **Checkpointing** means the workflow can pause indefinitely until analyst responds. diff --git a/agentic_ai/workflow/fraud_detection/start_backend.bat b/agentic_ai/workflow/fraud_detection/start_backend.bat deleted file mode 100644 index 7acd12251..000000000 --- a/agentic_ai/workflow/fraud_detection/start_backend.bat +++ /dev/null @@ -1,17 +0,0 @@ -@echo off -REM Startup script for Fraud Detection Workflow Backend -REM Sets UTF-8 encoding to prevent Unicode errors on Windows - -echo Starting Fraud Detection Workflow Backend... - -REM Set UTF-8 encoding for Python -set PYTHONIOENCODING=utf-8 -set PYTHONUTF8=1 - -REM Change to the script directory -cd /d "%~dp0" - -REM Run with uv -uv run --prerelease allow backend.py - -pause diff --git a/agentic_ai/workflow/fraud_detection/ui/.dockerignore b/agentic_ai/workflow/fraud_detection/ui/.dockerignore deleted file mode 100644 index 778a2ee8b..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.dockerignore +++ /dev/null @@ -1,30 +0,0 @@ -# Development dependencies and build artifacts -node_modules/ -dist/ -build/ - -# Logs -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* - -# Environment files -.env -.env.local -.env.*.local - -# IDE -.vscode/ -.idea/ - -# OS -.DS_Store -Thumbs.db - -# Test coverage -coverage/ - -# Temporary files -*.tmp -*.temp diff --git a/agentic_ai/workflow/fraud_detection/ui/.env.example b/agentic_ai/workflow/fraud_detection/ui/.env.example deleted file mode 100644 index 02c3939e0..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.env.example +++ /dev/null @@ -1,6 +0,0 @@ -# API Configuration -VITE_API_BASE_URL=http://localhost:8001 -VITE_WS_URL=ws://localhost:8001/ws - -# App Configuration -VITE_APP_TITLE=Fraud Detection Workflow Visualizer diff --git a/agentic_ai/workflow/fraud_detection/ui/.eslintrc.cjs b/agentic_ai/workflow/fraud_detection/ui/.eslintrc.cjs deleted file mode 100644 index 7617f77ca..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.eslintrc.cjs +++ /dev/null @@ -1,22 +0,0 @@ -module.exports = { - root: true, - env: { browser: true, es2020: true }, - extends: [ - 'eslint:recommended', - 'plugin:react/recommended', - 'plugin:react/jsx-runtime', - 'plugin:react-hooks/recommended', - ], - ignorePatterns: ['dist', '.eslintrc.cjs'], - parserOptions: { ecmaVersion: 'latest', sourceType: 'module' }, - settings: { react: { version: '19.0' } }, - plugins: ['react-refresh'], - rules: { - 'react/jsx-no-target-blank': 'off', - 'react-refresh/only-export-components': [ - 'warn', - { allowConstantExport: true }, - ], - 'react/prop-types': 'warn', - }, -} diff --git a/agentic_ai/workflow/fraud_detection/ui/.gitignore b/agentic_ai/workflow/fraud_detection/ui/.gitignore deleted file mode 100644 index aa674a799..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.gitignore +++ /dev/null @@ -1,31 +0,0 @@ -# Logs -logs -*.log -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* -lerna-debug.log* - -node_modules -dist -dist-ssr -*.local - -# Editor directories and files -.vscode/* -!.vscode/extensions.json -.idea -.DS_Store -*.suo -*.ntvs* -*.njsproj -*.sln -*.sw? - -# Environment variables -.env -.env.local -.env.production.local -.env.development.local -.env.test.local diff --git a/agentic_ai/workflow/fraud_detection/ui/.prettierignore b/agentic_ai/workflow/fraud_detection/ui/.prettierignore deleted file mode 100644 index 8c444c5a1..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.prettierignore +++ /dev/null @@ -1,7 +0,0 @@ -node_modules -dist -build -.env -.env.local -*.log -coverage diff --git a/agentic_ai/workflow/fraud_detection/ui/.prettierrc.cjs b/agentic_ai/workflow/fraud_detection/ui/.prettierrc.cjs deleted file mode 100644 index 9f6715a36..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.prettierrc.cjs +++ /dev/null @@ -1,14 +0,0 @@ -// Prettier Configuration -module.exports = { - semi: true, - trailingComma: 'es5', - singleQuote: true, - printWidth: 100, - tabWidth: 2, - useTabs: false, - arrowParens: 'always', - endOfLine: 'lf', - jsxSingleQuote: false, - bracketSpacing: true, - jsxBracketSameLine: false, -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/.vscode/extensions.json b/agentic_ai/workflow/fraud_detection/ui/.vscode/extensions.json deleted file mode 100644 index 82a944c17..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "dbaeumer.vscode-eslint", - "esbenp.prettier-vscode", - "ms-vscode.vscode-typescript-next" - ] -} diff --git a/agentic_ai/workflow/fraud_detection/ui/Dockerfile b/agentic_ai/workflow/fraud_detection/ui/Dockerfile deleted file mode 100644 index f613068f8..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/Dockerfile +++ /dev/null @@ -1,57 +0,0 @@ -# Build stage -FROM node:22-alpine AS builder - -# Set working directory -WORKDIR /app - -# Add metadata -LABEL maintainer="OpenAI Workshop Team" -LABEL description="Fraud Detection UI - React + Vite Application" - -# Copy package files -COPY package*.json ./ - -# Install dependencies with clean install for reproducible builds -RUN npm ci --only=production=false && \ - npm cache clean --force - -# Copy application source -COPY . . - -# Build the application -RUN npm run build - -# Production stage -FROM node:22-alpine AS production - -# Install dumb-init for proper signal handling -RUN apk add --no-cache dumb-init - -# Set working directory -WORKDIR /app - -# Create a non-root user -RUN addgroup -g 1001 -S nodejs && \ - adduser -S nodejs -u 1001 - -# Install serve globally -RUN npm install -g serve - -# Copy built assets from builder stage -COPY --from=builder --chown=nodejs:nodejs /app/dist ./dist - -# Switch to non-root user -USER nodejs - -# Expose port -EXPOSE 3000 - -# Health check -HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \ - CMD wget --no-verbose --tries=1 --spider http://localhost:3000/ || exit 1 - -# Use dumb-init to handle signals properly -ENTRYPOINT ["dumb-init", "--"] - -# Serve the application -CMD ["serve", "-s", "dist", "-l", "3000"] diff --git a/agentic_ai/workflow/fraud_detection/ui/README.md b/agentic_ai/workflow/fraud_detection/ui/README.md deleted file mode 100644 index 9a785b969..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/README.md +++ /dev/null @@ -1,247 +0,0 @@ -# Fraud Detection Workflow Visualizer - -A modern React application for visualizing and managing fraud detection workflows in real-time. Built with Vite, React, Material-UI, and React Flow. - -## Features - -- 🔄 Real-time workflow visualization -- 🎯 Interactive workflow controls -- 📊 Live event logging -- 👤 Human-in-the-loop decision making -- 🔌 WebSocket-based real-time updates -- 🎨 Material-UI based responsive design -- 📈 React Flow powered workflow graphs - -## Tech Stack - -- **Framework**: React 19.2 -- **Build Tool**: Vite 7.2 -- **UI Library**: Material-UI (MUI) 7.3 -- **Graph Visualization**: React Flow 11.11 -- **State Management**: React Hooks -- **Real-time Communication**: WebSocket - -## Prerequisites - -- Node.js 18+ or newer -- npm 9+ or newer - -## Getting Started - -### Installation - -1. Clone the repository -2. Navigate to the project directory: - - ```bash - cd agentic_ai/workflow/fraud_detection/ui - ``` - -3. Install dependencies: - - ```bash - npm install - ``` - -4. Copy the environment file: - - ```bash - cp .env.example .env - ``` - -5. Configure your environment variables in `.env` if needed - -### Development - -Start the development server: - -```bash -npm run dev -``` - -The application will open at `http://localhost:3000` - -### Build - -Build for production: - -```bash -npm run build -``` - -Preview the production build: - -```bash -npm run preview -``` - -### Docker - -Build and run the application in a Docker container: - -```bash -# Build the Docker image -docker build -t fraud-detection-ui:latest . - -# Run the container -docker run -p 3000:3000 fraud-detection-ui:latest - -# Run with custom backend URL -docker run -p 3000:3000 \ - -e VITE_API_BASE_URL=http://your-backend:8001 \ - -e VITE_WS_URL=ws://your-backend:8001/ws \ - fraud-detection-ui:latest -``` - -The Docker image uses a multi-stage build with: - -- **Build stage**: Node.js 22 Alpine with all dependencies -- **Production stage**: Node.js 22 Alpine serving with `serve` -- **Security**: Runs as non-root user (nodejs:1001) -- **Port**: Exposes port 3000 -- **Health check**: Built-in health monitoring - -### Linting - -Run ESLint: - -```bash -npm run lint -``` - -Fix linting issues automatically: - -```bash -npm run lint:fix -``` - -## Project Structure - -```text -ui/ -├── src/ -│ ├── components/ # React components -│ │ ├── AnalystDecisionPanel.jsx -│ │ ├── ControlPanel.jsx -│ │ ├── CustomNode.jsx -│ │ ├── EventLog.jsx -│ │ └── WorkflowVisualizer.jsx -│ ├── hooks/ # Custom React hooks -│ │ └── useWebSocket.js -│ ├── utils/ # Utility functions -│ │ ├── api.js -│ │ ├── helpers.js -│ │ └── uiHelpers.jsx -│ ├── constants/ # Application constants -│ │ ├── config.js -│ │ └── workflow.js -│ ├── theme/ # MUI theme configuration -│ │ └── index.js -│ ├── App.jsx # Main application component -│ ├── main.jsx # Application entry point -│ └── index.css # Global styles -├── public/ # Static assets -├── .env # Environment variables -├── .env.example # Environment variables example -├── .eslintrc.cjs # ESLint configuration -├── .gitignore # Git ignore rules -├── index.html # HTML template -├── jsconfig.json # JavaScript configuration -├── package.json # Project dependencies -├── vite.config.js # Vite configuration -└── README.md # This file -``` - -## Path Aliases - -The project uses path aliases for cleaner imports: - -- `@/` → `src/` -- `@components/` → `src/components/` -- `@hooks/` → `src/hooks/` -- `@utils/` → `src/utils/` -- `@theme/` → `src/theme/` -- `@constants/` → `src/constants/` - -Example: - -```javascript -import WorkflowVisualizer from '@components/WorkflowVisualizer'; -import { useWebSocket } from '@hooks/useWebSocket'; -import theme from '@theme'; -``` - -## Environment Variables - -| Variable | Description | Default | -|----------|-------------|---------| -| `VITE_API_BASE_URL` | Backend API URL | `http://localhost:8001` | -| `VITE_WS_URL` | WebSocket URL | `ws://localhost:8001/ws` | -| `VITE_APP_TITLE` | Application title | `Fraud Detection Workflow Visualizer` | - -## API Integration - -The application connects to a backend API running on `http://localhost:8001` by default. The following endpoints are used: - -- `GET /api/alerts` - Fetch available alerts -- `POST /api/workflow/start` - Start a workflow -- `POST /api/workflow/decision` - Submit analyst decision -- `WS /ws` - WebSocket connection for real-time updates - -## Components - -### Main Components - -- **App.jsx** - Main application component, orchestrates all child components -- **WorkflowVisualizer** - Displays the workflow graph with real-time state updates -- **ControlPanel** - Alert selection and workflow control interface -- **AnalystDecisionPanel** - Human-in-the-loop decision making interface -- **EventLog** - Real-time event logging display -- **CustomNode** - Custom node component for workflow graph - -### Hooks - -- **useWebSocket** - Manages WebSocket connections with automatic reconnection - -## Development Guidelines - -### Code Style - -- Use functional components with hooks -- Follow ESLint rules -- Use path aliases for imports -- Add JSDoc comments for functions -- Keep components small and focused - -### Adding New Components - -1. Create component in `src/components/` -2. Export from the component file -3. Import using path alias: `import Component from '@components/Component'` - -### Adding New Constants - -1. Add constants to appropriate file in `src/constants/` -2. Export named exports -3. Import where needed: `import { CONSTANT } from '@constants/config'` - -## Troubleshooting - -### WebSocket Connection Issues - -- Ensure backend server is running -- Check `VITE_WS_URL` in `.env` -- Check browser console for connection errors - -### Build Issues - -- Clear node_modules and reinstall: `rm -rf node_modules && npm install` -- Clear Vite cache: `rm -rf node_modules/.vite` - -## License - -See the main project LICENSE file. - -## Contributing - -Please read the main project CONTRIBUTING guidelines. diff --git a/agentic_ai/workflow/fraud_detection/ui/index.html b/agentic_ai/workflow/fraud_detection/ui/index.html deleted file mode 100644 index fcdac8966..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/index.html +++ /dev/null @@ -1,16 +0,0 @@ - - - - - - Fraud Detection Workflow Visualizer - - - -
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"optional": true - }, - "immer": { - "optional": true - }, - "react": { - "optional": true - } - } - } - } -} diff --git a/agentic_ai/workflow/fraud_detection/ui/package.json b/agentic_ai/workflow/fraud_detection/ui/package.json deleted file mode 100644 index 77e904866..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/package.json +++ /dev/null @@ -1,30 +0,0 @@ -{ - "name": "fraud-detection-ui", - "version": "1.0.0", - "private": true, - "type": "module", - "scripts": { - "dev": "vite", - "build": "vite build", - "preview": "vite preview", - "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0", - "lint:fix": "eslint . --ext js,jsx --fix" - }, - "dependencies": { - "@emotion/react": "^11.14.0", - "@emotion/styled": "^11.14.1", - "@mui/icons-material": "^7.3.6", - "@mui/material": "^7.3.6", - "react": "^19.2.1", - "react-dom": "^19.2.1", - "reactflow": "^11.11.4" - }, - "devDependencies": { - "@vitejs/plugin-react": "^5.1.2", - "eslint": "^9.39.1", - "eslint-plugin-react": "^7.37.5", - "eslint-plugin-react-hooks": "^7.0.1", - "eslint-plugin-react-refresh": "^0.4.24", - "vite": "^7.2.7" - } -} diff --git a/agentic_ai/workflow/fraud_detection/ui/src/App.jsx b/agentic_ai/workflow/fraud_detection/ui/src/App.jsx deleted file mode 100644 index 67f692064..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/App.jsx +++ /dev/null @@ -1,231 +0,0 @@ -import React, { useState, useCallback, useEffect } from 'react'; -import { - Box, - ThemeProvider, - createTheme, - CssBaseline, - AppBar, - Toolbar, - Typography, - Container, - Paper, - Grid, -} from '@mui/material'; -import SecurityIcon from '@mui/icons-material/Security'; -import WorkflowVisualizer from './components/WorkflowVisualizer'; -import ControlPanel from './components/ControlPanel'; -import AnalystDecisionPanel from './components/AnalystDecisionPanel'; -import EventLog from './components/EventLog'; -import { useWebSocket } from './hooks/useWebSocket'; - -const theme = createTheme({ - palette: { - mode: 'light', - primary: { - main: '#1976d2', - }, - secondary: { - main: '#dc004e', - }, - success: { - main: '#4caf50', - }, - warning: { - main: '#ff9800', - }, - error: { - main: '#f44336', - }, - }, - typography: { - fontFamily: '"Roboto", "Helvetica", "Arial", sans-serif', - }, -}); - -function App() { - const [alerts, setAlerts] = useState([]); - const [selectedAlert, setSelectedAlert] = useState(null); - const [workflowRunning, setWorkflowRunning] = useState(false); - const [events, setEvents] = useState([]); - const [pendingDecision, setPendingDecision] = useState(null); - const [executorStates, setExecutorStates] = useState({}); - - // WebSocket hook for real-time updates - const { lastMessage, sendMessage } = useWebSocket('ws://localhost:8001/ws'); - - // Load sample alerts on mount - useEffect(() => { - fetch('/api/alerts') - .then((res) => res.json()) - .then((data) => setAlerts(data.alerts)) - .catch((err) => console.error('Error loading alerts:', err)); - }, []); - - // Handle WebSocket messages - useEffect(() => { - if (!lastMessage) return; - - try { - const event = lastMessage; - - // Add to event log - prevent duplicates by checking timestamp + type + executor_id - setEvents((prev) => { - const eventKey = `${event.timestamp}-${event.type || event.event_type}-${event.executor_id || ''}`; - const isDuplicate = prev.some( - (e) => `${e.timestamp}-${e.type || e.event_type}-${e.executor_id || ''}` === eventKey - ); - return isDuplicate ? prev : [...prev, event]; - }); - - // Handle workflow initialization - if (event.type === 'workflow_initializing') { - // Keep workflow running flag true, just show initialization message - } - - // Handle workflow started - if (event.type === 'workflow_started') { - // Workflow is now running - } - - // Update executor states based on event type - if (event.event_type === 'executor_invoked') { - setExecutorStates((prev) => ({ - ...prev, - [event.executor_id]: 'running', - })); - } else if (event.event_type === 'executor_completed') { - setExecutorStates((prev) => ({ - ...prev, - [event.executor_id]: 'completed', - })); - } - - // Handle decision required - if (event.type === 'decision_required') { - setPendingDecision(event); - setWorkflowRunning(false); - } - - // Handle workflow completion - if (event.type === 'workflow_completed' || event.type === 'workflow_error') { - setWorkflowRunning(false); - // Keep all executor states as-is (they should already be 'completed') - } - } catch (error) { - console.error('Error handling WebSocket message:', error); - } - }, [lastMessage]); - - const handleStartWorkflow = useCallback(async (alert) => { - console.log('Starting workflow for alert:', alert); - setSelectedAlert(alert); - setWorkflowRunning(true); - setEvents([]); - setExecutorStates({}); - setPendingDecision(null); - - try { - const response = await fetch('/api/workflow/start', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify(alert), - }); - - const data = await response.json(); - console.log('Workflow started:', data); - } catch (error) { - console.error('Error starting workflow:', error); - setWorkflowRunning(false); - } - }, []); - - const handleSubmitDecision = useCallback(async (decision) => { - console.log('Submitting decision:', decision); - - try { - const response = await fetch('/api/workflow/decision', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify(decision), - }); - - const data = await response.json(); - console.log('Decision submitted:', data); - - setPendingDecision(null); - setWorkflowRunning(true); - } catch (error) { - console.error('Error submitting decision:', error); - } - }, []); - - return ( - - - - {/* App Bar */} - - - - - Fraud Detection Workflow Visualizer - - - Real-time Multi-Agent Workflow Monitoring - - - - - {/* Main Content */} - - - {/* Left Column - Controls and Decision Panel */} - - - - {pendingDecision && ( - - )} - - - {/* Center Column - Workflow Visualization */} - - - - Workflow Graph - - {selectedAlert - ? `Alert: ${selectedAlert.alert_id} - ${selectedAlert.description}` - : 'Select an alert to start'} - - - - - - - - - {/* Right Column - Event Log */} - - - - - - - - ); -} - -export default App; \ No newline at end of file diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx deleted file mode 100644 index d2cea18f6..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/AnalystDecisionPanel.jsx +++ /dev/null @@ -1,179 +0,0 @@ -import { useState } from 'react'; -import { - Paper, - Box, - Typography, - Button, - TextField, - Select, - MenuItem, - FormControl, - InputLabel, - Chip, - Alert, - Divider, -} from '@mui/material'; -import GavelIcon from '@mui/icons-material/Gavel'; -import SendIcon from '@mui/icons-material/Send'; -import { ACTION_OPTIONS } from '../constants/workflow'; -import { getRiskLevel, getRiskColor } from '../utils/uiHelpers'; - -/** - * Panel for analyst to make decisions on fraud alerts - * @param {Object} props - Component props - * @param {Object} props.decision - Decision request object - * @param {Function} props.onSubmit - Callback to submit decision - */ -function AnalystDecisionPanel({ decision, onSubmit }) { - const [selectedAction, setSelectedAction] = useState( - decision.data?.recommended_action || 'clear' - ); - const [notes, setNotes] = useState(''); - - const handleSubmit = () => { - onSubmit({ - request_id: decision.request_id, - alert_id: decision.data.alert_id, - customer_id: decision.data.customer_id, - approved_action: selectedAction, - analyst_notes: notes || 'Analyst decision from UI', - analyst_id: 'analyst_ui', - }); - }; - - return ( - - - - Analyst Review Required - - - - - Human Decision Needed - - - - - - {/* Risk Assessment */} - - - Risk Assessment - - - Risk Score: - - - - Alert ID: - - - - - {/* Reasoning */} - {decision.data?.reasoning && ( - - - AI Analysis - - - - {decision.data.reasoning.length > 500 - ? decision.data.reasoning.substring(0, 500) + '...' - : decision.data.reasoning} - - - - )} - - {/* Recommended Action */} - - - Recommended Action - - opt.value === decision.data?.recommended_action) - ?.label || 'Unknown' - } - color={ - ACTION_OPTIONS.find((opt) => opt.value === decision.data?.recommended_action) - ?.color || 'default' - } - size="small" - sx={{ height: 20, fontSize: '0.7rem' }} - /> - - - - - {/* Decision Form */} - - Your Decision - - - - setNotes(e.target.value)} - placeholder="Add notes..." - sx={{ '& .MuiInputBase-input': { fontSize: '0.875rem' } }} - /> - - - - ); -} - -export default AnalystDecisionPanel; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/ControlPanel.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/ControlPanel.jsx deleted file mode 100644 index acacc11f1..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/ControlPanel.jsx +++ /dev/null @@ -1,128 +0,0 @@ -import { useState } from 'react'; -import { - Paper, - Box, - Typography, - Button, - Select, - MenuItem, - FormControl, - InputLabel, - Chip, - CircularProgress, -} from '@mui/material'; -import PlayArrowIcon from '@mui/icons-material/PlayArrow'; -import WarningIcon from '@mui/icons-material/Warning'; -import ErrorIcon from '@mui/icons-material/Error'; -import InfoIcon from '@mui/icons-material/Info'; -import { getSeverityIcon, getSeverityColor } from '../utils/uiHelpers'; - -/** - * Control panel component for selecting alerts and starting workflows - * @param {Object} props - Component props - * @param {Array} props.alerts - Array of alert objects - * @param {Function} props.onStartWorkflow - Callback to start workflow - * @param {boolean} props.workflowRunning - Whether workflow is currently running - * @param {Object} props.selectedAlert - Currently selected alert object - */ -function ControlPanel({ alerts = [], onStartWorkflow, workflowRunning = false, selectedAlert }) { - const [selectedAlertId, setSelectedAlertId] = useState(''); - - const handleStartClick = () => { - const alert = alerts.find((a) => a.alert_id === selectedAlertId); - if (alert) { - onStartWorkflow(alert); - } - }; - - return ( - - - Workflow Control - - - - Select Alert - - - - {selectedAlertId && !workflowRunning && ( - - - Description: - - - {alerts.find((a) => a.alert_id === selectedAlertId)?.description} - - - a.alert_id === selectedAlertId)?.customer_id}`} - size="small" - variant="outlined" - sx={{ height: 18, fontSize: '0.7rem' }} - /> - a.alert_id === selectedAlertId)?.alert_type} - size="small" - variant="outlined" - sx={{ height: 18, fontSize: '0.7rem' }} - /> - - - )} - - - - {selectedAlert && workflowRunning && ( - - - Active Workflow - - - Processing {selectedAlert.alert_id} - - - )} - - ); -} - -export default ControlPanel; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/CustomNode.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/CustomNode.jsx deleted file mode 100644 index a39a9c0be..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/CustomNode.jsx +++ /dev/null @@ -1,81 +0,0 @@ -import { Handle, Position } from 'reactflow'; -import { Box, Typography, Paper, Chip } from '@mui/material'; -import CheckCircleIcon from '@mui/icons-material/CheckCircle'; -import HourglassEmptyIcon from '@mui/icons-material/HourglassEmpty'; -import CircleIcon from '@mui/icons-material/Circle'; -import { getNodeStatusColor, getStatusLabel } from '../utils/uiHelpers'; - -/** - * Gets the appropriate icon for node status - * @param {string} status - Node status - * @returns {JSX.Element} Icon component - */ -const getStatusIcon = (status) => { - switch (status) { - case 'running': - return ; - case 'completed': - return ; - default: - return ; - } -}; - -/** - * Custom node component for React Flow workflow visualization - * @param {Object} props - Component props - * @param {Object} props.data - Node data object - */ -function CustomNode({ data = {} }) { - const statusColor = getNodeStatusColor(data.status); - const isActive = data.status === 'running'; - - return ( - - - - - - - {data.label} - - - - - {data.description && ( - - {data.description} - - )} - - - - - ); -} - -export default CustomNode; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/EventLog.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/EventLog.jsx deleted file mode 100644 index f27343e96..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/EventLog.jsx +++ /dev/null @@ -1,121 +0,0 @@ -import { useRef, useEffect, Fragment } from 'react'; -import { - Paper, - Box, - Typography, - List, - ListItem, - ListItemIcon, - ListItemText, - Chip, - Divider, -} from '@mui/material'; -import PlayArrowIcon from '@mui/icons-material/PlayArrow'; -import CheckCircleIcon from '@mui/icons-material/CheckCircle'; -import InfoIcon from '@mui/icons-material/Info'; -import GavelIcon from '@mui/icons-material/Gavel'; -import ErrorIcon from '@mui/icons-material/Error'; -import { getEventIcon, getEventColor, getEventTitle } from '../utils/uiHelpers'; -import { formatTime } from '../utils/helpers'; - -/** - * Event log component for displaying workflow events in real-time - * @param {Object} props - Component props - * @param {Array} props.events - Array of event objects - */ -function EventLog({ events = [] }) { - const listRef = useRef(null); - - // Auto-scroll to bottom when new events arrive - useEffect(() => { - if (listRef.current) { - listRef.current.scrollTop = listRef.current.scrollHeight; - } - }, [events]); - - const icons = { PlayArrowIcon, CheckCircleIcon, InfoIcon, GavelIcon, ErrorIcon }; - - return ( - - - Event Log - - {events.length} events - - - - - {events.length === 0 ? ( - - - No events yet. Start a workflow to see events. - - - ) : ( - events.map((event, index) => ( - - - - {getEventIcon(event, icons)} - - - - {getEventTitle(event)} - - - - } - secondary={ - - {formatTime(event.timestamp)} - - } - /> - - {index < events.length - 1 && } - - )) - )} - - - ); -} - -export default EventLog; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx b/agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx deleted file mode 100644 index 1852c058f..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/components/WorkflowVisualizer.jsx +++ /dev/null @@ -1,151 +0,0 @@ -import { useMemo, useEffect } from 'react'; -import ReactFlow, { - Background, - Controls, - MiniMap, - useNodesState, - useEdgesState, -} from 'reactflow'; -import 'reactflow/dist/style.css'; -import { Box } from '@mui/material'; -import CustomNode from './CustomNode'; -import { EXECUTOR_STATES } from '../constants/workflow'; - -const nodeTypes = { - custom: CustomNode, -}; - -// Define the workflow graph structure -const initialNodes = [ - { - id: 'alert_router', - type: 'custom', - position: { x: 400, y: 50 }, - data: { label: 'Alert Router', status: 'idle', description: 'Routes alerts to analysts' }, - }, - { - id: 'usage_pattern_executor', - type: 'custom', - position: { x: 150, y: 200 }, - data: { label: 'Usage Analyst', status: 'idle', description: 'Analyzes usage patterns' }, - }, - { - id: 'location_analysis_executor', - type: 'custom', - position: { x: 400, y: 200 }, - data: { label: 'Location Analyst', status: 'idle', description: 'Analyzes location anomalies' }, - }, - { - id: 'billing_charge_executor', - type: 'custom', - position: { x: 650, y: 200 }, - data: { label: 'Billing Analyst', status: 'idle', description: 'Analyzes billing patterns' }, - }, - { - id: 'fraud_risk_aggregator', - type: 'custom', - position: { x: 400, y: 350 }, - data: { label: 'Risk Aggregator', status: 'idle', description: 'Aggregates risk assessment' }, - }, - { - id: 'review_gateway', - type: 'custom', - position: { x: 550, y: 500 }, - data: { label: 'Review Gateway', status: 'idle', description: 'Human analyst review (pauses workflow)' }, - }, - { - id: 'auto_clear_executor', - type: 'custom', - position: { x: 250, y: 500 }, - data: { label: 'Auto Clear', status: 'idle', description: 'Auto-clears low risk' }, - }, - { - id: 'fraud_action_executor', - type: 'custom', - position: { x: 550, y: 650 }, - data: { label: 'Fraud Action', status: 'idle', description: 'Execute fraud action' }, - }, - { - id: 'final_notification_executor', - type: 'custom', - position: { x: 400, y: 800 }, - data: { label: 'Final Notification', status: 'idle', description: 'Send notifications' }, - }, -]; - -const initialEdges = [ - // Fan-out from alert router to analysts - { id: 'e1-1', source: 'alert_router', target: 'usage_pattern_executor', animated: true }, - { id: 'e1-2', source: 'alert_router', target: 'location_analysis_executor', animated: true }, - { id: 'e1-3', source: 'alert_router', target: 'billing_charge_executor', animated: true }, - - // Fan-in to aggregator - { id: 'e2-1', source: 'usage_pattern_executor', target: 'fraud_risk_aggregator' }, - { id: 'e2-2', source: 'location_analysis_executor', target: 'fraud_risk_aggregator' }, - { id: 'e2-3', source: 'billing_charge_executor', target: 'fraud_risk_aggregator' }, - - // Switch case from aggregator - { id: 'e3-1', source: 'fraud_risk_aggregator', target: 'review_gateway', label: 'High Risk', style: { stroke: '#f44336' } }, - { id: 'e3-2', source: 'fraud_risk_aggregator', target: 'auto_clear_executor', label: 'Low Risk', style: { stroke: '#4caf50' } }, - - // Review gateway to fraud action (human review happens via request_info, then proceeds) - { id: 'e4-1', source: 'review_gateway', target: 'fraud_action_executor', animated: true, style: { stroke: '#ff9800' } }, - - // Final paths - { id: 'e5-1', source: 'auto_clear_executor', target: 'final_notification_executor' }, - { id: 'e5-2', source: 'fraud_action_executor', target: 'final_notification_executor' }, -]; - -/** - * Workflow visualizer component using React Flow - * Displays the fraud detection workflow as an interactive graph - * @param {Object} props - Component props - * @param {Object} props.executorStates - Map of executor IDs to their current states - */ -function WorkflowVisualizer({ executorStates = {} }) { - // Update nodes with current executor states - const nodes = useMemo(() => { - return initialNodes.map((node) => ({ - ...node, - data: { - ...node.data, - status: executorStates[node.id] || EXECUTOR_STATES.IDLE, - }, - })); - }, [executorStates]); - - const [nodesState, setNodes, onNodesChange] = useNodesState(nodes); - const [edgesState, setEdges, onEdgesChange] = useEdgesState(initialEdges); - - // Update nodes when executor states change - useEffect(() => { - setNodes(nodes); - }, [nodes, setNodes]); - - return ( - - - - - { - const status = node.data.status; - if (status === 'running') return '#1976d2'; - if (status === 'completed') return '#4caf50'; - return '#9e9e9e'; - }} - /> - - - ); -} - -export default WorkflowVisualizer; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/constants/config.js b/agentic_ai/workflow/fraud_detection/ui/src/constants/config.js deleted file mode 100644 index b5bbabc03..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/constants/config.js +++ /dev/null @@ -1,33 +0,0 @@ -/** - * API configuration constants - */ -export const API_CONFIG = { - BASE_URL: import.meta.env.VITE_API_BASE_URL || 'http://localhost:8001', - WS_URL: import.meta.env.VITE_WS_URL || 'ws://localhost:8001/ws', - ENDPOINTS: { - ALERTS: '/api/alerts', - WORKFLOW_START: '/api/workflow/start', - WORKFLOW_DECISION: '/api/workflow/decision', - }, -}; - -/** - * WebSocket configuration - */ -export const WS_CONFIG = { - RECONNECT_DELAY: 3000, - MAX_RECONNECT_ATTEMPTS: 10, -}; - -/** - * Application constants - */ -export const APP_CONFIG = { - TITLE: import.meta.env.VITE_APP_TITLE || 'Fraud Detection Workflow Visualizer', -}; - -export default { - API_CONFIG, - WS_CONFIG, - APP_CONFIG, -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/constants/workflow.js b/agentic_ai/workflow/fraud_detection/ui/src/constants/workflow.js deleted file mode 100644 index 07d791540..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/constants/workflow.js +++ /dev/null @@ -1,53 +0,0 @@ -/** - * Workflow event types - */ -export const EVENT_TYPES = { - WORKFLOW_INITIALIZING: 'workflow_initializing', - WORKFLOW_STARTED: 'workflow_started', - WORKFLOW_COMPLETED: 'workflow_completed', - WORKFLOW_ERROR: 'workflow_error', - EXECUTOR_INVOKED: 'executor_invoked', - EXECUTOR_COMPLETED: 'executor_completed', - DECISION_REQUIRED: 'decision_required', - STATUS_CHANGE: 'status_change', - WORKFLOW_OUTPUT: 'workflow_output', -}; - -/** - * Alert severity levels - */ -export const SEVERITY_LEVELS = { - HIGH: 'high', - MEDIUM: 'medium', - LOW: 'low', -}; - -/** - * Executor states - */ -export const EXECUTOR_STATES = { - IDLE: 'idle', - RUNNING: 'running', - COMPLETED: 'completed', - ERROR: 'error', -}; - -/** - * Fraud action options - */ -export const ACTION_OPTIONS = [ - { value: 'clear', label: 'Clear - No Action Needed', color: 'success' }, - { value: 'lock_account', label: 'Lock Account', color: 'error' }, - { value: 'refund_charges', label: 'Refund Charges', color: 'warning' }, - { value: 'both', label: 'Lock Account & Refund', color: 'error' }, -]; - -/** - * WebSocket ready states - */ -export const WS_READY_STATES = { - CONNECTING: 'CONNECTING', - OPEN: 'OPEN', - CLOSING: 'CLOSING', - CLOSED: 'CLOSED', -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js b/agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js deleted file mode 100644 index dd9a4f13d..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/hooks/useWebSocket.js +++ /dev/null @@ -1,113 +0,0 @@ -import { useState, useEffect, useRef, useCallback } from 'react'; -import { WS_CONFIG } from '../constants/config'; - -/** - * Custom hook for managing WebSocket connections with automatic reconnection - * Uses a message queue to ensure no messages are lost when they arrive quickly - * @param {string} url - WebSocket URL to connect to - * @returns {Object} Object containing lastMessage, readyState, and sendMessage function - */ -export function useWebSocket(url) { - const [lastMessage, setLastMessage] = useState(null); - const [readyState, setReadyState] = useState('CONNECTING'); - const ws = useRef(null); - const reconnectTimeout = useRef(null); - const reconnectAttempts = useRef(0); - - // Message queue to prevent lost messages during rapid updates - const messageQueue = useRef([]); - const processingQueue = useRef(false); - - // Process messages from queue one at a time - const processQueue = useCallback(() => { - if (processingQueue.current || messageQueue.current.length === 0) { - return; - } - - processingQueue.current = true; - const message = messageQueue.current.shift(); - - // Use setTimeout to ensure React processes each state update - setLastMessage(message); - - // Schedule next message processing - setTimeout(() => { - processingQueue.current = false; - processQueue(); - }, 0); - }, []); - - const connect = useCallback(() => { - try { - ws.current = new WebSocket(url); - - ws.current.onopen = () => { - console.log('WebSocket connected'); - setReadyState('OPEN'); - reconnectAttempts.current = 0; - }; - - ws.current.onmessage = (event) => { - try { - const data = JSON.parse(event.data); - // Add to queue instead of directly setting state - messageQueue.current.push(data); - processQueue(); - } catch (error) { - console.error('Error parsing WebSocket message:', error); - } - }; - - ws.current.onerror = (error) => { - console.error('WebSocket error:', error); - }; - - ws.current.onclose = () => { - console.log('WebSocket disconnected'); - setReadyState('CLOSED'); - - // Attempt to reconnect if under max attempts - if (reconnectAttempts.current < WS_CONFIG.MAX_RECONNECT_ATTEMPTS) { - reconnectTimeout.current = setTimeout(() => { - reconnectAttempts.current += 1; - console.log( - `Attempting to reconnect... (${reconnectAttempts.current}/${WS_CONFIG.MAX_RECONNECT_ATTEMPTS})` - ); - connect(); - }, WS_CONFIG.RECONNECT_DELAY); - } else { - console.error('Max reconnection attempts reached'); - } - }; - } catch (error) { - console.error('Error creating WebSocket:', error); - } - }, [url, processQueue]); - - useEffect(() => { - connect(); - - return () => { - if (reconnectTimeout.current) { - clearTimeout(reconnectTimeout.current); - } - if (ws.current) { - ws.current.close(); - } - }; - }, [connect]); - - const sendMessage = useCallback((message) => { - if (ws.current && ws.current.readyState === WebSocket.OPEN) { - ws.current.send(JSON.stringify(message)); - } else { - console.warn('WebSocket is not open. Ready state:', ws.current?.readyState); - } - }, []); - - return { - lastMessage, - readyState, - sendMessage, - }; -} diff --git a/agentic_ai/workflow/fraud_detection/ui/src/index.css b/agentic_ai/workflow/fraud_detection/ui/src/index.css deleted file mode 100644 index 0979488ae..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/index.css +++ /dev/null @@ -1,18 +0,0 @@ -body { - margin: 0; - font-family: 'Roboto', -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Oxygen', - 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', - sans-serif; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; - background-color: #f5f5f5; -} - -* { - box-sizing: border-box; -} - -#root { - height: 100vh; - width: 100vw; -} diff --git a/agentic_ai/workflow/fraud_detection/ui/src/main.jsx b/agentic_ai/workflow/fraud_detection/ui/src/main.jsx deleted file mode 100644 index 8e3e8340e..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/main.jsx +++ /dev/null @@ -1,18 +0,0 @@ -import { StrictMode } from 'react'; -import { createRoot } from 'react-dom/client'; -import App from './App'; -import './index.css'; - -const rootElement = document.getElementById('root'); - -if (!rootElement) { - throw new Error('Failed to find the root element'); -} - -const root = createRoot(rootElement); - -root.render( - - - -); diff --git a/agentic_ai/workflow/fraud_detection/ui/src/theme/index.js b/agentic_ai/workflow/fraud_detection/ui/src/theme/index.js deleted file mode 100644 index 799f49a2f..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/theme/index.js +++ /dev/null @@ -1,84 +0,0 @@ -import { createTheme } from '@mui/material'; - -/** - * Application theme configuration - * Uses Material-UI theme customization - */ -export const theme = createTheme({ - palette: { - mode: 'light', - primary: { - main: '#1976d2', - light: '#42a5f5', - dark: '#1565c0', - contrastText: '#fff', - }, - secondary: { - main: '#dc004e', - light: '#f33a6a', - dark: '#9a0036', - contrastText: '#fff', - }, - success: { - main: '#4caf50', - light: '#81c784', - dark: '#388e3c', - }, - warning: { - main: '#ff9800', - light: '#ffb74d', - dark: '#f57c00', - }, - error: { - main: '#f44336', - light: '#e57373', - dark: '#d32f2f', - }, - info: { - main: '#2196f3', - light: '#64b5f6', - dark: '#1976d2', - }, - background: { - default: '#f5f5f5', - paper: '#ffffff', - }, - }, - typography: { - fontFamily: '"Roboto", "Helvetica", "Arial", sans-serif', - h6: { - fontWeight: 500, - }, - subtitle1: { - fontWeight: 500, - }, - body1: { - fontSize: '0.875rem', - }, - body2: { - fontSize: '0.75rem', - }, - }, - shape: { - borderRadius: 8, - }, - components: { - MuiButton: { - styleOverrides: { - root: { - textTransform: 'none', - borderRadius: 8, - }, - }, - }, - MuiPaper: { - styleOverrides: { - root: { - borderRadius: 8, - }, - }, - }, - }, -}); - -export default theme; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/utils/api.js b/agentic_ai/workflow/fraud_detection/ui/src/utils/api.js deleted file mode 100644 index 26ea9986c..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/utils/api.js +++ /dev/null @@ -1,73 +0,0 @@ -import { API_CONFIG } from '../constants/config'; - -/** - * Fetches alerts from the API - * @returns {Promise} Array of alerts - */ -export const fetchAlerts = async () => { - try { - const response = await fetch(`${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.ALERTS}`); - if (!response.ok) { - throw new Error(`HTTP error! status: ${response.status}`); - } - const data = await response.json(); - return data.alerts || []; - } catch (error) { - console.error('Error loading alerts:', error); - throw error; - } -}; - -/** - * Starts a workflow for a given alert - * @param {Object} alert - The alert object - * @returns {Promise} Response data - */ -export const startWorkflow = async (alert) => { - try { - const response = await fetch(`${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.WORKFLOW_START}`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify(alert), - }); - - if (!response.ok) { - throw new Error(`HTTP error! status: ${response.status}`); - } - - const data = await response.json(); - return data; - } catch (error) { - console.error('Error starting workflow:', error); - throw error; - } -}; - -/** - * Submits an analyst decision - * @param {Object} decision - The decision object - * @returns {Promise} Response data - */ -export const submitDecision = async (decision) => { - try { - const response = await fetch(`${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.WORKFLOW_DECISION}`, { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify(decision), - }); - - if (!response.ok) { - throw new Error(`HTTP error! status: ${response.status}`); - } - - const data = await response.json(); - return data; - } catch (error) { - console.error('Error submitting decision:', error); - throw error; - } -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/utils/helpers.js b/agentic_ai/workflow/fraud_detection/ui/src/utils/helpers.js deleted file mode 100644 index 8f303b5cc..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/utils/helpers.js +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Event helpers for workflow event processing - */ - -/** - * Generates a unique key for an event - * @param {Object} event - The event object - * @returns {string} Unique event key - */ -export const generateEventKey = (event) => { - return `${event.timestamp}-${event.type || event.event_type}-${event.executor_id || ''}`; -}; - -/** - * Checks if an event is a duplicate in an array of events - * @param {Object} event - The event to check - * @param {Array} events - Existing events array - * @returns {boolean} True if duplicate - */ -export const isDuplicateEvent = (event, events) => { - const eventKey = generateEventKey(event); - return events.some((e) => generateEventKey(e) === eventKey); -}; - -/** - * Formats a timestamp for display - * @param {string|number} timestamp - The timestamp - * @returns {string} Formatted time string - */ -export const formatTime = (timestamp) => { - const date = new Date(timestamp); - return date.toLocaleTimeString('en-US', { - hour: '2-digit', - minute: '2-digit', - second: '2-digit', - hour12: false, - }); -}; - -/** - * Formats a date and time for display - * @param {string|number} timestamp - The timestamp - * @returns {string} Formatted datetime string - */ -export const formatDateTime = (timestamp) => { - const date = new Date(timestamp); - return date.toLocaleString('en-US', { - month: 'short', - day: 'numeric', - hour: '2-digit', - minute: '2-digit', - second: '2-digit', - hour12: false, - }); -}; - -/** - * Truncates text to a maximum length - * @param {string} text - Text to truncate - * @param {number} maxLength - Maximum length - * @returns {string} Truncated text - */ -export const truncateText = (text, maxLength = 100) => { - if (!text || text.length <= maxLength) return text; - return `${text.substring(0, maxLength)}...`; -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/src/utils/uiHelpers.jsx b/agentic_ai/workflow/fraud_detection/ui/src/utils/uiHelpers.jsx deleted file mode 100644 index 83723375a..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/src/utils/uiHelpers.jsx +++ /dev/null @@ -1,189 +0,0 @@ -/** - * UI helper functions for components - */ - -/** - * Gets the appropriate icon for alert severity - * @param {string} severity - Alert severity level - * @param {Object} icons - Icon components object - * @returns {JSX.Element} Icon component - */ -export const getSeverityIcon = (severity, icons) => { - const { ErrorIcon, WarningIcon, InfoIcon } = icons; - switch (severity) { - case 'high': - return ; - case 'medium': - return ; - case 'low': - return ; - default: - return ; - } -}; - -/** - * Gets the appropriate color for alert severity - * @param {string} severity - Alert severity level - * @returns {string} MUI color name - */ -export const getSeverityColor = (severity) => { - switch (severity) { - case 'high': - return 'error'; - case 'medium': - return 'warning'; - case 'low': - return 'info'; - default: - return 'default'; - } -}; - -/** - * Gets the appropriate icon for event type - * @param {Object} event - Event object - * @param {Object} icons - Icon components object - * @returns {JSX.Element} Icon component - */ -export const getEventIcon = (event, icons) => { - const { PlayArrowIcon, CheckCircleIcon, InfoIcon, GavelIcon, ErrorIcon } = icons; - - switch (event.event_type) { - case 'executor_invoked': - return ; - case 'executor_completed': - return ; - case 'status_change': - return ; - case 'workflow_output': - return ; - default: - if (event.type === 'decision_required') { - return ; - } - if (event.type === 'workflow_error') { - return ; - } - return ; - } -}; - -/** - * Gets the appropriate color for event type - * @param {Object} event - Event object - * @returns {string} MUI color name - */ -export const getEventColor = (event) => { - switch (event.event_type) { - case 'executor_invoked': - return 'primary'; - case 'executor_completed': - return 'success'; - case 'status_change': - return 'info'; - case 'workflow_output': - return 'success'; - default: - if (event.type === 'decision_required') { - return 'warning'; - } - if (event.type === 'workflow_error') { - return 'error'; - } - return 'default'; - } -}; - -/** - * Gets the display title for an event - * @param {Object} event - Event object - * @returns {string} Event title - */ -export const getEventTitle = (event) => { - if (event.event_type === 'executor_invoked') { - return `${event.executor_id} started`; - } - if (event.event_type === 'executor_completed') { - return `${event.executor_id} completed`; - } - if (event.event_type === 'status_change') { - return `Status: ${event.status}`; - } - if (event.event_type === 'workflow_output') { - return 'Workflow Output'; - } - if (event.type === 'decision_required') { - return 'Decision Required'; - } - if (event.type === 'workflow_started') { - return 'Workflow Started'; - } - if (event.type === 'workflow_completed') { - return 'Workflow Completed'; - } - if (event.type === 'workflow_error') { - return 'Error Occurred'; - } - return event.type || 'Event'; -}; - -/** - * Gets risk level from risk score - * @param {number} score - Risk score (0-1) - * @returns {string} Risk level - */ -export const getRiskLevel = (score) => { - if (score >= 0.8) return 'Critical'; - if (score >= 0.6) return 'High'; - if (score >= 0.3) return 'Medium'; - return 'Low'; -}; - -/** - * Gets risk color from risk score - * @param {number} score - Risk score (0-1) - * @returns {string} MUI color name - */ -export const getRiskColor = (score) => { - if (score >= 0.8) return 'error'; - if (score >= 0.6) return 'warning'; - if (score >= 0.3) return 'info'; - return 'success'; -}; - -/** - * Gets node status color configuration - * @param {string} status - Node status - * @returns {Object} Color configuration object - */ -export const getNodeStatusColor = (status) => { - switch (status) { - case 'running': - return { bg: '#1976d2', text: '#ffffff' }; - case 'completed': - return { bg: '#4caf50', text: '#ffffff' }; - case 'error': - return { bg: '#f44336', text: '#ffffff' }; - default: - return { bg: '#ffffff', text: '#000000' }; - } -}; - -/** - * Gets status label for display - * @param {string} status - Status value - * @returns {string} Display label - */ -export const getStatusLabel = (status) => { - switch (status) { - case 'running': - return 'Running'; - case 'completed': - return 'Completed'; - case 'error': - return 'Error'; - default: - return 'Idle'; - } -}; diff --git a/agentic_ai/workflow/fraud_detection/ui/vite.config.js b/agentic_ai/workflow/fraud_detection/ui/vite.config.js deleted file mode 100644 index e05580dca..000000000 --- a/agentic_ai/workflow/fraud_detection/ui/vite.config.js +++ /dev/null @@ -1,34 +0,0 @@ -import { defineConfig } from 'vite' -import react from '@vitejs/plugin-react' - -// https://vitejs.dev/config/ -export default defineConfig({ - plugins: [react()], - server: { - port: 3000, - open: true, - proxy: { - '/api': { - target: 'http://localhost:8001', - changeOrigin: true, - }, - '/ws': { - target: 'ws://localhost:8001', - ws: true, - }, - }, - }, - build: { - outDir: 'dist', - sourcemap: true, - rollupOptions: { - output: { - manualChunks: { - vendor: ['react', 'react-dom'], - mui: ['@mui/material', '@mui/icons-material'], - reactflow: ['reactflow'], - }, - }, - }, - }, -}) diff --git a/agentic_ai/workflow/fraud_detection/uv.lock b/agentic_ai/workflow/fraud_detection/uv.lock deleted file mode 100644 index 0576fe255..000000000 --- a/agentic_ai/workflow/fraud_detection/uv.lock +++ /dev/null @@ -1,3039 +0,0 @@ -version = 1 -revision = 1 -requires-python = ">=3.12" -resolution-markers = [ - "python_full_version >= '3.13'", - "python_full_version < '3.13'", -] - -[options] -prerelease-mode = "allow" - -[[package]] -name = "a2a-sdk" -version = "0.3.8" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "google-api-core" }, - { name = "httpx" }, - { name = "httpx-sse" }, - { name = "protobuf" }, - { name = "pydantic" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/b3/dc/253ca3a07a43f49d1cf029dcbbcde8b101a3dbee1c115e45deae9c79625b/a2a_sdk-0.3.8.tar.gz", hash = "sha256:7ce8bcddb431db3f29753d602e9fa97dd060ef6c5db64542b5109302941f0d17", size = 223907 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/73/23/bf86f1d3a04a6d967176e20939a98207f8a8ed49480622ad6cf5ce232083/a2a_sdk-0.3.8-py3-none-any.whl", hash = "sha256:21254dd47d89a958b9d15576a69fe3d44aaef558858d148e187d1f1e26b320e7", size = 138098 }, -] - 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In production every process maps to an **Azure Container App (ACA)** revision — but the topology is _not_ uniform. The worker has **no ingress at all**; the MCP server is **internal-only**; and the backend is the **sole external surface**. + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#4A90D9', 'primaryTextColor': '#fff', 'primaryBorderColor': '#2E6BA4', 'lineColor': '#5C6B77', 'secondaryColor': '#F5A623', 'tertiaryColor': '#E8F4FD'}}}%% +flowchart TB + subgraph INTERNET["☁️ Internet"] + USER["🧑‍💻 Analyst / Browser"] + end + + subgraph AZURE["Azure Subscription"] + subgraph ACA_ENV["Azure Container Apps Environment"] + direction TB + + subgraph BACKEND["Backend (ACA)
── only external ingress ──"] + BE_APP["FastAPI BFF
REST + SSE + WebSocket
Port 8001"] + end + + subgraph WORKER["Worker (ACA)
── NO ingress ──"] + W_APP["DTS Worker
Pull-based gRPC
FraudAnalysisAgent Entity
Orchestration + Activities"] + end + + subgraph MCP_SVC["MCP Server (ACA)
── internal-only ingress ──"] + MCP_APP["FastMCP
Contoso Tools
Port 8000"] + end + end + + DTS["🔷 Azure Durable Task
Scheduler (DTS)
──────────────
Task Hub · Event Store
Timer Service"] + AOAI["🤖 Azure OpenAI
gpt-4o / gpt-5"] + COSMOS["🗄️ Cosmos DB
Customer Data
(replaces SQLite)"] + EVENTHUB["📡 Event Hubs
Real Telemetry
(replaces synthetic producer)"] + APPINS["📊 Application Insights
OpenTelemetry"] + SWA["🌐 Azure Static Web Apps
React UI"] + end + + USER -->|"HTTPS"| SWA + SWA -->|"API calls"| BE_APP + USER -->|"REST / SSE / WS"| BE_APP + + BE_APP -->|"gRPC (schedule_new_orchestration,
raise_event, get_instance)"| DTS + W_APP -.->|"gRPC PULL
(long-poll for work items)"| DTS + W_APP -->|"Streamable HTTP"| MCP_APP + W_APP -->|"Chat Completions API"| AOAI + MCP_APP -->|"Data queries"| COSMOS + EVENTHUB -->|"Ingested by"| BE_APP + + BE_APP -.->|"traces"| APPINS + W_APP -.->|"traces"| APPINS + + style INTERNET fill:#f0f4f8,stroke:#ccc + style AZURE fill:#e8f4fd,stroke:#2E6BA4,stroke-width:2px + style ACA_ENV fill:#fff,stroke:#4A90D9,stroke-width:2px + style BACKEND fill:#d4edda,stroke:#28a745 + style WORKER fill:#ffeeba,stroke:#ffc107 + style MCP_SVC fill:#d6d8db,stroke:#6c757d + style DTS fill:#cce5ff,stroke:#004085 + style AOAI fill:#e2d5f1,stroke:#6f42c1 + style COSMOS fill:#fff3cd,stroke:#856404 + style EVENTHUB fill:#f8d7da,stroke:#721c24 + style APPINS fill:#d1ecf1,stroke:#0c5460 + style SWA fill:#d4edda,stroke:#155724 +``` + +--- + +## Component Deep-Dive + +### 1. Worker — No Ingress, Pull-Based + +| Property | Value | +|----------|-------| +| **Ingress** | None — no HTTP port exposed | +| **Communication** | Outbound gRPC to DTS (long-poll for work items) | +| **Scaling trigger** | KEDA scaler watching DTS task-hub queue depth | +| **Min replicas** | 1 (must always be polling) | +| **Max replicas** | 10+ (each replica pulls independently) | +| **Identity** | Managed Identity — `DefaultAzureCredential()` | + +The worker **never receives inbound requests**. It calls `worker.start()` which opens a persistent gRPC stream to DTS, pulling orchestration work items, activity tasks, and entity operations. When there's nothing to do, it simply blocks on the long-poll. + +```mermaid +sequenceDiagram + participant DTS as Azure DTS + participant W as Worker (ACA) + participant AOAI as Azure OpenAI + participant MCP as MCP Server + + loop Pull-based work loop + W->>DTS: gRPC: GetWorkItems() [long-poll] + DTS-->>W: WorkItem (orchestration / activity / entity) + + alt Orchestration step + W->>W: Replay event history → resume generator + W->>DTS: CompleteOrchestration(actions=[]) + else Entity operation (agent call) + W->>MCP: Streamable HTTP (MCP tools) + MCP-->>W: Tool results + W->>AOAI: Chat Completions (with tool results) + AOAI-->>W: Assessment JSON + W->>DTS: CompleteEntity(state=DurableAgentState) + else Activity task + W->>W: Execute activity function + W->>DTS: CompleteActivity(result) + end + end +``` + +> **Key insight:** Because the worker has no ingress, there is **zero attack surface**. The only way to make the worker do something is to schedule an orchestration in DTS — which requires the `Durable Task Data Contributor` RBAC role. + +### 2. MCP Server — Internal-Only + +| Property | Value | +|----------|-------| +| **Ingress** | Internal only (ACA environment VNET) | +| **Consumers** | Worker only | +| **Protocol** | Streamable HTTP (`/mcp`) | +| **Data store** | Cosmos DB (replaces SQLite) | +| **Identity** | Managed Identity to Cosmos DB | + +The MCP server exposes Contoso business tools (customer lookup, transaction history, subscription details). In the workshop it uses SQLite; in production, swap the backend module to Cosmos DB — the MCP tool surface stays identical. + +### 3. Backend — The Only External Surface + +| Property | Value | +|----------|-------| +| **Ingress** | External HTTPS (public or VNET-integrated) | +| **Role** | Backend-for-Frontend (BFF) | +| **APIs** | REST, SSE, WebSocket | +| **DTS interaction** | gRPC client (schedule, query, raise events) | +| **Identity** | Managed Identity to DTS | + +The backend translates browser-friendly protocols into DTS SDK calls: + +```mermaid +flowchart LR + subgraph Browser + A["React UI"] + end + + subgraph Backend["Backend (ACA)"] + B1["POST /api/workflow/start"] --> B2["client.schedule_new_orchestration()"] + B3["POST /api/workflow/decision"] --> B4["client.raise_orchestration_event()"] + B5["GET /api/workflow/status/:id"] --> B6["client.get_instance_metadata()"] + B7["WS /api/workflow/ws/:id"] --> B8["client.wait_for_completion()"] + B9["GET /api/events/stream"] --> B10["SSE: telemetry + alerts"] + end + + subgraph DTS["Azure DTS"] + C["Task Hub"] + end + + A --> B1 + A --> B3 + A --> B5 + A --> B7 + A --> B9 + B2 --> C + B4 --> C + B6 --> C + B8 --> C + + style Browser fill:#e8f4fd,stroke:#4A90D9 + style Backend fill:#d4edda,stroke:#28a745 + style DTS fill:#cce5ff,stroke:#004085 +``` + +### 4. React UI — Static Web App + +The React UI is a static bundle (no server rendering). In production, deploy to **Azure Static Web Apps** with an API proxy rule routing `/api/*` to the Backend ACA. + +--- + +## What Changes From Workshop to Production? + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#4A90D9'}}}%% +flowchart LR + subgraph WORKSHOP["Workshop (localhost)"] + direction TB + W_DTS["Docker DTS Emulator
localhost:8080"] + W_MCP["MCP + SQLite
localhost:8000"] + W_WORKER["Worker process"] + W_BACKEND["Backend process
localhost:8001"] + W_UI["Vite dev server
localhost:3000"] + W_EVENTS["Synthetic event_producer.py"] + end + + subgraph PRODUCTION["Production (Azure)"] + direction TB + P_DTS["Azure DTS
Managed service"] + P_MCP["MCP + Cosmos DB
Internal ACA"] + P_WORKER["Worker ACA
No ingress"] + P_BACKEND["Backend ACA
External HTTPS"] + P_UI["Static Web App"] + P_EVENTS["Event Hubs
Real telemetry"] + end + + W_DTS -.->|"Same SDK, change endpoint"| P_DTS + W_MCP -.->|"Swap SQLite → Cosmos"| P_MCP + W_WORKER -.->|"Same code, add MI"| P_WORKER + W_BACKEND -.->|"Same code, add MI"| P_BACKEND + W_UI -.->|"npm build → deploy"| P_UI + W_EVENTS -.->|"Replace with real data"| P_EVENTS + + style WORKSHOP fill:#fff3cd,stroke:#856404,stroke-width:2px + style PRODUCTION fill:#d4edda,stroke:#28a745,stroke-width:2px +``` + +### Code Changes Required + +| Component | Change | LOC Impact | +|-----------|--------|------------| +| **Worker** | `DTS_ENDPOINT` env var → Azure URL | 0 lines (config only) | +| **Worker** | `DefaultAzureCredential()` already used when not localhost | 0 lines | +| **Backend** | Same as worker | 0 lines | +| **MCP Server** | Swap `_backend_sqlite.py` → `_backend_cosmos.py` | ~1 import line | +| **UI** | `npm run build` → deploy static files | 0 lines | +| **Event Producer** | Replace with Event Hubs consumer | ~50 lines (new module) | + +> **Total:** Near-zero code changes. The architecture was designed for this — `DTS_ENDPOINT` is the only knob. + +--- + +## Security Model + +```mermaid +flowchart TB + subgraph IDENTITY["🔐 Managed Identity"] + MI_WORKER["Worker MI"] + MI_BACKEND["Backend MI"] + MI_MCP["MCP MI"] + end + + MI_WORKER -->|"Durable Task Data Contributor"| DTS["Azure DTS"] + MI_WORKER -->|"Cognitive Services OpenAI User"| AOAI["Azure OpenAI"] + MI_BACKEND -->|"Durable Task Data Contributor"| DTS + MI_MCP -->|"Cosmos DB Data Reader"| COSMOS["Cosmos DB"] + + subgraph NETWORK["🌐 Network"] + EXT["External Traffic"] -->|"HTTPS only"| BE["Backend"] + BE -->|"Internal VNET"| MCP_S["MCP Server"] + WORKER_S["Worker"] -->|"Internal VNET"| MCP_S + WORKER_S -.->|"Outbound gRPC"| DTS + end + + style IDENTITY fill:#e2d5f1,stroke:#6f42c1,stroke-width:2px + style NETWORK fill:#e8f4fd,stroke:#4A90D9,stroke-width:2px +``` + +**Key principles:** +- **No API keys in production** — Managed Identity everywhere +- **No secrets in environment variables** — `DefaultAzureCredential()` auto-discovers MI +- **Worker has zero ingress** — no attack surface, pull-only +- **MCP server is internal-only** — not reachable from internet +- **Backend is the sole entry point** — all external traffic funneled through one surface + +--- + +## Scaling Characteristics + +```mermaid +%%{init: {'theme': 'base'}}%% +xychart-beta + title "Scaling Profile by Component" + x-axis ["Worker", "Backend", "MCP Server", "DTS"] + y-axis "Scaling Dimension" 0 --> 5 + bar [5, 3, 2, 5] +``` + +| Component | Scaling Model | Trigger | Notes | +|-----------|--------------|---------|-------| +| **Worker** | Horizontal (KEDA) | DTS queue depth | Each replica pulls independently. Stateless — all state lives in DTS. | +| **Backend** | Horizontal (HTTP) | Request count / CPU | Standard HTTP scaling. SSE connections are long-lived. | +| **MCP Server** | Horizontal (HTTP) | Request count | Stateless tool calls. Scale with worker count. | +| **DTS** | Managed service | Automatic | Microsoft manages partitioning and throughput. | + +### Why the Worker Scales So Well + +The worker is **completely stateless**. All durable state — orchestration event logs, entity state (`DurableAgentState`), timers, external events — lives in DTS storage. When you add a second worker replica: + +1. It calls `worker.start()` and begins long-polling DTS +2. DTS distributes work items across all connected workers +3. Any replica can pick up any orchestration — replay rebuilds state from the event log +4. If a replica dies, its in-progress work item times out and another replica picks it up + +This is the same model Azure Durable Functions uses at massive scale. + +--- + +## Deployment Checklist + +- [ ] Provision Azure DTS (Consumption SKU) + Task Hub +- [ ] Provision Azure Container Apps Environment with VNET +- [ ] Deploy Worker ACA (no ingress, MI enabled) +- [ ] Deploy MCP Server ACA (internal ingress, MI enabled) +- [ ] Deploy Backend ACA (external ingress, MI enabled) +- [ ] Assign RBAC: Worker MI → `Durable Task Data Contributor` on DTS +- [ ] Assign RBAC: Backend MI → `Durable Task Data Contributor` on DTS +- [ ] Assign RBAC: Worker MI → `Cognitive Services OpenAI User` on Azure OpenAI +- [ ] Assign RBAC: MCP MI → Cosmos DB `Data Reader` role +- [ ] Deploy React UI to Static Web Apps with proxy to Backend +- [ ] Configure Event Hubs for real telemetry ingestion +- [ ] Enable Application Insights for all containers +- [ ] Test: `provision_dts.ps1` covers DTS + RBAC steps + +--- + +## Cost Estimation (Consumption Tier) + +| Resource | SKU | Estimated Monthly Cost | +|----------|-----|----------------------| +| Azure DTS | Consumption | Pay-per-execution (~$0.01/1K orchestrations) | +| ACA Worker | Consumption | Pay-per-vCPU-second (idle = ~$0) | +| ACA Backend | Consumption | Pay-per-request | +| ACA MCP Server | Consumption | Pay-per-request | +| Azure OpenAI | Pay-as-you-go | ~$0.01–0.03 per investigation | +| Cosmos DB | Serverless | Pay-per-RU | +| Static Web Apps | Free tier | $0 | +| Application Insights | Pay-per-GB | First 5GB/month free | + +> For a low-volume fraud detection system (~100 alerts/day), expect **< $50/month** total. + +--- + +*Back to [Workshop README](README.md)* diff --git a/agentic_ai/workflow/fraud_detection_durable/README.md b/agentic_ai/workflow/fraud_detection_durable/README.md index 5aa5c7753..c64a376fd 100644 --- a/agentic_ai/workflow/fraud_detection_durable/README.md +++ b/agentic_ai/workflow/fraud_detection_durable/README.md @@ -1,360 +1,728 @@ -# Durable Fraud Detection Workflow +# Durable Agentic Workflows — A Design Pattern for Long-Running AI Agent Orchestration -A hybrid architecture combining **Workflow** (complex topology) and **Durable Task** (durability, HITL) for enterprise-grade fraud detection. +## The Problem: AI Agents That Must Not Forget -## 🏗️ Architecture +Most AI agent demos run in a single request-response cycle: user asks, agent thinks, agent answers. But real-world agent systems need to do things that take **minutes, hours, or days** — and they must survive failures along the way. -``` -┌─────────────────────────────────────────────────────────────────────────┐ -│ DURABLE TASK ORCHESTRATION (Outer Layer) │ -│ Handles: Durability, Long Waits, Crash Recovery │ -│ │ -│ ┌─────────────────────────────────────────────────────────────────┐ │ -│ │ 1. Receive Alert │ │ -│ │ 2. Call "run_fraud_analysis" Activity ──────────────────────┐ │ │ -│ │ │ │ │ -│ │ ┌─────────────────────────────────────────────────────┐ │ │ │ -│ │ │ WORKFLOW (Inner Layer - Activity) │ │ │ │ -│ │ │ Handles: Complex Topology, Fast Execution │ │ │ │ -│ │ │ │ │ │ │ -│ │ │ AlertRouter │ │ │ │ -│ │ │ ↓ │ │ │ │ -│ │ │ ┌───┴───┬───────┐ (fan-out) │ │ │ │ -│ │ │ ↓ ↓ ↓ │ │ │ │ -│ │ │ Usage Location Billing │ │ │ │ -│ │ │ └───────┼───────┘ (fan-in) │ │ │ │ -│ │ │ ↓ │ │ │ │ -│ │ │ Aggregator (LLM) │ │ │ │ -│ │ │ ↓ │ │ │ │ -│ │ │ Returns: FraudRiskAssessment │ │ │ │ -│ │ └─────────────────────────────────────────────────────┘ │ │ │ -│ │ ◄─────┘ │ │ -│ │ 3. Check Risk Score (simple if/else) │ │ -│ │ │ │ -│ │ IF risk >= 0.6: │ │ -│ │ → notify_analyst Activity │ │ -│ │ → wait_for_external_event("AnalystDecision") ⏸️ │ │ -│ │ → execute_fraud_action Activity │ │ -│ │ │ │ -│ │ ELSE: │ │ -│ │ → auto_clear Activity │ │ -│ │ │ │ -│ │ 4. send_notification Activity │ │ -│ └─────────────────────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────────────┘ -``` +Consider what happens when an AI agent needs to: -## 🎯 Why Hybrid Architecture? +- **Monitor transactions 24/7 for fraud** — continuously ingest telemetry events, detect anomalies in real time, autonomously launch multi-agent investigations, then wait for a human analyst to approve or reject the recommended action (which might take hours or happen over a weekend). This is an *ambient agent* — it runs in the background with no user prompt, watching for patterns that require intervention +- **Manage an IT incident** — detect anomaly, triage severity, page on-call engineer, wait for acknowledgment (with escalation if no response in 15 minutes), coordinate remediation steps, produce post-mortem +- **Process a loan application** — pull credit reports, verify employment, run compliance checks in parallel, wait for underwriter approval, then execute the disbursement +- **Orchestrate a supply chain order** — validate inventory across warehouses, reserve stock, calculate shipping, wait for supplier confirmation, handle partial fulfillments, retry failed shipments -| Feature | Workflow Only | Durable Task Only | **Hybrid (This)** | -|---------|--------------|-------------------|-------------------| -| Complex topology (fan-out/fan-in) | ✅ Easy | ❌ Manual | ✅ Easy | -| Crash recovery | ❌ Lost state | ✅ Automatic | ✅ Automatic | -| Human-in-the-loop | ⚠️ Manual checkpoints | ✅ Built-in events | ✅ Built-in events | -| Timeout handling | ❌ Not built-in | ✅ Native timers | ✅ Native timers | -| Long waits (hours/days) | ❌ Memory-bound | ✅ Persistent | ✅ Persistent | -| Visibility/Dashboard | ❌ Custom logging | ✅ DTS Dashboard | ✅ DTS Dashboard | +These scenarios share a common trait: **no human initiates the work**. Events arrive continuously — transactions, alerts, sensor readings, log entries — and the agent must watch, decide, act, and sometimes wait for human input before proceeding. This is fundamentally different from chatbot-style "user asks, agent answers" interactions. -## 📁 Project Structure +These are **long-running, multi-step, stateful workflows** where: -``` -fraud_detection_durable/ -├── pyproject.toml # Dependencies -├── .env.sample # Environment template -├── fraud_analysis_workflow.py # Inner workflow (fan-out → aggregate) -├── worker.py # DTS Worker with orchestration -├── client.py # CLI client for testing -├── backend.py # FastAPI backend for UI -├── README.md # This file -└── ui/ # React UI - ├── src/ - │ ├── App.jsx # Main app with WebSocket connection - │ └── components/ - │ └── WorkflowVisualizer.jsx # Interactive workflow diagram - └── package.json -``` +1. **Human decisions inject unbounded delays** — an analyst might respond in 5 minutes or 5 days +2. **Multiple agents collaborate** — a router dispatches to specialists, an aggregator synthesizes results +3. **Failures are inevitable** — processes crash, containers restart, VMs get preempted, network connections drop +4. **Actions must not be repeated** — you can't lock a bank account twice or charge a customer twice because the worker restarted -## 🚀 Quick Start +The fundamental question is: **when the process running your agent dies, what happens to all the work it already completed?** -### Prerequisites +--- -1. **Docker** - For DTS emulator -2. **Python 3.12+** -3. **Azure OpenAI** - With a deployed model -4. **MCP Server** - Running on port 8000 +## Why Building This Yourself Is Harder Than You Think -### Step 1: Start Durable Task Scheduler +The instinct is to reach for familiar tools: "I'll just checkpoint my state to a database or blob storage." This works for simple linear flows, but falls apart in real agent orchestration scenarios. Here are the five problems you'll need to solve — and each one is a distributed systems project in its own right. -```bash -docker run -d --name dts-emulator \ - -p 8080:8080 -p 8082:8082 \ - mcr.microsoft.com/dts/dts-emulator:latest -``` +### 1. The Wait-for-Human Problem -Dashboard: http://localhost:8082 +Your orchestration reaches a point where it needs a human decision. The agent has produced a risk assessment; now an analyst must approve or reject. -### Step 2: Start MCP Server +**DIY approach:** Save current state to blob → poll a database for the decision → resume. -```bash -cd mcp -uv run mcp_service.py -``` +**What goes wrong:** +- You need an event subscription system: when the analyst submits a decision via your API, something must correlate that decision back to the specific waiting orchestration instance +- You need a timeout mechanism: if no decision arrives in 72 hours, escalate automatically +- You need both to race: whichever fires first (human decision or timeout) wins, and the other must be cancelled cleanly +- All of this must survive process restarts — the timer can't live in `asyncio`; the event subscription can't live in a Python dict -### Step 3: Configure Environment +With a durable orchestrator, this is one line: -```bash -cd agentic_ai/workflow/fraud_detection_durable -cp .env.sample .env -# Edit .env with your Azure OpenAI credentials +```python +winner = yield when_any([ + context.wait_for_external_event("AnalystDecision"), + context.create_timer(timedelta(hours=72)) +]) ``` -### Step 4: Install Dependencies +The event subscription, the timer, and the race — all persisted in the orchestrator's storage, not in worker memory. -```bash -uv sync -``` +### 2. The Replay/Resume Problem -### Step 5: Start Worker +Your workflow has 8 steps. It crashed after step 5. How do you resume at step 6? -```bash -uv run worker.py -``` +**DIY approach:** Save a `current_step` counter → on restart, load it → use a giant if/elif chain to jump to the right step. -### Step 6: Run Tests +**What goes wrong:** +- Each step's *output* was consumed by later steps. You need to save every intermediate result, not just the step number +- If your workflow has branches (`if risk > 0.6: wait for human; else: auto-clear`), the if/elif chain grows exponentially +- If your workflow has loops (analyst rejects → re-investigate → wait again), the state machine becomes nearly impossible to maintain +- Every time you change the workflow logic, you must update the resume logic in lockstep -**Option A: CLI Client** -```bash -uv run client.py +With a durable orchestrator, there is no resume logic. The framework **replays your function from the beginning**, returning cached results for completed steps and suspending at the first incomplete step. Your code is the state machine. + +### 3. The Concurrency Problem + +Multiple worker instances are running (for scale). Two workers pick up the same orchestration after a restart. + +**DIY approach:** Distributed locks via Redis or blob leases → acquire before processing → release after. + +**What goes wrong:** +- Lock expiration vs. long-running work: if the LLM call takes 30 seconds and your lock expires at 15, another worker enters +- Deadlocks when one worker holds lock A and waits for lock B while another holds lock B and waits for lock A +- Partial completion: worker acquires lock, completes 3 of 5 steps, crashes — now you have half-done state and need rollback + +A durable orchestrator manages concurrency internally via its event store — each orchestration instance has an atomic event stream with built-in sequencing. + +### 4. The Exactly-Once Side Effects Problem + +Step 5 is "lock the customer's bank account." The worker executed it, but crashed before recording that it succeeded. + +**DIY approach:** Idempotency keys for every side effect → check before executing → mark after. + +**What goes wrong:** +- You need an idempotency store separate from your state store (they must be updated atomically or you get inconsistencies) +- Every activity function must be wrapped with idempotency-key generation, lookup, and recording +- The pattern is different for every external system (REST API idempotency key vs. database upsert vs. message dedup) + +A durable orchestrator treats activities as **events in an append-only log**. If the worker crashes after the activity completes but before it records the result, the replay will see the completion event and skip re-execution automatically. + +### 5. The Stateful Agent Conversation Problem + +Your agent needs to re-investigate with full context: the original alert, its first analysis, the analyst's feedback. This conversation must persist across process restarts and even across multiple reject-reinvestigate cycles. + +**DIY approach:** Serialize the chat history to blob/database → load on each call → append → save. + +**What goes wrong:** +- Concurrent writes: two activities might try to update the same conversation simultaneously +- You need optimistic concurrency (ETags) or pessimistic locking +- Schema evolution: as your agent's message format changes, old serialized conversations must still deserialize +- Garbage collection: conversations that are complete should eventually be pruned + +A durable orchestrator with **entity state** (like DTS entities) handles all of this: atomic reads/writes, built-in concurrency control, and structured state that the framework manages. + +--- + +### The Honest Assessment + +| Concern | DIY Blob/DB Checkpointing | Durable Orchestrator (DTS) | +|---------|---------------------------|---------------------------| +| Simple linear pipeline, no human wait | ✅ Works fine, less infra | ⚠️ Overkill | +| Human-in-the-loop with timeout | 🔴 Build entire event system | ✅ `when_any([event, timer])` | +| Crash recovery with branches/loops | 🔴 Exponential state machine | ✅ Automatic replay | +| Multi-worker concurrency | 🔴 Distributed locks | ✅ Built-in event sequencing | +| Exactly-once side effects | 🔴 Idempotency infrastructure | ✅ Activity completion log | +| Persistent agent conversations | 🟡 Possible but manual | ✅ Entity state with concurrency | + +**Bottom line:** If your agent workflow is a straight-line script with no human waits, DIY checkpointing works. The moment you add human-in-the-loop, branching, loops, or multi-step crash recovery, you're building a workflow engine — and building a *correct* one is a multi-year distributed systems project. + +--- + +## Enter: Azure Durable Task Scheduler (DTS) + +The [Azure Durable Task Scheduler](https://learn.microsoft.com/en-us/azure/durable-task-scheduler/) is a managed service that provides exactly the primitives needed for durable agent orchestration: + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#4A90D9', 'primaryTextColor': '#fff' }}}%% +flowchart LR + subgraph DTS_SCOPE["🔷 What DTS Provides"] + direction TB + DS1["📋 Persistent task queue"] + DS2["📝 Event store — append-only log"] + DS3["⏱️ Timer service — survives restarts"] + DS4["🗃️ Entity state storage"] + DS5["📦 Work item distribution"] + DS6["🔁 At-least-once delivery"] + end + + subgraph WORKER_SCOPE["🔧 What Your Code Provides"] + direction TB + WS1["🐍 Compute runtime — Python"] + WS2["🤖 LLM calls — Azure OpenAI"] + WS3["🔌 Tool calls — MCP, APIs"] + WS4["📊 Business logic — orchestration"] + WS5["🧩 Agent framework integration"] + end + + DTS_SCOPE ~~~ WORKER_SCOPE + + style DTS_SCOPE fill:#cce5ff,stroke:#004085,stroke-width:2px + style WORKER_SCOPE fill:#ffeeba,stroke:#ffc107,stroke-width:2px ``` -**Option B: FastAPI Backend + React UI** +**DTS is a persistent task queue + event store + timer service.** It doesn't run your code — it stores the *record of what happened* and *dispatches work items* to workers. Your worker is a **stateless compute runtime** that pulls tasks, executes your Python/LLM/MCP logic, and reports results back. If the worker dies, DTS still has the full event log; a new worker replays it and picks up where things left off. + +### Key Properties + +| Property | How It Works | +|----------|-------------| +| **Checkpointing** | Every `yield` in your orchestration writes a completion event to DTS's append-only log | +| **Crash recovery** | Worker replays the orchestration function; completed yields return cached results instantly | +| **Human-in-the-loop** | `wait_for_external_event()` creates a subscription in DTS storage — survives indefinitely | +| **Timers** | `create_timer(72h)` fires in DTS's timer service — not in your process memory | +| **Entity state** | Agent conversation history persisted as a structured entity, with atomic updates | +| **Scaling** | Multiple workers pull from the same task hub — DTS distributes work items | + +### Development Experience + +DTS ships a **local emulator** as a Docker container, so you develop and test locally without an Azure subscription: + ```bash -# Terminal 1: Backend -uv run backend.py +# Local development — zero cloud dependency +docker run -d --name dts-emulator -p 8080:8080 mcr.microsoft.com/dts/dts-emulator:latest -# Terminal 2: React UI -cd ui -npm install -npm run dev +# Production — same SDK, same code, just change the endpoint +DTS_ENDPOINT=https://your-dts.northcentralus.durabletask.io ``` -Open http://localhost:5173 to view the interactive workflow UI. +--- + +## Reference Architecture: Ambient Fraud Detection + +To prove these patterns, we built a complete fraud detection system that exercises every durable orchestration primitive: fan-out/fan-in agents, human-in-the-loop with timeout, stateful feedback loops, and crash recovery. + +### The 3-Layer Design + +```mermaid +%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#4A90D9', 'primaryTextColor': '#fff', 'lineColor': '#5C6B77' }}}%% +flowchart TB + subgraph LAYER1["🟢 Layer 1 — Detection · Ambient Monitoring"] + direction LR + TELEMETRY["📊 Telemetry
Generator
2–5s interval"] + RULES["⚡ Rule Engine
Python, no LLM
• Multi-country login
• Spending spike > 3×
• Burst API calls
• Repeated auth failures"] + SUBMIT["🚨 Auto-submit
POST /api/workflow/start"] + SSE_OUT["📺 SSE Stream
→ React Live Feed"] + + TELEMETRY --> RULES + RULES -->|"anomaly detected"| SUBMIT + TELEMETRY -->|"all events"| SSE_OUT + RULES -->|"flagged events"| SSE_OUT + end + + subgraph LAYER2["🔵 Layer 2 — Investigation · Durable Agent Orchestration"] + direction TB + ORCH["🔷 DTS Orchestration
checkpointed, crash-recoverable"] + + subgraph ENTITY["dafx-FraudAnalysisAgent Entity"] + direction TB + INNER["Inner Workflow
(fast, async, in-memory)"] + + subgraph FANOUT["Fan-out / Fan-in"] + direction LR + ROUTER["AlertRouter"] --> USAGE["📈 Usage
Analyst"] + ROUTER --> LOCATION["🌍 Location
Analyst"] + ROUTER --> BILLING["💳 Billing
Analyst"] + end + + USAGE -->|"MCP tools"| AGG["🧠 FraudRisk
Aggregator
(LLM)"] + LOCATION -->|"MCP tools"| AGG + BILLING -->|"MCP tools"| AGG + AGG --> ASSESSMENT["📋 FraudRisk
Assessment"] + + INNER --- FANOUT + end + + ORCH -->|"yield fraud_agent.run()"| ENTITY + end + + subgraph LAYER3["🟠 Layer 3 — Decision & Action · HITL + Execution"] + direction TB + RISK_CHECK{"Risk ≥ 0.6?"} + + subgraph HITL_LOOP["HITL Feedback Loop
durable, stateful, crash-safe"] + direction TB + NOTIFY["📧 Notify Analyst
DTS Activity"] + WAIT["⏳ Wait for Decision
when_any(event, 72h timer)
Survives process death"] + DECIDE{"Analyst
Decision?"} + EXECUTE["✅ Execute Action
DTS Activity"] + REINVEST["🔄 Re-investigate
same session = full history"] + TIMEOUT_ACT["⏰ Escalate Timeout
DTS Activity"] + end + + AUTO_CLEAR["🟢 Auto-clear
DTS Activity"] + FINAL["📨 Send Notification
DTS Activity"] + + RISK_CHECK -->|"YES"| NOTIFY + NOTIFY --> WAIT + WAIT --> DECIDE + DECIDE -->|"Approve"| EXECUTE + DECIDE -->|"Reject + feedback"| REINVEST + DECIDE -->|"Timeout"| TIMEOUT_ACT + REINVEST -->|"loop back"| NOTIFY + RISK_CHECK -->|"NO"| AUTO_CLEAR + + EXECUTE --> FINAL + TIMEOUT_ACT --> FINAL + AUTO_CLEAR --> FINAL + end + + SUBMIT -->|"triggers"| ORCH + ENTITY -->|"risk score"| RISK_CHECK + + style LAYER1 fill:#d4edda,stroke:#28a745,stroke-width:2px + style LAYER2 fill:#cce5ff,stroke:#004085,stroke-width:2px + style LAYER3 fill:#fff3cd,stroke:#856404,stroke-width:2px + style ENTITY fill:#e8f4fd,stroke:#4A90D9 + style HITL_LOOP fill:#ffeeba,stroke:#ffc107 + style FANOUT fill:#f0f4f8,stroke:#adb5bd +``` -## 🧪 Test Scenarios +### Why This Layering? -### 1. High-Risk Alert with Analyst Approval +| Layer | Uses LLM? | Durable? | Rationale | +|-------|-----------|----------|-----------| +| **Layer 1** — Detection | ❌ No | ❌ No | Events arrive every 2–5s. An LLM call takes 2–5s and costs money. Simple rules catch 95% of benign events at zero cost. | +| **Layer 2** — Investigation | ✅ Yes | ✅ Yes (entity) | Complex multi-signal reasoning is the LLM's strength. Entity state persists the full conversation for re-investigation. | +| **Layer 3** — Decision | ❌ No | ✅ Yes (orchestration) | Human decisions can take hours/days. DTS timers and external events survive crashes and restarts. | +### Service Topology + +The system runs as four independent processes communicating through DTS and HTTP: + +```mermaid +flowchart LR + BROWSER["🌐 Browser
(REST / SSE / WS)"] + BACKEND["⚙️ Backend
(FastAPI)"] + DTS["🔷 DTS
(gRPC SDK)"] + WORKER["🔧 Worker"] + MCP["🔌 MCP Server"] + + BROWSER -->|"HTTP"| BACKEND + BACKEND -->|"gRPC"| DTS + DTS -.->|"pull"| WORKER + WORKER -->|"HTTP"| MCP + + style BROWSER fill:#e8f4fd,stroke:#4A90D9 + style BACKEND fill:#d4edda,stroke:#28a745 + style DTS fill:#cce5ff,stroke:#004085 + style WORKER fill:#ffeeba,stroke:#ffc107 + style MCP fill:#d6d8db,stroke:#6c757d ``` -Alert: ALERT-001 (multi_country_login, high severity) - ↓ -Workflow: Fan-out to 3 specialists - ↓ -Risk Score: 0.75 (HIGH RISK) - ↓ -⏸️ Waiting for analyst decision... - ↓ -Analyst: "lock_account" - ↓ -✅ Account locked, notification sent -``` -### 2. Low-Risk Alert with Auto-Clear +The browser cannot call DTS's gRPC SDK directly. The FastAPI backend acts as a Backend-For-Frontend (BFF), translating REST/WebSocket/SSE into SDK calls. In production, swap `DTS_ENDPOINT` from `localhost:8080` to your Azure DTS endpoint — **zero code changes**. + +--- + +## Key Design Patterns + +### Pattern 1: Durability Boundaries — What Gets Checkpointed + +Not everything needs to be durable. The key architectural insight is choosing **where** to draw the durability boundary: + +```mermaid +%%{init: {'theme': 'base'}}%% +flowchart LR + subgraph DURABLE["✅ DTS-Managed · each yield = checkpoint"] + direction TB + D1["yield fraud_agent.run(...)"] + D2["yield wait_for_external_event()"] + D3["yield create_timer(72h)"] + D4["yield call_activity(...)"] + end + subgraph FAST["⚡ Fast Async · retry on failure"] + direction TB + F1["Inner workflow fan-out/fan-in"] + F2["MCP tool calls via HTTP"] + F3["LLM calls to Azure OpenAI"] + end + + DURABLE ~~~ FAST + + style DURABLE fill:#d4edda,stroke:#28a745,stroke-width:2px + style FAST fill:#fff3cd,stroke:#856404,stroke-width:2px ``` -Alert: ALERT-002 (data_spike, low severity) - ↓ -Workflow: Fan-out to 3 specialists - ↓ -Risk Score: 0.35 (LOW RISK) - ↓ -✅ Auto-cleared, notification sent + +**Why not checkpoint every LLM call?** Adding DTS checkpoints to each LLM call would add ~200ms overhead per call and massively complicate the agent topology. The inner workflow runs in ~10–20 seconds — fast enough that retry-on-failure is the right strategy. If the worker crashes mid-inner-workflow, the entity call simply retries from the beginning. + +### Pattern 2: Stateful Feedback Loop via Durable Entity + +The `FraudAnalysisAgent` is registered as a DTS entity (`dafx-FraudAnalysisAgent`). The entity persists conversation history via `DurableAgentState`, enabling meaningful re-investigation: + +```mermaid +sequenceDiagram + participant O as DTS Orchestration + participant E as Entity
(dafx-FraudAnalysisAgent) + participant S as DurableAgentState + participant LLM as Azure OpenAI + + Note over O,S: First Investigation + O->>E: yield fraud_agent.run(alert_json, session) + E->>S: Load state (empty) + E->>S: Append user message (alert) + E->>LLM: Chat with full history + LLM-->>E: Risk assessment + E->>S: Append assistant response + E->>S: Persist state ← saved to DTS storage + E-->>O: AgentResponse (risk=0.85) + + Note over O,S: Analyst Rejects — Re-investigation + O->>E: yield fraud_agent.run(feedback, same session!) + E->>S: Load state (has alert + first analysis) + E->>S: Append user message (feedback) + E->>LLM: Chat with FULL history
(alert + analysis + feedback) + LLM-->>E: Deeper assessment + E->>S: Append assistant response + E->>S: Persist state + E-->>O: AgentResponse (risk=0.72) ``` -### 3. Timeout Escalation +The agent doesn't start from scratch — it sees the original alert, its first analysis, AND the analyst's feedback. This is what makes re-investigation meaningful rather than redundant. +### Pattern 3: Ambient Detection Without LLM Overhead + +Layer 1 uses fast Python rule evaluation, not LLM inference: + +```python +# Rule: Multi-country login within 2 hours +if event.type == "login" and event.country != last_login_country: + if time_delta < timedelta(hours=2): + trigger_alert(event) # → Layer 2 DTS orchestration + +# Rule: Spending spike > 3× average +if event.type == "transaction" and event.amount > 3 * customer_average: + trigger_alert(event) ``` -Alert: ALERT-003 (unusual_charges, high severity) - ↓ -Workflow: Fan-out to 3 specialists - ↓ -Risk Score: 0.80 (CRITICAL) - ↓ -⏸️ Waiting for analyst decision... - ↓ -⏰ Timeout (72 hours) - ↓ -⚠️ Escalated to manager -``` -## �️ React UI Features +At 1 event every 2–5 seconds, an LLM call (2–5s each) can't keep up. Rules handle the 95% of benign events at zero cost. The LLM's value is in Layer 2, where it reasons about complex multi-signal patterns. + +--- + +## Durability Mechanics — Deep Dive -The interactive React UI provides real-time visualization of the fraud detection workflow: +This section explains the **concrete mechanics** that make the architecture truly durable — not just the claim, but how it actually works under the hood. -### Interactive Workflow Diagram +### The Durability Stack -- **Real-time Status Updates**: Nodes change color based on execution state - - Gray: Pending - - Blue: Running (with pulse animation) - - Green: Completed - - Red: Failed +```mermaid +%%{init: {'theme': 'base'}}%% +flowchart TB + subgraph YOUR_CODE["Your Code"] + A["worker.py
Orchestration generator + activities"] + end -- **Clickable Nodes**: Click any workflow step to see detailed execution info: - - **Tool Calls**: Actual MCP tool calls made (e.g., `get_billing_summary`, `get_data_usage`) - - **Arguments**: Parameters passed to each tool - - **Results**: Output returned from each tool call - - **Step Output**: Final output from the agent step + subgraph AF["agent-framework DTS Layer"] + B["DurableAIAgentWorker
DurableAIAgentOrchestrationContext
DurableAgentState"] + end -### Human-in-the-Loop Panel + subgraph SDK["DTS Python SDK"] + C["DurableTaskSchedulerWorker
gRPC protocol"] + end -When the workflow reaches the Review Gateway (risk ≥ 0.6): -- Shows analyst decision options: `lock_account`, `flag_review`, `dismiss` -- Displays risk assessment details -- Allows analyst to submit decision via UI + subgraph DTS["Azure Durable Task Scheduler"] + D["Event Store · Timer Service
Entity State Store · Task Queue"] + end -### Example: Viewing Step Details + YOUR_CODE --> AF + AF --> SDK + SDK --> DTS + style YOUR_CODE fill:#d4edda,stroke:#28a745,stroke-width:2px + style AF fill:#e2d5f1,stroke:#6f42c1,stroke-width:2px + style SDK fill:#cce5ff,stroke:#004085,stroke-width:2px + style DTS fill:#fff3cd,stroke:#856404,stroke-width:2px ``` -1. Click on "Usage Analyst" node -2. Popover shows: - - Tool Calls (Real): - • get_data_usage(subscription_id=5, start_date="2025-12-01", ...) - → Result: {"total_gb": 45.2, "daily_avg": 1.5, ...} - • get_billing_summary(customer_id=3) - → Result: {"current_balance": 150.00, ...} - - Output: "Usage analysis indicates 300% spike in data..." + +### 1. Event Sourcing — Checkpoints via `yield` + +Every `yield` in the orchestration generator is a **checkpoint written to DTS's event store**: + +```python +# Each yield writes an event to DTS's append-only log +response = yield fraud_agent.run(messages=alert, session=session) # ← checkpoint +yield context.call_activity("notify_analyst", input=assessment) # ← checkpoint +winner = yield when_any([wait_for_event(...), create_timer(72h)]) # ← checkpoint ``` -## �📊 DTS Dashboard +These aren't in-memory variables — they're **persisted facts** in DTS storage. If the process crashes after step 2, DTS knows steps 1 and 2 completed because their completion events exist in the log. -Open http://localhost:8082 to see: +### 2. Replay, Not Restore — How Crash Recovery Works -- All orchestration instances -- Pending external events (analyst decisions) -- Activity execution logs -- Orchestration timeline and status +When a worker restarts after a crash, it doesn't "load state" — it **replays the orchestration function from the beginning**, but with a critical twist: -![DTS Dashboard](../docs/media/dts-dashboard.png) +```mermaid +flowchart TB + subgraph REPLAY["Orchestration Replay After Crash"] + direction TB + Y1["yield fraud_agent.run(...)"] -->|"DTS has completion event
→ returns cached result instantly
(no actual agent call)"| Y2["yield call_activity('notify_analyst')"] + Y2 -->|"DTS has completion event
→ returns cached result instantly"| Y3["yield when_any([event, timer])"] + Y3 -->|"No completion event yet
→ SUSPENDS HERE
waiting for real event"| WAIT["⏳ Waiting..."] + end -## 🔧 Configuration + style REPLAY fill:#e8f4fd,stroke:#4A90D9,stroke-width:2px + style WAIT fill:#ffeeba,stroke:#ffc107 +``` -### Environment Variables +The generator function re-executes, but each `yield` that already completed **returns its cached result instantly** without re-executing the actual work. The orchestration replays forward until it reaches the first incomplete step, then suspends. This is why your orchestration code must be **deterministic** — it's replayed, not restored. -| Variable | Description | Default | -|----------|-------------|---------| -| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint | Required | -| `AZURE_OPENAI_CHAT_DEPLOYMENT` | Deployment name | `gpt-4o` | -| `MCP_SERVER_URI` | MCP server URL | `http://localhost:8000/mcp` | -| `DTS_ENDPOINT` | DTS scheduler URL | `http://localhost:8080` | -| `DTS_TASKHUB` | DTS task hub name | `fraud-detection` | -| `ANALYST_APPROVAL_TIMEOUT_HOURS` | Timeout for analyst review | `72` | +### 3. External Events Survive Process Death -### Risk Threshold +The `wait_for_external_event("AnalystDecision")` call is especially powerful — it creates a subscription that lives entirely in DTS storage: -Edit `worker.py` to change the risk threshold: +```mermaid +sequenceDiagram + participant W1 as Worker (original) + participant DTS as DTS Storage + participant W2 as Worker (restarted) + participant BE as Backend + participant UI as Analyst -```python -# Current: 0.6 (60%) -if risk_score >= 0.6: - # High risk path -else: - # Low risk path + W1->>DTS: yield when_any([event, timer]) + Note over W1: Worker crashes! 💥 + + Note over DTS: Event subscription persists
Timer ticks in DTS storage + + UI->>BE: POST /api/workflow/decision + BE->>DTS: raise_orchestration_event
("AnalystDecision", data) + Note over DTS: Event stored in event log + + W2->>DTS: Start + long-poll for work + DTS->>W2: Orchestration has pending event + W2->>W2: Replay generator → resume at yield + W2->>W2: Process analyst decision ``` -## 🏛️ Key Components +The event subscription, the timer countdown, and the eventual analyst response — **all live in DTS storage**, not in the worker's memory. The worker is just a stateless compute runtime that pulls work. -### 1. Inner Workflow (`fraud_analysis_workflow.py`) +### 4. The Worker Is Stateless — All State Lives in DTS -The workflow handles complex multi-agent topology: +This is the fundamental insight. The worker process holds **zero durable state**: -```python -# Fan-out: Alert → 3 Specialists -builder.add_edge(alert_router, usage_executor) -builder.add_edge(alert_router, location_executor) -builder.add_edge(alert_router, billing_executor) - -# Fan-in: 3 Specialists → Aggregator -builder.add_fan_in_edge( - [usage_executor, location_executor, billing_executor], - aggregator -) +| What | Where It Lives | Survives Crash? | +|------|---------------|-----------------| +| Orchestration progress (which step) | DTS event log | ✅ Yes | +| Agent conversation history | DTS entity state (`DurableAgentState`) | ✅ Yes | +| Pending timers (72h analyst timeout) | DTS timer service | ✅ Yes | +| External event subscriptions | DTS event store | ✅ Yes | +| Activity results | DTS event log | ✅ Yes | +| In-flight LLM call | Worker memory | ❌ No (retried) | +| In-flight MCP tool call | Worker memory | ❌ No (retried) | + +When the worker crashes, the only things lost are in-flight LLM/MCP calls — and those are inside the inner workflow, which retries as a unit when the entity operation is re-dispatched. + +### 5. Entity State Persistence (`DurableAgentState`) + +The agent-framework library stores the full agent conversation in a structured schema: + +```mermaid +%%{init: {'theme': 'base'}}%% +erDiagram + ENTITY["dafx-FraudAnalysisAgent"] { + string instance_key "session_id" + } + + STATE["DurableAgentState"] { + string schema_version "v1.1.0" + } + + ENTRY["ConversationEntry"] { + string type "request or response" + json messages "Message array" + json tool_calls "ToolCall array" + json errors "Error array" + json token_usage "TokenUsage" + } + + ENTITY ||--|| STATE : "persists" + STATE ||--o{ ENTRY : "conversationHistory" ``` -### 2. DTS Orchestration (`worker.py`) +Every time the entity processes a request: +1. **Load** state from DTS → `DurableAgentState` +2. **Append** user message (alert or analyst feedback) +3. **Rebuild** full chat history from all entries +4. **Call** LLM with complete conversation +5. **Append** assistant response +6. **Persist** state back to DTS via `self.persist_state()` -The orchestration handles durability and HITL: +This is why the agent can re-investigate with full context — the conversation history is a **durable, append-only log** stored in DTS, not in worker memory. -```python -def fraud_detection_orchestration(context, payload): - # Run inner workflow as activity - assessment = yield context.call_activity("run_fraud_analysis", alert) - - if assessment["risk_score"] >= 0.6: - # Wait for analyst with timeout - approval_task = context.wait_for_external_event("AnalystDecision") - timeout_task = context.create_timer(timedelta(hours=72)) - - winner = yield when_any([approval_task, timeout_task]) - - if winner == approval_task: - yield context.call_activity("execute_fraud_action", decision) - else: - yield context.call_activity("escalate_timeout", assessment) - else: - yield context.call_activity("auto_clear_alert", assessment) +--- + +## Running the Demo + +This section covers how to run the reference implementation locally to see the patterns in action. + +### Prerequisites + +- **Docker** — for DTS emulator +- **Python 3.12+** with **uv** +- **Node.js 18+** — for React UI +- **Azure OpenAI** — with a deployed chat model +- **MCP Server** — Contoso tools on port 8000 + +### Service Startup Order + +```mermaid +%%{init: {'theme': 'base'}}%% +flowchart LR + S1["1️⃣ DTS Emulator
Port 8080"] + S2["2️⃣ MCP Server
Port 8000"] + S3["3️⃣ Worker
Pulls from DTS"] + S4["4️⃣ Backend
Port 8001"] + S5["5️⃣ React UI
Port 3000"] + + S1 --> S2 --> S3 --> S4 --> S5 + + style S1 fill:#cce5ff,stroke:#004085 + style S2 fill:#d6d8db,stroke:#6c757d + style S3 fill:#ffeeba,stroke:#ffc107 + style S4 fill:#d4edda,stroke:#28a745 + style S5 fill:#e8f4fd,stroke:#4A90D9 ``` -### 3. Activities +#### 1. Start DTS Emulator + +```bash +docker run -d --name dts-emulator \ + -p 8080:8080 -p 8082:8082 \ + mcr.microsoft.com/dts/dts-emulator:latest +``` + +Dashboard: http://localhost:8082 + +> **Production:** Replace with Azure DTS — same SDK, just change `DTS_ENDPOINT`. See [provision_dts.ps1](provision_dts.ps1). + +#### 2. Start MCP Server + +```bash +cd mcp && uv run python mcp_service.py +``` -| Activity | Purpose | -|----------|---------| -| `run_fraud_analysis` | Runs inner workflow, returns assessment | -| `notify_analyst` | Sends notification for review | -| `execute_fraud_action` | Executes approved action | -| `auto_clear_alert` | Auto-clears low-risk alerts | -| `escalate_timeout` | Escalates on timeout | -| `send_notification` | Sends final notification | +#### 3. Start Worker -## 🔄 Comparison with Original Implementation +```bash +cd agentic_ai/workflow/fraud_detection_durable +uv sync && uv run python worker.py +``` -| Aspect | Original (`fraud_detection/`) | Durable (`fraud_detection_durable/`) | -|--------|-------------------------------|-------------------------------------| -| HITL Pattern | `ctx.request_info()` + `@response_handler` | `wait_for_external_event()` | -| Checkpointing | `FileCheckpointStorage` (manual) | DTS (automatic) | -| Timeout | Not built-in | Native `create_timer()` | -| Recovery | Load checkpoint manually | Automatic replay | -| Dashboard | Custom logging | DTS Dashboard | -| Topology | Full workflow | Workflow as activity | +#### 4. Start Backend -## 🐛 Troubleshooting +```bash +uv run python backend.py +``` -### "Cannot connect to DTS" +#### 5. Start React UI ```bash -# Check if DTS is running -docker ps | grep dts +cd ui && npm install && npm run dev +# Open http://localhost:3000 +``` + +--- + +## Demo Scenarios + +### Scenario 1: Ambient Detection → Auto-Clear + +Watch the event feed — a multi-country login anomaly triggers automatic investigation. The agent assesses low risk → auto-cleared without human involvement. + +**What it proves:** Layer 1 rule engine triggering Layer 2 durable orchestration, with Layer 3 auto-clear path. + +### Scenario 2: Ambient Detection → HITL Approval + +A spending spike triggers investigation. Agent assesses high risk → analyst reviews in the UI → approves "lock account" → action executed. + +**What it proves:** Full 3-layer flow including human-in-the-loop via durable external events. + +### Scenario 3: Reject → Stateful Re-investigation + +Analyst rejects with feedback "check if VPN usage." Agent re-investigates with **full conversation history** (original alert + first analysis + analyst feedback) and produces a refined assessment. + +**What it proves:** Entity state persistence enables meaningful multi-turn investigation across the HITL feedback loop. + +### Scenario 4: Kill & Recover — The Durability Proof 💥 + +This is the critical scenario — it proves the architecture delivers on its durability claims: -# Restart if needed -docker restart dts-emulator +```mermaid +sequenceDiagram + participant UI as Analyst + participant BE as Backend + participant DTS as DTS + participant W1 as Worker v1 + + UI->>BE: Start high-risk workflow + BE->>DTS: schedule_new_orchestration() + DTS->>W1: Work item: run orchestration + W1->>W1: yield fraud_agent.run() ✅ + W1->>W1: yield notify_analyst() ✅ + W1->>DTS: yield wait_for_external_event() + Note over W1: Status: Awaiting analyst review + + Note over W1: 💥 taskkill /F /IM python.exe + + participant W2 as Worker v2 + Note over W2: uv run python worker.py + + UI->>BE: POST /api/workflow/decision (approve) + BE->>DTS: raise_orchestration_event() + DTS->>W2: Orchestration has pending event + W2->>W2: Replay: yield agent.run() → cached ✅ + W2->>W2: Replay: yield notify() → cached ✅ + W2->>W2: Resume: when_any() → event arrived! + W2->>W2: yield execute_fraud_action() ✅ + Note over W2: Workflow completes normally! 🎉 ``` -### "Worker not processing" +**Steps:** +1. Start a high-risk workflow → reaches "Awaiting analyst review" +2. `taskkill /F /IM python.exe` — kill all Python processes +3. `uv run python worker.py` — restart the worker +4. Submit analyst decision via UI +5. **Workflow completes normally** — DTS replayed the orchestration from its event log + +--- -1. Check worker is running: `uv run worker.py` -2. Check logs for errors -3. Verify DTS endpoint in `.env` +## Project Structure -### "Analyst decision not received" +``` +fraud_detection_durable/ +├── worker.py # DTS Worker: orchestration + agent entity + activities +├── backend.py # FastAPI BFF: REST API, WebSocket, SSE, event producer +├── event_producer.py # Layer 1: telemetry generation + anomaly detection +├── fraud_analysis_workflow.py # Inner workflow: fan-out → aggregate (Layer 2) +├── provision_dts.ps1 # Azure DTS provisioning script +├── .env # Configuration (Azure OpenAI, DTS, App Insights) +├── pyproject.toml # Dependencies +├── README.md # This file +├── PRODUCTION_ARCHITECTURE.md # Production deployment on Azure Container Apps +└── ui/ # React/Vite UI + ├── src/ + │ ├── App.jsx # Main app: WebSocket + SSE connections + │ └── components/ + │ ├── ControlPanel.jsx # Alert selector + start button + │ ├── WorkflowVisualizer.jsx # React Flow DAG visualization + │ ├── AnalystDecisionPanel.jsx # HITL approve/reject/feedback + │ └── EventFeed.jsx # Live telemetry feed (Layer 1) + └── package.json +``` + +## Configuration + +| Variable | Description | Default | +|----------|-------------|---------| +| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint | Required | +| `AZURE_OPENAI_CHAT_DEPLOYMENT` | Model deployment name | `gpt-4o` | +| `MCP_SERVER_URI` | MCP server URL | `http://localhost:8000/mcp` | +| `DTS_ENDPOINT` | DTS endpoint (local or Azure) | `http://localhost:8080` | +| `DTS_TASKHUB` | DTS task hub name | `default` | +| `ANALYST_APPROVAL_TIMEOUT_HOURS` | HITL timeout | `72` | +| `MAX_REVIEW_ATTEMPTS` | Max reject → re-investigate cycles | `3` | +| `EVENT_PRODUCER_ENABLED` | Enable Layer 1 event producer | `true` | +| `EVENT_INTERVAL_SECONDS` | Seconds between telemetry events | `3` | +| `BACKEND_OBSERVABILITY` | Enable Application Insights | `false` | -1. Check instance ID matches -2. Verify event name is `AnalystDecision` -3. Check DTS dashboard for pending events +## Production Deployment -## 📚 Related Documentation +For Azure Container Apps deployment topology, Managed Identity security, KEDA scaling, and cost estimation, see: -- [Agent Framework Workflow](../human-in-the-loop.md) -- [Durable Task Samples](https://github.com/microsoft/agent-framework/tree/main/python/samples/getting_started/durabletask) -- [Original Fraud Detection](../fraud_detection/README.md) +👉 **[PRODUCTION_ARCHITECTURE.md](PRODUCTION_ARCHITECTURE.md)** -## 📜 License +--- -Copyright (c) Microsoft. All rights reserved. +*Copyright (c) Microsoft. All rights reserved.* diff --git a/agentic_ai/workflow/fraud_detection_durable/backend.py b/agentic_ai/workflow/fraud_detection_durable/backend.py index df653c200..729d77cd6 100644 --- a/agentic_ai/workflow/fraud_detection_durable/backend.py +++ b/agentic_ai/workflow/fraud_detection_durable/backend.py @@ -31,15 +31,17 @@ # Add parent directories to path for the shared observability module sys.path.insert(0, str(Path(__file__).parent.parent.parent)) # agentic_ai/ -try: - from observability import setup_observability - _observability_enabled = setup_observability( - service_name="contoso-fraud-workflow", - enable_live_metrics=True, - enable_sensitive_data=os.getenv("ENABLE_SENSITIVE_DATA", "false").lower() in ("1", "true", "yes"), - ) -except ImportError: - _observability_enabled = False +_observability_enabled = False +if os.getenv("BACKEND_OBSERVABILITY", "false").lower() in ("1", "true", "yes"): + try: + from observability import setup_observability + _observability_enabled = setup_observability( + service_name="contoso-fraud-workflow", + enable_live_metrics=True, + enable_sensitive_data=os.getenv("ENABLE_SENSITIVE_DATA", "false").lower() in ("1", "true", "yes"), + ) + except (ImportError, Exception): + _observability_enabled = False # ------------------------------------------------------------------ from azure.identity import DefaultAzureCredential @@ -48,9 +50,11 @@ from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException from fastapi.middleware.cors import CORSMiddleware from fastapi.staticfiles import StaticFiles -from fastapi.responses import FileResponse +from fastapi.responses import FileResponse, StreamingResponse from pydantic import BaseModel +from event_producer import EventProducer, CUSTOMER_PROFILES, TelemetryEvent + # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) @@ -68,6 +72,7 @@ CORS_ORIGINS = [ "http://localhost:3000", "http://localhost:5173", + "http://localhost:8001", "http://localhost:8002", ] # Add Azure Container Apps URL if set @@ -129,7 +134,9 @@ class AnalystDecisionRequest(BaseModel): """Analyst decision submitted from UI.""" instance_id: str alert_id: str - approved_action: str + approved: bool = True # True = approve action, False = reject with feedback + approved_action: str = "clear" + feedback: str = "" # Feedback for re-investigation (when approved=False) analyst_notes: str = "" analyst_id: str = "analyst_ui" @@ -249,6 +256,16 @@ async def broadcast(self, instance_id: str, message: dict): manager = ConnectionManager() +# ============================================================================ +# Event Producer (Layer 1 — Ambient Detection) +# ============================================================================ + +event_producer = EventProducer( + interval_seconds=float(os.getenv("EVENT_INTERVAL_SECONDS", "3.0")), + anomaly_probability=float(os.getenv("ANOMALY_PROBABILITY", "0.08")), +) +_event_producer_task: asyncio.Task | None = None + # ============================================================================ # Background Task: Poll DTS for Status Updates @@ -438,7 +455,9 @@ async def submit_decision(request: AnalystDecisionRequest): decision = { "alert_id": request.alert_id, + "approved": request.approved, "approved_action": request.approved_action, + "feedback": request.feedback, "analyst_notes": request.analyst_notes, "analyst_id": request.analyst_id, } @@ -462,6 +481,38 @@ async def submit_decision(request: AnalystDecisionRequest): return {"status": "submitted", "instance_id": request.instance_id} +# ============================================================================ +# SSE Endpoint — Live Event Feed (Layer 1 → UI) +# ============================================================================ + + +@app.get("/api/events/stream") +async def event_stream(): + """Server-Sent Events stream of live telemetry + anomaly detections. + + The React UI connects here with EventSource to display the Live Feed panel. + Events are JSON objects with event_type, customer_name, is_anomaly, etc. + """ + queue = event_producer.subscribe() + + async def generate(): + try: + while True: + event = await queue.get() + data = json.dumps(event.to_dict()) + yield f"data: {data}\n\n" + except asyncio.CancelledError: + pass + finally: + event_producer.unsubscribe(queue) + + return StreamingResponse(generate(), media_type="text/event-stream", headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", # Disable nginx buffering + }) + + # ============================================================================ # WebSocket Endpoint # ============================================================================ @@ -529,17 +580,68 @@ async def websocket_endpoint(websocket: WebSocket, instance_id: str): @app.on_event("startup") async def startup(): """Initialize on startup.""" + global _event_producer_task + logger.info("="*60) logger.info("Starting Durable Fraud Detection Backend") logger.info("="*60) - # Pre-initialize DTS client - try: - get_dts_client() - logger.info("✓ DTS client initialized") - except Exception as e: - logger.error(f"Failed to initialize DTS client: {e}") - logger.error("Make sure DTS emulator is running") + # DTS client is lazy-initialized on first API call (get_dts_client()). + # Pre-init inside uvicorn's startup causes a CancelledError on Windows + # because gRPC's C extension installs a SIGINT handler that conflicts + # with asyncio's ProactorEventLoop signal handling. + logger.info("✓ DTS client will initialize on first request (lazy)") + + # Start Layer 1 event producer (ambient detection) + if os.getenv("EVENT_PRODUCER_ENABLED", "true").lower() in ("1", "true", "yes"): + async def alert_callback(alert_id, customer_id, alert_type, description, severity): + """Auto-submit detected anomaly to the durable workflow.""" + try: + client = get_dts_client() + alert = { + "alert_id": alert_id, + "customer_id": customer_id, + "alert_type": alert_type, + "description": description, + "timestamp": datetime.now().isoformat(), + "severity": severity, + "approval_timeout_hours": 0.05, + "auto_detected": True, + } + instance_id = client.schedule_new_orchestration( + ORCHESTRATION_NAME, + input=alert, + instance_id=f"fraud-{alert_id}-{int(time.time())}", + ) + logger.info(f"🤖 Auto-submitted alert {alert_id} → orchestration {instance_id}") + + # Broadcast workflow_auto_started to SSE so the UI can connect + started_event = TelemetryEvent( + id=f"WF-{alert_id}", + timestamp=alert["timestamp"], + customer_id=customer_id, + customer_name=CUSTOMER_PROFILES.get(customer_id, {}).get("name", f"Customer {customer_id}"), + event_type="workflow_auto_started", + details={ + "instance_id": instance_id, + "alert_id": alert_id, + "alert_type": alert_type, + "description": description, + "severity": severity, + }, + is_anomaly=True, + anomaly_rule=alert_type, + alert_triggered=True, + ) + await event_producer._broadcast(started_event) + except Exception as e: + logger.error(f"Failed to auto-submit alert: {e}") + + event_producer.set_alert_callback(alert_callback) + _event_producer_task = asyncio.create_task(event_producer.run()) + logger.info("✓ Event producer started (Layer 1 ambient detection)") + else: + logger.info("⏸ Event producer disabled (set EVENT_PRODUCER_ENABLED=true to enable)") logger.info("Backend ready! 🚀") @@ -549,6 +651,11 @@ async def shutdown(): """Cleanup on shutdown.""" logger.info("Shutting down backend...") + # Stop event producer + event_producer.stop() + if _event_producer_task: + _event_producer_task.cancel() + # Cancel polling tasks for task in _polling_tasks.values(): task.cancel() @@ -562,7 +669,7 @@ async def shutdown(): if __name__ == "__main__": import uvicorn - port = int(os.environ.get("BACKEND_PORT", "8002")) + port = int(os.environ.get("BACKEND_PORT", "8001")) uvicorn.run( app, host="0.0.0.0", diff --git a/agentic_ai/workflow/fraud_detection_durable/event_producer.py b/agentic_ai/workflow/fraud_detection_durable/event_producer.py new file mode 100644 index 000000000..3c73c9be2 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/event_producer.py @@ -0,0 +1,395 @@ +""" +Layer 1: Ambient Event Producer & Anomaly Detection. + +Generates simulated customer telemetry events and applies fast rule-based +anomaly detection. When an anomaly is detected, it auto-submits an alert +to the backend's /api/workflow/start endpoint, triggering the Layer 2 +durable investigation workflow. + +This module runs as an asyncio background task inside the FastAPI backend. +It does NOT use LLMs — rules are pure Python for speed and cost efficiency. + +Events are broadcast via SSE (Server-Sent Events) to the React UI's +Live Feed panel. +""" + +import asyncio +import logging +import random +import time +from collections import deque +from dataclasses import dataclass, field +from datetime import datetime, timedelta +from typing import Any + +logger = logging.getLogger(__name__) + + +# ============================================================================ +# Customer Profiles (baseline behavior for anomaly detection) +# ============================================================================ + +COUNTRIES = ["USA", "Canada", "UK", "Germany", "France", "Japan", "Australia", "Brazil", "India", "Russia"] +NORMAL_COUNTRIES = { + 1: ["USA", "Canada"], + 2: ["USA", "UK"], + 3: ["Germany", "France"], + 4: ["Japan", "Australia"], + 5: ["USA", "India"], +} + +CUSTOMER_PROFILES = { + 1: {"name": "Alice Johnson", "avg_transaction": 85.0, "avg_daily_data_gb": 1.2, "subscription_id": 5}, + 2: {"name": "Bob Smith", "avg_transaction": 120.0, "avg_daily_data_gb": 2.5, "subscription_id": 8}, + 3: {"name": "Carlos Rivera", "avg_transaction": 200.0, "avg_daily_data_gb": 0.8, "subscription_id": 12}, + 4: {"name": "Diana Chen", "avg_transaction": 55.0, "avg_daily_data_gb": 3.0, "subscription_id": 3}, + 5: {"name": "Ethan Patel", "avg_transaction": 150.0, "avg_daily_data_gb": 1.8, "subscription_id": 7}, +} + +EVENT_TYPES = ["login", "transaction", "data_usage", "api_call", "auth_failure"] + + +# ============================================================================ +# Data structures +# ============================================================================ + +@dataclass +class TelemetryEvent: + """A single telemetry event from the simulated environment.""" + id: str + timestamp: str + customer_id: int + customer_name: str + event_type: str + details: dict + is_anomaly: bool = False + anomaly_rule: str = "" + alert_triggered: bool = False + + def to_dict(self) -> dict: + return { + "id": self.id, + "timestamp": self.timestamp, + "customer_id": self.customer_id, + "customer_name": self.customer_name, + "event_type": self.event_type, + "details": self.details, + "is_anomaly": self.is_anomaly, + "anomaly_rule": self.anomaly_rule, + "alert_triggered": self.alert_triggered, + } + + +# ============================================================================ +# Event Producer +# ============================================================================ + +class EventProducer: + """Generates telemetry events and detects anomalies using fast rules. + + Maintains per-customer sliding windows for rule evaluation. + Broadcasts events to registered SSE subscribers. + """ + + def __init__( + self, + interval_seconds: float = 3.0, + anomaly_probability: float = 0.08, + ): + self.interval = interval_seconds + self.anomaly_probability = anomaly_probability + self._running = False + self._event_counter = 0 + self._subscribers: list[asyncio.Queue] = [] + + # Per-customer state for rule evaluation + self._recent_logins: dict[int, deque] = { + cid: deque(maxlen=10) for cid in CUSTOMER_PROFILES + } + self._recent_transactions: dict[int, deque] = { + cid: deque(maxlen=20) for cid in CUSTOMER_PROFILES + } + self._recent_auth_failures: dict[int, deque] = { + cid: deque(maxlen=10) for cid in CUSTOMER_PROFILES + } + + # Track which alerts we've fired to avoid duplicates + self._active_alerts: set[str] = set() + + # Callback for auto-submitting alerts + self._alert_callback: Any = None + + def set_alert_callback(self, callback): + """Set the async callback for when an anomaly triggers an alert.""" + self._alert_callback = callback + + def subscribe(self) -> asyncio.Queue: + """Subscribe to the event stream. Returns a queue to read events from.""" + queue: asyncio.Queue = asyncio.Queue(maxsize=100) + self._subscribers.append(queue) + return queue + + def unsubscribe(self, queue: asyncio.Queue): + """Unsubscribe from the event stream.""" + if queue in self._subscribers: + self._subscribers.remove(queue) + + async def _broadcast(self, event: TelemetryEvent): + """Send event to all SSE subscribers.""" + dead_queues = [] + for queue in self._subscribers: + try: + queue.put_nowait(event) + except asyncio.QueueFull: + dead_queues.append(queue) + for q in dead_queues: + self._subscribers.remove(q) + + # ======================================================================== + # Event Generation + # ======================================================================== + + def _generate_normal_event(self, customer_id: int) -> TelemetryEvent: + """Generate a normal (non-anomalous) telemetry event.""" + profile = CUSTOMER_PROFILES[customer_id] + event_type = random.choice(EVENT_TYPES) + self._event_counter += 1 + now = datetime.now() + + if event_type == "login": + country = random.choice(NORMAL_COUNTRIES[customer_id]) + details = {"country": country, "ip": f"192.168.{random.randint(1,255)}.{random.randint(1,255)}", "success": True} + elif event_type == "transaction": + amount = round(random.gauss(profile["avg_transaction"], profile["avg_transaction"] * 0.3), 2) + amount = max(5.0, amount) # No negative/tiny amounts + details = {"amount": amount, "currency": "USD", "merchant": random.choice(["Amazon", "Walmart", "Target", "BestBuy", "Costco"])} + elif event_type == "data_usage": + gb = round(random.gauss(profile["avg_daily_data_gb"], profile["avg_daily_data_gb"] * 0.2), 2) + gb = max(0.1, gb) + details = {"gb_used": gb, "subscription_id": profile["subscription_id"]} + elif event_type == "api_call": + details = {"endpoint": random.choice(["/api/account", "/api/billing", "/api/usage", "/api/profile"]), "status_code": 200, "latency_ms": random.randint(50, 300)} + else: # auth_failure + details = {"method": "password", "reason": "typo", "ip": f"10.0.{random.randint(1,255)}.{random.randint(1,255)}"} + + return TelemetryEvent( + id=f"EVT-{self._event_counter:06d}", + timestamp=now.isoformat(), + customer_id=customer_id, + customer_name=profile["name"], + event_type=event_type, + details=details, + ) + + def _generate_anomalous_event(self, customer_id: int) -> TelemetryEvent: + """Generate an event that should trigger an anomaly rule.""" + profile = CUSTOMER_PROFILES[customer_id] + self._event_counter += 1 + now = datetime.now() + + anomaly_type = random.choice(["multi_country_login", "spending_spike", "data_spike", "rapid_auth_failures"]) + + if anomaly_type == "multi_country_login": + # Login from unusual country + unusual = [c for c in COUNTRIES if c not in NORMAL_COUNTRIES[customer_id]] + country = random.choice(unusual) + details = {"country": country, "ip": f"203.{random.randint(1,255)}.{random.randint(1,255)}.{random.randint(1,255)}", "success": True} + return TelemetryEvent( + id=f"EVT-{self._event_counter:06d}", + timestamp=now.isoformat(), + customer_id=customer_id, + customer_name=profile["name"], + event_type="login", + details=details, + ) + + elif anomaly_type == "spending_spike": + # Transaction 4-8× the average + multiplier = random.uniform(4.0, 8.0) + amount = round(profile["avg_transaction"] * multiplier, 2) + details = {"amount": amount, "currency": "USD", "merchant": random.choice(["LuxuryGoods.com", "HighEnd Electronics", "Crypto Exchange"])} + return TelemetryEvent( + id=f"EVT-{self._event_counter:06d}", + timestamp=now.isoformat(), + customer_id=customer_id, + customer_name=profile["name"], + event_type="transaction", + details=details, + ) + + elif anomaly_type == "data_spike": + # Data usage 5-10× the average + multiplier = random.uniform(5.0, 10.0) + gb = round(profile["avg_daily_data_gb"] * multiplier, 2) + details = {"gb_used": gb, "subscription_id": profile["subscription_id"]} + return TelemetryEvent( + id=f"EVT-{self._event_counter:06d}", + timestamp=now.isoformat(), + customer_id=customer_id, + customer_name=profile["name"], + event_type="data_usage", + details=details, + ) + + else: # rapid_auth_failures + details = {"method": "password", "reason": "invalid_credentials", "ip": f"185.{random.randint(1,255)}.{random.randint(1,255)}.{random.randint(1,255)}"} + return TelemetryEvent( + id=f"EVT-{self._event_counter:06d}", + timestamp=now.isoformat(), + customer_id=customer_id, + customer_name=profile["name"], + event_type="auth_failure", + details=details, + ) + + # ======================================================================== + # Anomaly Detection Rules (fast Python, no LLM) + # ======================================================================== + + def _evaluate_rules(self, event: TelemetryEvent) -> tuple[bool, str]: + """Evaluate anomaly detection rules against an event. + + Returns (is_anomaly, rule_name). + """ + cid = event.customer_id + profile = CUSTOMER_PROFILES[cid] + + # Rule 1: Multi-country login within 2 hours + if event.event_type == "login" and event.details.get("success"): + country = event.details.get("country", "") + recent = self._recent_logins[cid] + for prev in recent: + prev_country = prev.get("country", "") + prev_time = prev.get("time", 0) + if prev_country and prev_country != country: + if time.time() - prev_time < 7200: # 2 hours + return True, "multi_country_login" + # Record this login + recent.append({"country": country, "time": time.time()}) + + # Rule 2: Transaction amount > 3× customer average + if event.event_type == "transaction": + amount = event.details.get("amount", 0) + if amount > profile["avg_transaction"] * 3: + return True, "spending_spike" + self._recent_transactions[cid].append({"amount": amount, "time": time.time()}) + + # Rule 3: Data usage > 4× daily average + if event.event_type == "data_usage": + gb = event.details.get("gb_used", 0) + if gb > profile["avg_daily_data_gb"] * 4: + return True, "data_usage_spike" + + # Rule 4: 3+ auth failures in 5 minutes + if event.event_type == "auth_failure": + recent_failures = self._recent_auth_failures[cid] + recent_failures.append({"time": time.time()}) + # Count failures in last 5 minutes + cutoff = time.time() - 300 + recent_count = sum(1 for f in recent_failures if f["time"] > cutoff) + if recent_count >= 3: + return True, "rapid_auth_failures" + + return False, "" + + def _make_alert_description(self, event: TelemetryEvent) -> str: + """Build a human-readable alert description from an anomalous event.""" + if event.anomaly_rule == "multi_country_login": + country = event.details.get("country", "unknown") + recent = self._recent_logins[event.customer_id] + prev_countries = [r["country"] for r in recent if r["country"] != country] + prev = prev_countries[-1] if prev_countries else "unknown" + return f"Login from {country} detected for customer {event.customer_id} ({event.customer_name}). Previous login was from {prev} within 2 hours. Possible credential compromise or account sharing." + elif event.anomaly_rule == "spending_spike": + amount = event.details.get("amount", 0) + avg = CUSTOMER_PROFILES[event.customer_id]["avg_transaction"] + return f"Transaction of ${amount:.2f} detected for customer {event.customer_id} ({event.customer_name}). This is {amount/avg:.1f}× their average of ${avg:.2f}. Possible unauthorized purchase." + elif event.anomaly_rule == "data_usage_spike": + gb = event.details.get("gb_used", 0) + avg = CUSTOMER_PROFILES[event.customer_id]["avg_daily_data_gb"] + return f"Data usage of {gb:.1f} GB detected for customer {event.customer_id} ({event.customer_name}). This is {gb/avg:.1f}× their daily average of {avg:.1f} GB. Possible data exfiltration or compromised device." + elif event.anomaly_rule == "rapid_auth_failures": + return f"Multiple authentication failures detected for customer {event.customer_id} ({event.customer_name}). 3+ failed attempts in 5 minutes from suspicious IP. Possible brute-force attack." + return f"Anomaly detected for customer {event.customer_id}: {event.anomaly_rule}" + + # ======================================================================== + # Main Loop + # ======================================================================== + + async def run(self): + """Main event production loop. Runs until stopped.""" + self._running = True + logger.info(f"🔄 Event producer started (interval={self.interval}s, anomaly_prob={self.anomaly_probability})") + + # Generate a few normal events first before any anomalies + warmup_count = 5 + event_count = 0 + + while self._running: + try: + customer_id = random.choice(list(CUSTOMER_PROFILES.keys())) + event_count += 1 + + # After warmup, sometimes generate anomalous events + if event_count > warmup_count and random.random() < self.anomaly_probability: + event = self._generate_anomalous_event(customer_id) + else: + event = self._generate_normal_event(customer_id) + + # Run anomaly rules + is_anomaly, rule = self._evaluate_rules(event) + if is_anomaly: + event.is_anomaly = True + event.anomaly_rule = rule + + # De-duplicate: don't fire the same rule for the same customer within 60s + dedup_key = f"{customer_id}-{rule}" + if dedup_key not in self._active_alerts: + self._active_alerts.add(dedup_key) + event.alert_triggered = True + + logger.warning( + f"⚠️ ANOMALY DETECTED: {rule} for customer {customer_id} " + f"({CUSTOMER_PROFILES[customer_id]['name']}) — triggering investigation" + ) + + # Auto-submit alert to Layer 2 + if self._alert_callback: + alert_type = rule + description = self._make_alert_description(event) + severity = "high" if rule in ("multi_country_login", "rapid_auth_failures") else "medium" + + try: + await self._alert_callback( + alert_id=f"AUTO-{event.id}", + customer_id=customer_id, + alert_type=alert_type, + description=description, + severity=severity, + ) + except Exception as e: + logger.error(f"Failed to submit alert: {e}") + + # Clear dedup after 60 seconds + asyncio.get_event_loop().call_later( + 60.0, lambda k=dedup_key: self._active_alerts.discard(k) + ) + + # Broadcast to SSE subscribers + await self._broadcast(event) + + # Wait for next event + jitter = random.uniform(-0.5, 0.5) + await asyncio.sleep(max(0.5, self.interval + jitter)) + + except asyncio.CancelledError: + break + except Exception as e: + logger.error(f"Event producer error: {e}", exc_info=True) + await asyncio.sleep(1) + + logger.info("Event producer stopped") + + def stop(self): + """Stop the event production loop.""" + self._running = False diff --git a/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py b/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py index 7557707da..475c4a66f 100644 --- a/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py +++ b/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py @@ -18,7 +18,7 @@ from pydantic import BaseModel from agent_framework import ( - ChatAgent, + Agent, Executor, handler, WorkflowBuilder, @@ -197,14 +197,14 @@ def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChat allowed_tools = ["get_customer_detail", "get_subscription_detail", "get_data_usage", "search_knowledge_base"] filtered_functions = [func for func in mcp_tool.functions if func.name in allowed_tools] - self._agent = ChatAgent( - chat_client=chat_client, - name="UsagePatternAnalyst", + self._agent = Agent( + client=chat_client, instructions=( "You are a specialist in analyzing customer data usage patterns. " "Look for anomalies like sudden spikes, unusual hours, or patterns inconsistent with history. " "Use the available tools to gather data and provide a risk score (0.0-1.0) and findings." ), + name="UsagePatternAnalyst", tools=filtered_functions, ) @@ -270,14 +270,14 @@ def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChat allowed_tools = ["get_customer_detail", "get_security_logs", "search_knowledge_base"] filtered_functions = [func for func in mcp_tool.functions if func.name in allowed_tools] - self._agent = ChatAgent( - chat_client=chat_client, - name="LocationAnalysisAgent", + self._agent = Agent( + client=chat_client, instructions=( "You are a specialist in analyzing geolocation and security patterns. " "Look for impossible travel, VPN usage, or login anomalies. " "Use the available tools to gather data and provide a risk score (0.0-1.0) and findings." ), + name="LocationAnalysisAgent", tools=filtered_functions, ) @@ -343,14 +343,14 @@ def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChat "get_customer_orders", "search_knowledge_base"] filtered_functions = [func for func in mcp_tool.functions if func.name in allowed_tools] - self._agent = ChatAgent( - chat_client=chat_client, - name="BillingChargeAnalyst", + self._agent = Agent( + client=chat_client, instructions=( "You are a specialist in analyzing billing and charge patterns. " "Look for unusual purchases, subscription changes, or payment anomalies. " "Use the available tools to gather data and provide a risk score (0.0-1.0) and findings." ), + name="BillingChargeAnalyst", tools=filtered_functions, ) @@ -446,14 +446,14 @@ async def handle_results( logger.info(f"[Aggregator] Aggregating results for {alert_id}") # Use LLM to synthesize findings - agent = ChatAgent( - chat_client=self._chat_client, - name="FraudRiskAggregator", + agent = Agent( + client=self._chat_client, instructions=( "You are a senior fraud analyst synthesizing findings from specialist agents. " "Weigh the evidence, calculate an overall risk score, and recommend an action. " "Be thorough but decisive. Return a JSON response with the assessment." ), + name="FraudRiskAggregator", ) prompt = f""" @@ -583,10 +583,7 @@ def create_fraud_analysis_workflow( aggregator = FraudRiskAggregatorExecutor(chat_client) # Build workflow topology - builder = WorkflowBuilder() - - # Set entry point - builder.set_start_executor(alert_router) + builder = WorkflowBuilder(start_executor=alert_router) # Fan-out: Alert Router → 3 Specialists builder.add_edge(alert_router, usage_executor) @@ -648,9 +645,9 @@ async def main(): print(f"{'='*60}\n") # Run workflow - async for event in workflow.run_stream(alert): + async for event in workflow.run(alert, stream=True): print(f"Event: {type(event).__name__}") - if hasattr(event, 'data') and isinstance(event.data, FraudRiskAssessment): + if event.type == "output" and isinstance(event.data, FraudRiskAssessment): assessment = event.data print(f"\n{'='*60}") print("FRAUD RISK ASSESSMENT") diff --git a/agentic_ai/workflow/fraud_detection_durable/provision_dts.ps1 b/agentic_ai/workflow/fraud_detection_durable/provision_dts.ps1 new file mode 100644 index 000000000..141242282 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/provision_dts.ps1 @@ -0,0 +1,193 @@ +<# +.SYNOPSIS + Provisions an Azure Durable Task Scheduler (DTS) resource and task hub + for the Fraud Detection Workshop demo. + +.DESCRIPTION + This script: + 1. Installs the `durabletask` Azure CLI extension (if missing) + 2. Creates a resource group (if it doesn't already exist) + 3. Creates a DTS Scheduler (Consumption SKU — free-tier preview) + 4. Creates a Task Hub inside the scheduler + 5. Assigns "Durable Task Data Contributor" RBAC to the current user + 6. Outputs the DTS_ENDPOINT and DTS_TASKHUB values for .env + +.PARAMETER ResourceGroup + Name of the Azure resource group. Default: "rg-fraud-workshop" + +.PARAMETER Location + Azure region. Default: "northcentralus" + Supported regions: northcentralus, westus2, westus3, eastus, etc. + +.PARAMETER SchedulerName + Name of the DTS Scheduler. Default: "dts-fraud-workshop" + +.PARAMETER TaskHubName + Name of the Task Hub. Default: "fraud-detection" + +.PARAMETER SkuName + SKU for the scheduler. Default: "consumption" (free preview). + Use "dedicated" for production (takes ~15min to provision). + +.EXAMPLE + .\provision_dts.ps1 + .\provision_dts.ps1 -Location eastus -SchedulerName my-scheduler + .\provision_dts.ps1 -SkuName dedicated +#> + +param( + [string]$ResourceGroup = "rg-fraud-workshop", + [string]$Location = "northcentralus", + [string]$SchedulerName = "dts-fraud-workshop", + [string]$TaskHubName = "fraud-detection", + [string]$SkuName = "consumption" +) + +$ErrorActionPreference = "Stop" + +Write-Host "" +Write-Host "========================================" -ForegroundColor Cyan +Write-Host " Azure Durable Task Scheduler Provisioning" -ForegroundColor Cyan +Write-Host "========================================" -ForegroundColor Cyan +Write-Host "" + +# ── 0. Verify Azure CLI login ─────────────────────────────────────────────── +Write-Host "[0/5] Checking Azure CLI login..." -ForegroundColor Yellow +try { + $account = az account show --output json 2>&1 | ConvertFrom-Json + $subscriptionId = $account.id + $userName = $account.user.name + Write-Host " Logged in as: $userName" -ForegroundColor Green + Write-Host " Subscription: $($account.name) ($subscriptionId)" -ForegroundColor Green +} +catch { + Write-Host " ERROR: Not logged in. Run 'az login' first." -ForegroundColor Red + exit 1 +} + +# ── 1. Install durabletask CLI extension ───────────────────────────────────── +Write-Host "" +Write-Host "[1/5] Installing durabletask CLI extension..." -ForegroundColor Yellow +$ErrorActionPreference = "Continue" +$extCheck = az extension show --name durabletask --query "name" -o tsv 2>$null +$ErrorActionPreference = "Stop" +if ($extCheck -eq "durabletask") { + Write-Host " Already installed." -ForegroundColor Green +} +else { + az extension add --name durabletask --yes 2>$null + Write-Host " Installed." -ForegroundColor Green +} +$ErrorActionPreference = "Stop" + +# ── 2. Create resource group ──────────────────────────────────────────────── +Write-Host "" +Write-Host "[2/5] Creating resource group '$ResourceGroup' in '$Location'..." -ForegroundColor Yellow +$rgExists = az group exists --name $ResourceGroup 2>$null +if ($rgExists -eq "true") { + Write-Host " Resource group already exists." -ForegroundColor Green +} +else { + az group create --name $ResourceGroup --location $Location --output none + Write-Host " Created." -ForegroundColor Green +} + +# ── 3. Create DTS Scheduler ──────────────────────────────────────────────── +Write-Host "" +Write-Host "[3/5] Creating DTS Scheduler '$SchedulerName' ($SkuName SKU)..." -ForegroundColor Yellow +Write-Host " This may take 5-15 minutes..." -ForegroundColor DarkYellow + +$schedulerArgs = @( + "durabletask", "scheduler", "create", + "--name", $SchedulerName, + "--resource-group", $ResourceGroup, + "--location", $Location, + "--ip-allowlist", "[0.0.0.0/0]", + "--sku-name", $SkuName +) +if ($SkuName -eq "dedicated") { + $schedulerArgs += @("--sku-capacity", "1") +} + +$ErrorActionPreference = "Continue" +$schedulerJson = az @schedulerArgs --output json 2>&1 | Where-Object { $_ -notmatch "^WARNING:" -and $_ -notmatch "^$" } +$ErrorActionPreference = "Stop" + +$scheduler = $schedulerJson | Out-String | ConvertFrom-Json + +$endpoint = $scheduler.properties.endpoint +if (-not $endpoint) { + # If creation returned quickly, the scheduler may already exist — try show + $ErrorActionPreference = "Continue" + $schedulerJson = az durabletask scheduler show --name $SchedulerName --resource-group $ResourceGroup --output json 2>&1 | Where-Object { $_ -notmatch "^WARNING:" } + $ErrorActionPreference = "Stop" + $scheduler = $schedulerJson | Out-String | ConvertFrom-Json + $endpoint = $scheduler.properties.endpoint +} + +Write-Host " Scheduler endpoint: $endpoint" -ForegroundColor Green +Write-Host " Provisioning state: $($scheduler.properties.provisioningState)" -ForegroundColor Green + +# ── 4. Create Task Hub ────────────────────────────────────────────────────── +Write-Host "" +Write-Host "[4/5] Creating Task Hub '$TaskHubName'..." -ForegroundColor Yellow +$ErrorActionPreference = "Continue" +az durabletask taskhub create --resource-group $ResourceGroup --scheduler-name $SchedulerName --name $TaskHubName --output none 2>$null +$ErrorActionPreference = "Stop" +Write-Host " Created." -ForegroundColor Green + +# ── 5. Assign RBAC to current user ────────────────────────────────────────── +Write-Host "" +Write-Host "[5/5] Assigning 'Durable Task Data Contributor' role to $userName..." -ForegroundColor Yellow + +$scope = "/subscriptions/$subscriptionId/resourceGroups/$ResourceGroup/providers/Microsoft.DurableTask/schedulers/$SchedulerName" + +# Get current user's object ID +$ErrorActionPreference = "Continue" +$assignee = az ad signed-in-user show --query "id" -o tsv 2>$null +$ErrorActionPreference = "Stop" + +if ($assignee) { + Write-Host " User object ID: $assignee" -ForegroundColor Gray + $ErrorActionPreference = "Continue" + $existing = az role assignment list --assignee $assignee --scope $scope --role "Durable Task Data Contributor" --query "length(@)" -o tsv 2>$null + $ErrorActionPreference = "Stop" + if ($existing -gt 0) { + Write-Host " Role already assigned." -ForegroundColor Green + } + else { + # Use --assignee-object-id + --assignee-principal-type for guest/EXT# users + $ErrorActionPreference = "Continue" + az role assignment create ` + --assignee-object-id $assignee ` + --assignee-principal-type "User" ` + --role "Durable Task Data Contributor" ` + --scope $scope ` + --output none 2>&1 | Where-Object { $_ -notmatch "^WARNING:" } + $ErrorActionPreference = "Stop" + Write-Host " Role assigned. (RBAC propagation may take 5-10 minutes)" -ForegroundColor Green + } +} +else { + Write-Host " WARNING: Could not determine user object ID. Assign the role manually:" -ForegroundColor DarkYellow + Write-Host " az role assignment create --assignee-object-id --assignee-principal-type User --role 'Durable Task Data Contributor' --scope '$scope'" -ForegroundColor DarkYellow +} + +# ── Summary ────────────────────────────────────────────────────────────────── +$dashboardUrl = "https://dashboard.durabletask.io/?endpoint=$endpoint&taskhub=$TaskHubName" + +Write-Host "" +Write-Host "========================================" -ForegroundColor Cyan +Write-Host " PROVISIONING COMPLETE" -ForegroundColor Cyan +Write-Host "========================================" -ForegroundColor Cyan +Write-Host "" +Write-Host "Add these to your .env file:" -ForegroundColor Green +Write-Host "" +Write-Host " DTS_ENDPOINT=$endpoint" -ForegroundColor White +Write-Host " DTS_TASKHUB=$TaskHubName" -ForegroundColor White +Write-Host "" +Write-Host "Dashboard: $dashboardUrl" -ForegroundColor Cyan +Write-Host "" +Write-Host "To tear down later:" -ForegroundColor DarkYellow +Write-Host " az group delete --name $ResourceGroup --yes --no-wait" -ForegroundColor DarkYellow +Write-Host "" diff --git a/agentic_ai/workflow/fraud_detection_durable/pyproject.toml b/agentic_ai/workflow/fraud_detection_durable/pyproject.toml index 17b502ae0..a68f35b94 100644 --- a/agentic_ai/workflow/fraud_detection_durable/pyproject.toml +++ b/agentic_ai/workflow/fraud_detection_durable/pyproject.toml @@ -6,7 +6,10 @@ readme = "README.md" requires-python = ">=3.12" dependencies = [ # Agent Framework (includes azure sub-package) - "agent-framework==1.0.0b260130", + "agent-framework==1.0.0b260212", + + # Durable Task Agent Framework integration + "agent-framework-durabletask>=1.0.0b260212", # Observability "azure-monitor-opentelemetry>=1.8.5", diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx index 1c8421430..f8644dc2d 100644 --- a/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/App.jsx @@ -15,6 +15,7 @@ import { import SecurityIcon from '@mui/icons-material/Security'; import CloudSyncIcon from '@mui/icons-material/CloudSync'; import WorkflowVisualizer from './components/WorkflowVisualizer'; +import EventFeed from './components/EventFeed'; import { API_CONFIG } from './constants/config'; import ControlPanel from './components/ControlPanel'; import AnalystDecisionPanel from './components/AnalystDecisionPanel'; @@ -134,6 +135,19 @@ function App() { location_analysis_executor: 'running', billing_charge_executor: 'running', }); + setPendingDecision(null); + } else if (customStatus.includes('Re-investigating')) { + // Re-investigation after analyst rejection - reset analysis nodes + setExecutorStates({ + alert_router: 'completed', + usage_pattern_executor: 'running', + location_analysis_executor: 'running', + billing_charge_executor: 'running', + fraud_risk_aggregator: 'idle', + review_gateway: 'idle', + }); + setPendingDecision(null); + setWorkflowRunning(true); } else if (customStatus.includes('Awaiting analyst')) { // Waiting for analyst - aggregation complete, waiting for human setExecutorStates({ @@ -179,8 +193,8 @@ function App() { })); } - // Check if decision is required - if (data.decision_required) { + // Check if decision is required (including re-investigation returns) + if (data.decision_required || customStatus.includes('Awaiting analyst')) { setPendingDecision({ instance_id: instanceId, alert_id: selectedAlert?.alert_id, @@ -238,11 +252,8 @@ function App() { if (data.type === 'decision_submitted') { setPendingDecision(null); setWorkflowRunning(true); - setExecutorStates((prev) => ({ - ...prev, - review_gateway: 'completed', - fraud_action_executor: 'running', - })); + // Don't assume approve — the status_update handler will set the right state + // based on whether the orchestration goes to "Executing" or "Re-investigating" } }, [instanceId, selectedAlert, addEvent]); @@ -299,8 +310,10 @@ function App() { body: JSON.stringify({ instance_id: instanceId, alert_id: selectedAlert?.alert_id, - approved_action: decision.approved_action, - analyst_notes: decision.analyst_notes, + approved: decision.approved !== false, + approved_action: decision.approved_action || 'clear', + feedback: decision.feedback || '', + analyst_notes: decision.analyst_notes || '', analyst_id: 'analyst_ui', }), }); @@ -345,8 +358,8 @@ function App() { {/* Main Content */} - {/* Left Column - Controls and Decision Panel */} - + {/* Left Column - Controls, Decision Panel, and Live Feed */} + )} + + {/* Layer 1 Live Feed */} + + { + // Connect the auto-triggered workflow to the visualizer + const syntheticAlert = { + alert_id: alertData.alert_id, + customer_id: alertData.customer_id, + alert_type: alertData.alert_type, + description: alertData.description, + severity: alertData.severity, + auto_detected: true, + }; + setSelectedAlert(syntheticAlert); + setWorkflowRunning(true); + setEvents([]); + setExecutorStates({ alert_router: 'running' }); + setPendingDecision(null); + setStepDetails({}); + // Setting instanceId triggers the WebSocket connection + setInstanceId(alertData.instance_id); + addEvent({ + type: 'workflow_auto_started', + instance_id: alertData.instance_id, + alert_id: alertData.alert_id, + message: `Layer 1 auto-detected ${alertData.alert_type} — investigation started`, + }); + }} + /> + {/* Center Column - Workflow Visualization */} - + Workflow Graph diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx index 1c87aea79..69cca1a2b 100644 --- a/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/AnalystDecisionPanel.jsx @@ -12,32 +12,50 @@ import { Chip, Alert, Divider, + ToggleButton, + ToggleButtonGroup, } from '@mui/material'; import GavelIcon from '@mui/icons-material/Gavel'; import SendIcon from '@mui/icons-material/Send'; +import CheckCircleIcon from '@mui/icons-material/CheckCircle'; +import CancelIcon from '@mui/icons-material/Cancel'; import { ACTION_OPTIONS } from '../constants/workflow'; /** - * Panel for analyst to make decisions on fraud alerts (Durable version) - * @param {Object} props - Component props - * @param {Object} props.decision - Decision request object with instance_id, alert_id, customer_id - * @param {Function} props.onSubmit - Callback to submit decision + * Panel for analyst to approve or reject (with feedback) fraud alerts. + * Supports the stateful HITL feedback loop. */ function AnalystDecisionPanel({ decision, onSubmit }) { + const [mode, setMode] = useState('approve'); // 'approve' or 'reject' const [selectedAction, setSelectedAction] = useState( - decision.recommended_action || 'block' + decision.recommended_action || 'lock_account' ); const [notes, setNotes] = useState(''); + const [feedback, setFeedback] = useState(''); const handleSubmit = () => { - onSubmit({ - instance_id: decision.instance_id, - alert_id: decision.alert_id, - customer_id: decision.customer_id, - approved_action: selectedAction, - analyst_notes: notes || 'Analyst decision from UI', - analyst_id: 'analyst_ui', - }); + if (mode === 'approve') { + onSubmit({ + instance_id: decision.instance_id, + alert_id: decision.alert_id, + customer_id: decision.customer_id, + approved: true, + approved_action: selectedAction, + analyst_notes: notes || 'Approved from UI', + analyst_id: 'analyst_ui', + }); + } else { + onSubmit({ + instance_id: decision.instance_id, + alert_id: decision.alert_id, + customer_id: decision.customer_id, + approved: false, + approved_action: '', + feedback: feedback || 'Please investigate further', + analyst_notes: notes, + analyst_id: 'analyst_ui', + }); + } }; return ( @@ -99,27 +117,71 @@ function AnalystDecisionPanel({ decision, onSubmit }) { - {/* Decision Form */} - - Your Decision - - + {/* Approve / Reject Toggle */} + val && setMode(val)} + fullWidth + size="small" + sx={{ mb: 0.5 }} + > + + + Approve + + + + Reject + + + + {/* Approve: Action selector */} + {mode === 'approve' && ( + + Action to Execute + + + )} + + {/* Reject: Feedback field */} + {mode === 'reject' && ( + <> + + + Provide feedback for the AI to re-investigate with more context. + + + setFeedback(e.target.value)} + placeholder="e.g. Check billing charges in the last 7 days more carefully..." + sx={{ '& .MuiInputBase-input': { fontSize: '0.875rem' } }} + required + /> + + )} } + startIcon={mode === 'approve' ? : } onClick={handleSubmit} + disabled={mode === 'reject' && !feedback.trim()} sx={{ mt: 0.5, py: 0.75 }} > - Submit Decision + {mode === 'approve' ? 'Approve & Execute' : 'Reject & Re-investigate'} ); diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/components/EventFeed.jsx b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/EventFeed.jsx new file mode 100644 index 000000000..ba633c545 --- /dev/null +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/components/EventFeed.jsx @@ -0,0 +1,338 @@ +import React, { useState, useEffect, useRef, useCallback } from 'react'; +import { + Box, + Paper, + Typography, + Chip, + IconButton, + Tooltip, + Divider, + Badge, +} from '@mui/material'; +import PauseIcon from '@mui/icons-material/Pause'; +import PlayArrowIcon from '@mui/icons-material/PlayArrow'; +import FiberManualRecordIcon from '@mui/icons-material/FiberManualRecord'; +import WarningAmberIcon from '@mui/icons-material/WarningAmber'; +import DeleteSweepIcon from '@mui/icons-material/DeleteSweep'; +import OpenInNewIcon from '@mui/icons-material/OpenInNew'; +import { API_CONFIG } from '../constants/config'; + +const MAX_EVENTS = 80; + +const EVENT_TYPE_COLORS = { + login: '#2196f3', + transaction: '#4caf50', + data_usage: '#9c27b0', + api_call: '#607d8b', + auth_failure: '#f44336', +}; + +const EVENT_TYPE_LABELS = { + login: 'LOGIN', + transaction: 'TXN', + data_usage: 'DATA', + api_call: 'API', + auth_failure: 'AUTH', +}; + +/** + * Live Event Feed panel showing Layer 1 telemetry events. + * + * Connects to the backend's /api/events/stream SSE endpoint. + * Green = normal, amber/red = anomaly detected. + * + * When Layer 1 auto-triggers a workflow, a "workflow_auto_started" event + * arrives with the DTS instance_id. The user can click to connect the + * WorkflowVisualizer to that running orchestration. + */ +export default function EventFeed({ onAnomalyDetected, onWorkflowAutoStarted }) { + const [events, setEvents] = useState([]); + const [paused, setPaused] = useState(false); + const [connected, setConnected] = useState(false); + const [anomalyCount, setAnomalyCount] = useState(0); + const [autoStartedAlerts, setAutoStartedAlerts] = useState([]); + const scrollRef = useRef(null); + const pausedRef = useRef(false); + const eventsRef = useRef([]); + const eventSourceRef = useRef(null); + + // Keep ref in sync with state + useEffect(() => { pausedRef.current = paused; }, [paused]); + + // Auto-scroll to bottom when new events arrive + useEffect(() => { + if (!paused && scrollRef.current) { + scrollRef.current.scrollTop = scrollRef.current.scrollHeight; + } + }, [events, paused]); + + // Connect to SSE stream + useEffect(() => { + const url = `${API_CONFIG.BASE_URL}${API_CONFIG.ENDPOINTS.EVENTS_STREAM}`; + console.log('Connecting to event stream:', url); + + const es = new EventSource(url); + eventSourceRef.current = es; + + es.onopen = () => { + console.log('Event stream connected'); + setConnected(true); + }; + + es.onmessage = (msg) => { + try { + const event = JSON.parse(msg.data); + + // Handle workflow auto-started events — these carry the DTS instance_id + if (event.event_type === 'workflow_auto_started') { + const alertInfo = { + instance_id: event.details?.instance_id, + alert_id: event.details?.alert_id, + alert_type: event.details?.alert_type, + description: event.details?.description, + severity: event.details?.severity, + customer_id: event.customer_id, + customer_name: event.customer_name, + timestamp: event.timestamp, + }; + setAutoStartedAlerts(prev => [alertInfo, ...prev].slice(0, 5)); + setAnomalyCount(prev => prev + 1); + // Don't add to the normal event list — it will render as a banner + return; + } + + if (event.is_anomaly) { + setAnomalyCount(prev => prev + 1); + if (onAnomalyDetected) { + onAnomalyDetected(event); + } + } + + if (!pausedRef.current) { + eventsRef.current = [...eventsRef.current.slice(-(MAX_EVENTS - 1)), event]; + setEvents(eventsRef.current); + } + } catch (err) { + console.error('Error parsing event:', err); + } + }; + + es.onerror = () => { + setConnected(false); + console.warn('Event stream disconnected, will retry...'); + }; + + return () => { + es.close(); + eventSourceRef.current = null; + }; + }, [onAnomalyDetected]); + + const handleClear = useCallback(() => { + eventsRef.current = []; + setEvents([]); + setAnomalyCount(0); + setAutoStartedAlerts([]); + }, []); + + const formatTime = (ts) => { + try { + const d = new Date(ts); + return d.toLocaleTimeString('en-US', { hour12: false, hour: '2-digit', minute: '2-digit', second: '2-digit' }); + } catch { + return ''; + } + }; + + const formatDetails = (event) => { + const d = event.details || {}; + switch (event.event_type) { + case 'login': + return `${d.country || '?'} · ${d.ip || ''}`; + case 'transaction': + return `$${(d.amount || 0).toFixed(2)} · ${d.merchant || ''}`; + case 'data_usage': + return `${(d.gb_used || 0).toFixed(1)} GB`; + case 'api_call': + return `${d.endpoint || ''} · ${d.status_code || ''}`; + case 'auth_failure': + return `${d.reason || ''} · ${d.ip || ''}`; + default: + return JSON.stringify(d).slice(0, 40); + } + }; + + return ( + + {/* Header */} + + + + Live Feed + + {anomalyCount > 0 && ( + } + label={anomalyCount} + size="small" + color="warning" + sx={{ height: 20, fontSize: 11 }} + /> + )} + + + + setPaused(p => !p)}> + {paused ? : } + + + + + + + + + + + {/* Auto-started workflow banners */} + {autoStartedAlerts.length > 0 && ( + + {autoStartedAlerts.map((a, i) => ( + onWorkflowAutoStarted && onWorkflowAutoStarted(a)} + sx={{ + display: 'flex', + alignItems: 'center', + gap: 0.5, + p: 0.5, + borderRadius: 1, + bgcolor: 'rgba(244,67,54,0.10)', + cursor: onWorkflowAutoStarted ? 'pointer' : 'default', + '&:hover': onWorkflowAutoStarted ? { bgcolor: 'rgba(244,67,54,0.20)' } : {}, + transition: 'background-color 0.15s', + }} + > + + + + ⚡ Auto-investigation started + + + C{a.customer_id} · {a.alert_type?.replace(/_/g, ' ')} · {a.severity} + + + {onWorkflowAutoStarted && ( + + + + )} + + ))} + + )} + + {/* Event list */} + + {events.length === 0 && ( + + {connected ? 'Waiting for events…' : 'Connecting to event stream…'} + + )} + {events.map((evt, i) => ( + + {/* Indicator dot */} + + {/* Timestamp */} + + {formatTime(evt.timestamp)} + + {/* Event type badge */} + + {/* Customer */} + + C{evt.customer_id} + + {/* Details */} + + {formatDetails(evt)} + + {/* Anomaly indicator */} + {evt.alert_triggered && ( + + )} + {evt.is_anomaly && !evt.alert_triggered && ( + + )} + + ))} + + + {/* Footer status */} + + + {events.length} events + + {paused && ( + + )} + + + ); +} diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js index 617845f48..d6295ab58 100644 --- a/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/config.js @@ -13,10 +13,10 @@ const isSameOrigin = typeof window !== 'undefined' && !window.location.port.includes('3000'); // Not React dev server // In production (same origin), use relative URLs. In dev, use localhost. -const defaultBaseUrl = isSameOrigin ? '' : 'http://localhost:8002'; +const defaultBaseUrl = isSameOrigin ? '' : 'http://localhost:8001'; const defaultWsUrl = isSameOrigin ? `${window.location.protocol === 'https:' ? 'wss:' : 'ws:'}//${window.location.host}/ws` - : 'ws://localhost:8002/ws'; + : 'ws://localhost:8001/ws'; /** * API configuration constants @@ -28,6 +28,7 @@ export const API_CONFIG = { ALERTS: '/api/alerts', WORKFLOW_START: '/api/workflow/start', WORKFLOW_DECISION: '/api/workflow/decision', + EVENTS_STREAM: '/api/events/stream', }, }; diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js index 07d791540..a04431ccc 100644 --- a/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js +++ b/agentic_ai/workflow/fraud_detection_durable/ui/src/constants/workflow.js @@ -42,6 +42,14 @@ export const ACTION_OPTIONS = [ { value: 'both', label: 'Lock Account & Refund', color: 'error' }, ]; +/** + * Decision modes + */ +export const DECISION_MODES = [ + { value: 'approve', label: 'Approve Action', color: 'success' }, + { value: 'reject', label: 'Reject & Request Re-investigation', color: 'error' }, +]; + /** * WebSocket ready states */ diff --git a/agentic_ai/workflow/fraud_detection_durable/ui/vite.config.js b/agentic_ai/workflow/fraud_detection_durable/ui/vite.config.js index e05580dca..af1187616 100644 --- a/agentic_ai/workflow/fraud_detection_durable/ui/vite.config.js +++ b/agentic_ai/workflow/fraud_detection_durable/ui/vite.config.js @@ -1,12 +1,24 @@ import { defineConfig } from 'vite' import react from '@vitejs/plugin-react' +import path from 'path' + +const __uiRoot = path.dirname(new URL(import.meta.url).pathname).replace(/^\/([A-Z]:)/, '$1') // https://vitejs.dev/config/ export default defineConfig({ plugins: [react()], + // Restrict dependency scanning to this UI directory only + optimizeDeps: { + entries: ['index.html', 'src/**/*.{js,jsx}'], + }, server: { port: 3000, open: true, + fs: { + // Only allow serving files from the UI directory + strict: true, + allow: [__uiRoot], + }, proxy: { '/api': { target: 'http://localhost:8001', 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= "opentelemetry-semantic-conventions-ai" }, { name = "packaging" }, { name = "pydantic" }, - { name = "pydantic-settings" }, + { name = "python-dotenv" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/4d/39/e508e778219bd6d20e023a6f48235861a639e3cf888776f9e873bbad3c6b/agent_framework_core-1.0.0b260130.tar.gz", hash = "sha256:030a5b2ced796eec6839c2dabad90b4bd1ea33d1026f3ed1813050a56ccfa4ec", size = 301823 } +sdist = { url = "https://files.pythonhosted.org/packages/1f/75/33444ede8faa90ebc0c81a7c362c401c9a29c224d8e4ef3653b018ad32f3/agent_framework_core-1.0.0b260212.tar.gz", hash = "sha256:4490e05f48dff6fe616331e46b4b4432e54a81075475c7087dce91b8c462c8d0", size = 259769 } wheels = [ - { url = "https://files.pythonhosted.org/packages/36/68/afe66c72951a279e0fe048fd5af1e775528cde40dbdab8ec03b42c545df4/agent_framework_core-1.0.0b260130-py3-none-any.whl", hash = "sha256:75b4dd0ca2ae52574d406cf5c9ed7adf63e187379f72fce891743254d83dfd56", size = 348724 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The main fraud_detection_orchestration (outer layer) -2. Activity functions for side effects -3. The inner workflow runs as an activity +Hybrid architecture combining: +- DurableAIAgentWorker: Registers FraudAnalysisAgent as a durable entity with + persistent conversation state (DurableAgentState) +- DurableAIAgentOrchestrationContext: Type-safe agent calls from within orchestrations +- Inner Workflow: Complex fan-out/fan-in topology with MCP tools for fast execution +- DTS Orchestration: Durability, HITL, timeouts, crash recovery, feedback loop -Architecture: -- DTS Orchestration handles: durability, HITL, timeouts, crash recovery -- Inner Workflow handles: complex fan-out/fan-in topology +The FraudAnalysisAgent entity preserves conversation history across invocations, +enabling stateful re-investigation when an analyst rejects and provides feedback. Prerequisites: - Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT -- Start Durable Task Scheduler: docker run -d --name dts -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest +- Start Durable Task Scheduler emulator - Start MCP Server: cd mcp && uv run mcp_service.py """ @@ -49,11 +50,22 @@ # ------------------------------------------------------------------ from azure.identity import DefaultAzureCredential, ManagedIdentityCredential from durabletask.azuremanaged.worker import DurableTaskSchedulerWorker -from durabletask.task import ActivityContext, OrchestrationContext, Task, when_any, when_all +from durabletask.task import ActivityContext, OrchestrationContext, Task, when_any from pydantic import BaseModel, ValidationError -from agent_framework import MCPStreamableHTTPTool, WorkflowOutputEvent +from agent_framework import ( + AgentResponse, + BaseAgent, + Content, + MCPStreamableHTTPTool, + Message, + WorkflowEvent, +) from agent_framework.azure import AzureOpenAIChatClient +from agent_framework_durabletask import ( + DurableAIAgentOrchestrationContext, + DurableAIAgentWorker, +) from fraud_analysis_workflow import ( SuspiciousActivityAlert, @@ -68,9 +80,151 @@ logger.info("✅ Application Insights observability enabled for fraud workflow worker") # Constants +FRAUD_AGENT_NAME = "FraudAnalysisAgent" ANALYST_APPROVAL_EVENT = "AnalystDecision" +# ============================================================================ +# FraudAnalysisAgent - Custom agent wrapping the inner workflow +# ============================================================================ + + +class FraudAnalysisAgent(BaseAgent): + """Custom agent that runs the fraud analysis workflow as a DTS entity. + + Registered via DurableAIAgentWorker as entity 'dafx-FraudAnalysisAgent'. + The entity persists conversation history (DurableAgentState), enabling + stateful re-investigation when analyst provides feedback. + + Each invocation creates fresh MCP connections (event-loop safe for DTS + entities which use asyncio.run() per call). + """ + + def __init__(self, *, name: str = FRAUD_AGENT_NAME): + super().__init__( + name=name, + description="Fraud analysis agent with fan-out workflow topology and MCP tools", + ) + self._mcp_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") + self._deployment_name = os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4o") + + async def run(self, messages: Any = None, *, stream: bool = False, **kwargs: Any) -> AgentResponse: + """Run fraud analysis. Entity calls this with conversation history. + + On first call: messages contain the alert JSON. + On subsequent calls (re-investigation): messages contain full history + including previous analysis and analyst feedback. + """ + if stream: + # DTS entity always tries stream=True first, then falls back. + # Raise TypeError with "stream" in message so entity falls back cleanly. + raise TypeError("stream=True not supported by FraudAnalysisAgent") + + msg_list: list[Message] = messages if isinstance(messages, list) else [] + alert_data, previous_context = self._parse_messages(msg_list) + + if alert_data is None: + raise ValueError("No alert data found in messages") + + logger.info(f"[FraudAnalysisAgent] Running analysis for alert {alert_data.get('alert_id')}") + if previous_context: + logger.info("[FraudAnalysisAgent] Re-investigation with analyst feedback") + + # Create fresh MCP connection for this event loop (DTS entity uses asyncio.run()) + mcp_tool = MCPStreamableHTTPTool(name="contoso_mcp", url=self._mcp_uri, timeout=30) + async with mcp_tool: + # Create chat client + azure_client_id = os.getenv("AZURE_CLIENT_ID") + credential = ( + ManagedIdentityCredential(client_id=azure_client_id) + if azure_client_id + else DefaultAzureCredential() + ) + chat_client = AzureOpenAIChatClient( + credential=credential, + deployment_name=self._deployment_name, + ) + + # If re-investigating, enrich the alert description with previous context + alert = SuspiciousActivityAlert(**alert_data) + if previous_context: + alert = alert.model_copy(update={ + "description": ( + f"{alert.description}\n\n" + f"--- RE-INVESTIGATION CONTEXT ---\n{previous_context}" + ), + }) + + # Build and run the fan-out workflow + workflow = create_fraud_analysis_workflow(mcp_tool, chat_client) + assessment: FraudRiskAssessment | None = None + + async for event in workflow.run(alert, stream=True): + if isinstance(event, WorkflowEvent) and event.type == "output": + if isinstance(event.data, FraudRiskAssessment): + assessment = event.data + break + + if assessment is None: + raise ValueError("Workflow did not produce FraudRiskAssessment") + + # Add customer_id from original alert + assessment_dict = assessment.model_dump() + assessment_dict["customer_id"] = alert_data.get("customer_id", 0) + + logger.info( + f"[FraudAnalysisAgent] Analysis complete: " + f"risk={assessment.overall_risk_score:.2f}, action={assessment.recommended_action}" + ) + + # Return as AgentResponse so entity can persist in DurableAgentState + response_text = json.dumps(assessment_dict, indent=2) + return AgentResponse( + messages=[Message(role="assistant", contents=[Content.from_text(response_text)])], + ) + + def _parse_messages(self, messages: list[Message]) -> tuple[dict | None, str | None]: + """Extract alert data and previous context from entity conversation history. + + Returns: + (alert_data, previous_context) - alert_data is the original alert dict, + previous_context is a string with previous findings + analyst feedback. + """ + alert_data: dict | None = None + context_parts: list[str] = [] + + for msg in messages: + # Extract text from message + text = "" + if hasattr(msg, "text") and msg.text: + text = msg.text + elif hasattr(msg, "contents"): + for c in msg.contents: + if hasattr(c, "text") and c.text: + text += c.text + + if not text: + continue + + role = getattr(msg, "role", "") + + if role == "user": + if alert_data is None: + # First user message = alert JSON + try: + alert_data = json.loads(text) + except json.JSONDecodeError: + alert_data = None + else: + # Subsequent user messages = analyst feedback + context_parts.append(f"ANALYST FEEDBACK: {text}") + elif role == "assistant" and alert_data is not None: + # Previous analysis response + context_parts.append(f"PREVIOUS ANALYSIS:\n{text[:2000]}") + + return alert_data, "\n\n".join(context_parts) if context_parts else None + + # ============================================================================ # Input/Output Models # ============================================================================ @@ -84,14 +238,16 @@ class FraudDetectionInput(BaseModel): description: str = "" timestamp: str = "" severity: str = "medium" - approval_timeout_hours: float = 72.0 # 72 hours for analyst review + approval_timeout_hours: float = 72.0 max_review_attempts: int = 3 class AnalystDecision(BaseModel): - """Human analyst decision.""" + """Human analyst decision - supports approve, reject with feedback, or timeout.""" alert_id: str - approved_action: str # "lock_account", "refund_charges", "clear", "both" + approved: bool # True = approve action, False = reject and request re-investigation + approved_action: str = "clear" # "lock_account", "refund_charges", "clear", "both" + feedback: str = "" # Analyst feedback for re-investigation (when approved=False) analyst_notes: str = "" analyst_id: str = "analyst" @@ -104,134 +260,38 @@ class ActionResult(BaseModel): details: str -# ============================================================================ -# Global Resources (initialized once) -# ============================================================================ - -_mcp_tool: MCPStreamableHTTPTool | None = None -_chat_client: AzureOpenAIChatClient | None = None - - -async def _ensure_resources(): - """Initialize global resources if not already done.""" - global _mcp_tool, _chat_client - - if _mcp_tool is None: - mcp_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") - _mcp_tool = MCPStreamableHTTPTool(name="contoso_mcp", url=mcp_uri, timeout=30) - await _mcp_tool.__aenter__() - logger.info(f"✓ MCP tool initialized at {mcp_uri}") - - if _chat_client is None: - # Use managed identity if AZURE_CLIENT_ID is set, otherwise DefaultAzureCredential - azure_client_id = os.getenv("AZURE_CLIENT_ID") - if azure_client_id: - from azure.identity import ManagedIdentityCredential - credential = ManagedIdentityCredential(client_id=azure_client_id) - logger.info(f"Using ManagedIdentityCredential with client_id: {azure_client_id}") - else: - credential = DefaultAzureCredential() - logger.info("Using DefaultAzureCredential") - - _chat_client = AzureOpenAIChatClient( - credential=credential, - deployment_name=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4o"), - ) - logger.info("✓ Azure OpenAI client initialized") - - return _mcp_tool, _chat_client - - # ============================================================================ # Activity Functions # ============================================================================ -def run_fraud_analysis(context: ActivityContext, alert_dict: dict) -> dict: - """ - Activity that runs the inner fraud analysis workflow. - - This is the bridge between Durable Task and the Workflow: - - Receives alert as dict - - Runs the fan-out → aggregate workflow - - Returns FraudRiskAssessment as dict - """ - logger.info(f"[Activity] run_fraud_analysis starting for alert {alert_dict.get('alert_id')}") - - async def _run_workflow(): - mcp_tool, chat_client = await _ensure_resources() - - # Create the workflow - workflow = create_fraud_analysis_workflow(mcp_tool, chat_client) - - # Create alert object - alert = SuspiciousActivityAlert(**alert_dict) - - # Run workflow and collect output - assessment: FraudRiskAssessment | None = None - - async for event in workflow.run_stream(alert): - if isinstance(event, WorkflowOutputEvent): - if isinstance(event.data, FraudRiskAssessment): - assessment = event.data - break - - if assessment is None: - raise ValueError("Workflow did not produce FraudRiskAssessment") - - # Add customer_id from original alert - assessment_dict = assessment.model_dump() - assessment_dict["customer_id"] = alert.customer_id - - return assessment_dict - - # Run the async workflow synchronously - result = asyncio.run(_run_workflow()) - logger.info(f"[Activity] run_fraud_analysis completed, risk_score={result.get('overall_risk_score')}") - return result - - -def notify_analyst(context: ActivityContext, assessment_dict: dict) -> str: - """ - Activity to notify analyst for review. - - In production, this would: - - Send email/Slack notification - - Create ticket in ITSM system - - Push to analyst dashboard - """ - alert_id = assessment_dict.get("alert_id") - risk_score = assessment_dict.get("overall_risk_score", 0) - recommended_action = assessment_dict.get("recommended_action", "unknown") - +def notify_analyst(context: ActivityContext, assessment_text: str) -> str: + """Activity to notify analyst for review.""" + try: + assessment = json.loads(assessment_text) + alert_id = assessment.get("alert_id", "unknown") + risk_score = assessment.get("overall_risk_score", 0) + recommended = assessment.get("recommended_action", "unknown") + except (json.JSONDecodeError, AttributeError): + alert_id = "unknown" + risk_score = 0 + recommended = "unknown" + logger.info(f"[Activity] NOTIFICATION: Analyst review required for alert {alert_id}") - logger.info(f"[Activity] Risk Score: {risk_score:.2f}, Recommended: {recommended_action}") - logger.info(f"[Activity] Reasoning: {assessment_dict.get('reasoning', 'N/A')}") - - # In production: send notification via email, Slack, etc. + logger.info(f"[Activity] Risk Score: {risk_score}, Recommended: {recommended}") + return f"Analyst notified for alert {alert_id}" def execute_fraud_action(context: ActivityContext, decision_dict: dict) -> dict: - """ - Activity to execute the approved fraud action. - - In production, this would: - - Lock account via API - - Process refunds - - Update fraud database - """ - alert_id = decision_dict.get("alert_id") + """Activity to execute the approved fraud action.""" + alert_id = decision_dict.get("alert_id", "unknown") action = decision_dict.get("approved_action", "unknown") analyst_id = decision_dict.get("analyst_id", "unknown") - + logger.info(f"[Activity] Executing fraud action: {action} for alert {alert_id}") logger.info(f"[Activity] Approved by analyst: {analyst_id}") - - # In production: execute actual action - success = True - details = f"Action '{action}' executed successfully" - + if action == "lock_account": logger.info(f"[Activity] 🔒 Account locked for alert {alert_id}") elif action == "refund_charges": @@ -240,42 +300,45 @@ def execute_fraud_action(context: ActivityContext, decision_dict: dict) -> dict: logger.info(f"[Activity] 🔒💰 Account locked and charges refunded for alert {alert_id}") elif action == "clear": logger.info(f"[Activity] ✅ Alert cleared for {alert_id}") - + return ActionResult( alert_id=alert_id, action_taken=action, - success=success, - details=details, + success=True, + details=f"Action '{action}' executed successfully", ).model_dump() -def auto_clear_alert(context: ActivityContext, assessment_dict: dict) -> dict: - """ - Activity to auto-clear low-risk alerts. - """ - alert_id = assessment_dict.get("alert_id") - risk_score = assessment_dict.get("overall_risk_score", 0) - - logger.info(f"[Activity] Auto-clearing low-risk alert {alert_id} (risk={risk_score:.2f})") - +def auto_clear_alert(context: ActivityContext, assessment_text: str) -> dict: + """Activity to auto-clear low-risk alerts.""" + try: + assessment = json.loads(assessment_text) + alert_id = assessment.get("alert_id", "unknown") + risk_score = assessment.get("overall_risk_score", 0) + except (json.JSONDecodeError, AttributeError): + alert_id = "unknown" + risk_score = 0 + + logger.info(f"[Activity] Auto-clearing low-risk alert {alert_id} (risk={risk_score})") + return ActionResult( alert_id=alert_id, action_taken="auto_clear", success=True, - details=f"Alert auto-cleared due to low risk score ({risk_score:.2f})", + details=f"Alert auto-cleared due to low risk score ({risk_score})", ).model_dump() -def escalate_timeout(context: ActivityContext, assessment_dict: dict) -> dict: - """ - Activity to escalate when analyst review times out. - """ - alert_id = assessment_dict.get("alert_id") - +def escalate_timeout(context: ActivityContext, assessment_text: str) -> dict: + """Activity to escalate when analyst review times out.""" + try: + assessment = json.loads(assessment_text) + alert_id = assessment.get("alert_id", "unknown") + except (json.JSONDecodeError, AttributeError): + alert_id = "unknown" + logger.warning(f"[Activity] ⚠️ ESCALATION: Analyst review timed out for alert {alert_id}") - logger.warning(f"[Activity] Escalating to fraud manager...") - - # In production: escalate to manager, auto-lock account, etc. + return ActionResult( alert_id=alert_id, action_taken="escalate_timeout", @@ -285,184 +348,272 @@ def escalate_timeout(context: ActivityContext, assessment_dict: dict) -> dict: def send_notification(context: ActivityContext, result_dict: dict) -> str: - """ - Activity to send final notification. - """ - alert_id = result_dict.get("alert_id") + """Activity to send final notification.""" + alert_id = result_dict.get("alert_id", "unknown") action_taken = result_dict.get("action_taken", "unknown") - + logger.info(f"[Activity] Sending final notification for alert {alert_id}") logger.info(f"[Activity] Action taken: {action_taken}") - - # In production: send customer notification, update audit log + return f"Notification sent for alert {alert_id}, action: {action_taken}" # ============================================================================ -# Main Orchestration +# Main Orchestration (uses DurableAIAgentOrchestrationContext) # ============================================================================ def fraud_detection_orchestration( context: OrchestrationContext, - payload_raw: Any + payload_raw: Any, ) -> Generator[Task[Any], Any, dict]: - """ - Main Durable Task orchestration for fraud detection. - - This orchestration: - 1. Runs the inner workflow (fan-out → aggregate) as an activity - 2. Routes based on risk score (simple if/else) - 3. For high risk: waits for analyst decision with timeout - 4. Executes approved action or auto-clears - 5. Sends final notification - - Args: - context: The orchestration context - payload_raw: The input payload (alert data) - - Returns: - dict: Final result with status and action taken + """Main DTS orchestration for fraud detection with stateful HITL feedback loop. + + This orchestration demonstrates: + 1. DurableAIAgentOrchestrationContext for type-safe agent calls + 2. DurableAgentState for persistent conversation history across iterations + 3. Stateful feedback loop: analyst reject -> re-investigation with full context + 4. External events (HITL), timeouts, activity functions + + Flow: + 1. Call FraudAnalysisAgent entity (inner workflow: fan-out -> aggregate) + 2. Route based on risk score + 3. HIGH RISK: notify analyst -> wait for decision + - Approve -> execute action -> done + - Reject + feedback -> re-investigate (same session = stateful) -> loop + - Timeout -> escalate + 4. LOW RISK: auto-clear + 5. Send notification """ logger.info("[Orchestration] Starting fraud detection orchestration") - + # Validate input if not isinstance(payload_raw, dict): raise ValueError("Alert data is required") - try: payload = FraudDetectionInput.model_validate(payload_raw) except ValidationError as exc: raise ValueError(f"Invalid alert input: {exc}") from exc - + alert_id = payload.alert_id logger.info(f"[Orchestration] Processing alert {alert_id}") - - context.set_custom_status(json.dumps({ - "message": f"Running fraud analysis for {alert_id}", - "step_details": {}, - "risk_score": None, - })) - + + # ======================================================================== + # Set up DurableAIAgentOrchestrationContext for agent entity calls + # ======================================================================== + + agent_ctx = DurableAIAgentOrchestrationContext(context) + fraud_agent = agent_ctx.get_agent(FRAUD_AGENT_NAME) + fraud_session = fraud_agent.create_session() + + logger.info(f"[Orchestration] Created agent session: {fraud_session.session_id}") + # ======================================================================== - # Step 1: Run the inner workflow (fan-out → aggregate) + # Step 1: Initial fraud analysis via DurableAIAgent entity # ======================================================================== - - logger.info("[Orchestration] Step 1: Running fraud analysis workflow...") - - alert_dict = { + + alert_json = json.dumps({ "alert_id": payload.alert_id, "customer_id": payload.customer_id, "alert_type": payload.alert_type, "description": payload.description, "timestamp": payload.timestamp, "severity": payload.severity, - } - - assessment_task: Task[dict] = context.call_activity("run_fraud_analysis", input=alert_dict) - assessment: dict = yield assessment_task - + }) + + context.set_custom_status(json.dumps({ + "message": f"Running fraud analysis for {alert_id}", + "step_details": {}, + "risk_score": None, + })) + + logger.info("[Orchestration] Step 1: Running fraud analysis via agent entity...") + + response: AgentResponse = yield fraud_agent.run( + messages=alert_json, + session=fraud_session, + ) + + # Parse assessment from agent response + assessment = json.loads(response.text) risk_score = assessment.get("overall_risk_score", 0) - recommended_action = assessment.get("recommended_action", "unknown") step_details = assessment.get("step_details", {}) - - logger.info(f"[Orchestration] Analysis complete: risk={risk_score:.2f}, recommended={recommended_action}") - + + logger.info(f"[Orchestration] Analysis complete: risk={risk_score:.2f}") + # ======================================================================== - # Step 2: Route based on risk score + # Step 2: Route based on risk score with HITL feedback loop # ======================================================================== - + result: dict - + attempt = 0 + if risk_score >= 0.6: - # HIGH RISK - Human-in-the-loop - logger.info(f"[Orchestration] HIGH RISK ({risk_score:.2f}) - Requiring analyst review") - context.set_custom_status(json.dumps({ - "message": f"Awaiting analyst review (risk={risk_score:.2f})", - "step_details": step_details, - "risk_score": risk_score, - })) - - # Notify analyst - yield context.call_activity("notify_analyst", input=assessment) - - # Wait for analyst decision OR timeout - approval_task: Task[Any] = context.wait_for_external_event(ANALYST_APPROVAL_EVENT) - timeout_task: Task[Any] = context.create_timer( - context.current_utc_datetime + timedelta(hours=payload.approval_timeout_hours) - ) - - logger.info(f"[Orchestration] Waiting for analyst decision (timeout: {payload.approval_timeout_hours}h)") - - winner_task = yield when_any([approval_task, timeout_task]) - - if winner_task == approval_task: - # Analyst responded - decision_data: Any = approval_task.get_result() - logger.info(f"[Orchestration] Received analyst decision: {decision_data}") - - # Parse decision - if isinstance(decision_data, dict): - decision = AnalystDecision.model_validate(decision_data) - else: - # Handle string or other formats - decision = AnalystDecision( - alert_id=alert_id, - approved_action=str(decision_data), - analyst_notes="", - analyst_id="unknown", - ) - - # Update step_details with review_gateway completion - step_details["review_gateway"] = { - "status": "completed", - "tool_calls": [{ - "name": "analyst_decision", - "arguments": {"action": decision.approved_action}, - "result": f"Approved by {decision.analyst_id}: {decision.analyst_notes or 'No notes'}" - }], - "output": f"Action approved: {decision.approved_action}", - } - - context.set_custom_status(json.dumps({ - "message": "Executing analyst-approved action", - "step_details": step_details, - "risk_score": risk_score, - })) - - # Execute the approved action - action_result: dict = yield context.call_activity( - "execute_fraud_action", - input=decision.model_dump() + # HIGH RISK - Human-in-the-loop with feedback loop + while attempt < payload.max_review_attempts: + attempt += 1 + logger.info( + f"[Orchestration] HIGH RISK ({risk_score:.2f}) - " + f"Analyst review (attempt {attempt}/{payload.max_review_attempts})" ) - result = action_result - - # Update step_details with fraud_action_executor completion - step_details["fraud_action_executor"] = { - "status": "completed", - "tool_calls": [{ - "name": "execute_fraud_action", - "arguments": {"action": decision.approved_action, "alert_id": alert_id}, - "result": f"Action executed: {result.get('action_taken', 'unknown')}" - }], - "output": result.get("details", f"Executed action: {decision.approved_action}"), - } - - else: - # Timeout - escalate - logger.warning(f"[Orchestration] Analyst review timed out after {payload.approval_timeout_hours}h") + context.set_custom_status(json.dumps({ - "message": "Review timed out - escalating", + "message": f"Awaiting analyst review (risk={risk_score:.2f}, attempt {attempt})", "step_details": step_details, "risk_score": risk_score, })) - - escalation_result: dict = yield context.call_activity( - "escalate_timeout", - input=assessment + + # Notify analyst + yield context.call_activity("notify_analyst", input=response.text) + + # Wait for analyst decision OR timeout + approval_task: Task[Any] = context.wait_for_external_event(ANALYST_APPROVAL_EVENT) + timeout_task: Task[Any] = context.create_timer( + context.current_utc_datetime + timedelta(hours=payload.approval_timeout_hours) ) - result = escalation_result - + + logger.info(f"[Orchestration] Waiting for analyst decision (timeout: {payload.approval_timeout_hours}h)") + winner_task = yield when_any([approval_task, timeout_task]) + + if winner_task == approval_task: + # Analyst responded + decision_data: Any = approval_task.get_result() + logger.info(f"[Orchestration] Received analyst decision: {decision_data}") + + # Parse decision + if isinstance(decision_data, dict): + decision = AnalystDecision.model_validate(decision_data) + else: + decision = AnalystDecision( + alert_id=alert_id, + approved=True, + approved_action=str(decision_data), + ) + + if decision.approved: + # ==================================================== + # APPROVED: Execute the action + # ==================================================== + logger.info(f"[Orchestration] ✅ Analyst approved: {decision.approved_action}") + + step_details["review_gateway"] = { + "status": "completed", + "tool_calls": [{ + "name": "analyst_decision", + "arguments": {"action": decision.approved_action}, + "result": f"Approved by {decision.analyst_id}" + }], + "output": f"Action approved: {decision.approved_action}", + } + + context.set_custom_status(json.dumps({ + "message": "Executing analyst-approved action", + "step_details": step_details, + "risk_score": risk_score, + })) + + action_result: dict = yield context.call_activity( + "execute_fraud_action", + input=decision.model_dump(), + ) + result = action_result + + step_details["fraud_action_executor"] = { + "status": "completed", + "tool_calls": [{ + "name": "execute_fraud_action", + "arguments": {"action": decision.approved_action}, + "result": f"Executed: {result.get('action_taken')}" + }], + "output": result.get("details", "Action executed"), + } + break # Exit the HITL loop + + else: + # ==================================================== + # REJECTED: Re-investigate with feedback (SAME SESSION) + # ==================================================== + feedback = decision.feedback or decision.analyst_notes or "No specific feedback" + logger.info(f"[Orchestration] ❌ Analyst rejected. Feedback: {feedback}") + + if attempt >= payload.max_review_attempts: + logger.warning("[Orchestration] Max review attempts exhausted") + step_details["review_gateway"] = { + "status": "completed", + "output": f"Max attempts ({payload.max_review_attempts}) exhausted", + } + result = ActionResult( + alert_id=alert_id, + action_taken="max_attempts_exhausted", + success=False, + details=f"Could not resolve after {payload.max_review_attempts} reviews", + ).model_dump() + break + + step_details["review_gateway"] = { + "status": "rejected", + "tool_calls": [{ + "name": "analyst_decision", + "arguments": {"feedback": feedback}, + "result": f"Rejected by {decision.analyst_id}: {feedback}" + }], + "output": f"Rejected - re-investigating (attempt {attempt + 1})", + } + + context.set_custom_status(json.dumps({ + "message": f"Re-investigating with analyst feedback (attempt {attempt + 1})", + "step_details": step_details, + "risk_score": risk_score, + })) + + # *** STATEFUL RE-INVESTIGATION *** + # Same session -> entity retains full conversation history + # Agent sees: original alert + first analysis + feedback + feedback_msg = ( + f"The analyst REJECTED the previous assessment and requests re-investigation.\n" + f"Analyst feedback: {feedback}\n" + f"Please conduct a deeper investigation focusing on the analyst's concerns." + ) + + logger.info("[Orchestration] Re-running fraud analysis with feedback context...") + + response = yield fraud_agent.run( + messages=feedback_msg, + session=fraud_session, # SAME SESSION = stateful! + ) + + # Parse updated assessment + assessment = json.loads(response.text) + risk_score = assessment.get("overall_risk_score", 0) + step_details = assessment.get("step_details", {}) + + logger.info(f"[Orchestration] Re-investigation complete: risk={risk_score:.2f}") + continue # Loop back for another review + + else: + # Timeout - escalate + logger.warning("[Orchestration] Analyst review timed out") + context.set_custom_status(json.dumps({ + "message": "Review timed out - escalating", + "step_details": step_details, + "risk_score": risk_score, + })) + + escalation_result: dict = yield context.call_activity( + "escalate_timeout", input=response.text + ) + result = escalation_result + break + + else: + # while loop exhausted without break (safety fallback) + result = ActionResult( + alert_id=alert_id, + action_taken="max_attempts_exhausted", + success=False, + details="Review loop exhausted", + ).model_dump() + else: # LOW RISK - Auto-clear logger.info(f"[Orchestration] LOW RISK ({risk_score:.2f}) - Auto-clearing") @@ -471,62 +622,48 @@ def fraud_detection_orchestration( "step_details": step_details, "risk_score": risk_score, })) - - clear_result: dict = yield context.call_activity("auto_clear_alert", input=assessment) + + clear_result: dict = yield context.call_activity( + "auto_clear_alert", input=response.text + ) result = clear_result - - # Update step_details with auto_clear_executor completion + step_details["auto_clear_executor"] = { "status": "completed", - "tool_calls": [{ - "name": "auto_clear_alert", - "arguments": {"alert_id": alert_id, "risk_score": risk_score}, - "result": f"Alert auto-cleared (low risk: {risk_score:.2f})" - }], - "output": result.get("details", "Alert automatically cleared due to low risk score"), + "output": result.get("details", "Auto-cleared"), } - + # ======================================================================== # Step 3: Send final notification # ======================================================================== - + logger.info("[Orchestration] Step 3: Sending final notification") context.set_custom_status(json.dumps({ "message": "Sending notification", "step_details": step_details, "risk_score": risk_score, })) - + yield context.call_activity("send_notification", input=result) - - # Update step_details with final_notification_executor completion - step_details["final_notification_executor"] = { - "status": "completed", - "tool_calls": [{ - "name": "send_notification", - "arguments": {"alert_id": alert_id, "action_taken": result.get("action_taken")}, - "result": "Notification sent to customer and internal teams" - }], - "output": f"Notification sent for alert {alert_id}", - } - + # ======================================================================== # Complete # ======================================================================== - + logger.info(f"[Orchestration] ✅ Fraud detection completed for alert {alert_id}") context.set_custom_status(json.dumps({ "message": "Completed", "step_details": step_details, "risk_score": risk_score, })) - + return { "alert_id": alert_id, "status": "completed", "risk_score": risk_score, "action_taken": result.get("action_taken"), "success": result.get("success"), + "review_attempts": attempt, "step_details": step_details, } @@ -543,13 +680,12 @@ def get_worker( """Create a configured DurableTaskSchedulerWorker.""" taskhub_name = taskhub or os.getenv("DTS_TASKHUB", "default") endpoint_url = endpoint or os.getenv("DTS_ENDPOINT", "http://localhost:8080") - + logger.info(f"Using DTS endpoint: {endpoint_url}") logger.info(f"Using taskhub: {taskhub_name}") - - # Use credentials for Azure-hosted DTS, None for local emulator + credential = None if endpoint_url.startswith("http://localhost") else DefaultAzureCredential() - + return DurableTaskSchedulerWorker( host_address=endpoint_url, secure_channel=not endpoint_url.startswith("http://localhost"), @@ -558,59 +694,85 @@ def get_worker( ) -def setup_worker(worker: DurableTaskSchedulerWorker) -> None: - """Set up the worker with orchestrations and activities.""" - +def setup_worker(worker: DurableTaskSchedulerWorker) -> DurableAIAgentWorker: + """Set up the worker with agents, orchestrations, and activities. + + Returns: + DurableAIAgentWorker with agents registered + """ + # Wrap raw worker with DurableAIAgentWorker for agent entity registration + agent_worker = DurableAIAgentWorker(worker) + + # Register FraudAnalysisAgent as a durable entity (dafx-FraudAnalysisAgent) + logger.info("Registering FraudAnalysisAgent as durable entity...") + fraud_agent = FraudAnalysisAgent() + agent_worker.add_agent(fraud_agent) + logger.info(f"✓ Registered entity: dafx-{fraud_agent.name}") + + # Register activity functions on the raw worker logger.info("Registering activities...") - worker.add_activity(run_fraud_analysis) worker.add_activity(notify_analyst) worker.add_activity(execute_fraud_action) worker.add_activity(auto_clear_alert) worker.add_activity(escalate_timeout) worker.add_activity(send_notification) logger.info("✓ Activities registered") - + + # Register the orchestration on the raw worker logger.info("Registering orchestration...") worker.add_orchestrator(fraud_detection_orchestration) logger.info("✓ Orchestration registered") + return agent_worker + async def main(): """Main entry point for the worker process.""" - logger.info("="*60) + logger.info("=" * 60) logger.info("Starting Durable Fraud Detection Worker") - logger.info("="*60) - - # Pre-initialize resources - logger.info("Initializing resources...") - try: - await _ensure_resources() - except Exception as e: - logger.error(f"Failed to initialize resources: {e}") - logger.error("Make sure MCP server is running and Azure OpenAI is configured") - return - + logger.info(" Architecture: DurableAIAgentWorker + Workflow + HITL") + logger.info("=" * 60) + # Create and setup worker worker = get_worker() - setup_worker(worker) - + agent_worker = setup_worker(worker) + logger.info("") + logger.info(f"Registered agents: {agent_worker.registered_agent_names}") logger.info("Worker is ready and listening for orchestrations!") logger.info("Dashboard: http://localhost:8082") logger.info("Press Ctrl+C to stop.") logger.info("") - + try: worker.start() - - # Keep running - while True: - await asyncio.sleep(1) + + # Keep running — use threading.Event for Windows compatibility. + # asyncio.sleep() can be cancelled by gRPC's SIGINT handler on Windows, + # so we fall back to a thread-safe wait. + import threading + stop_event = threading.Event() + try: + while not stop_event.wait(timeout=1): + pass + except (KeyboardInterrupt, asyncio.CancelledError): + pass except KeyboardInterrupt: logger.info("Worker shutdown initiated") - + finally: + # Gracefully stop the worker to tear down gRPC channels. + # Without this, the gRPC C-extension thread can keep the process + # alive indefinitely even after the Python runtime shuts down. + try: + worker.stop() + except Exception: + pass + logger.info("Worker stopped") if __name__ == "__main__": - asyncio.run(main()) + try: + asyncio.run(main()) + except KeyboardInterrupt: + pass diff --git a/mcp/mcp_service.py b/mcp/mcp_service.py index ae5052aab..53fb89bf1 100644 --- a/mcp/mcp_service.py +++ b/mcp/mcp_service.py @@ -416,17 +416,17 @@ async def _protected_resource_metadata(request: Request): # TOOL ENDPOINTS # ############################################################################## @mcp.tool(description="List all customers with basic info") -async def get_all_customers() -> List[CustomerSummary]: +async def get_all_customers() -> list: data = await get_all_customers_async() - return [CustomerSummary(**r) for r in data] + return data @mcp.tool(description="Get a full customer profile including their subscriptions") async def get_customer_detail( customer_id: Annotated[int, "Customer identifier value"], -) -> CustomerDetail: +) -> dict: data = await get_customer_detail_async(customer_id) - return CustomerDetail(**data) + return data @mcp.tool( @@ -436,26 +436,17 @@ async def get_customer_detail( ) async def get_subscription_detail( subscription_id: Annotated[int, "Subscription identifier value"], -) -> SubscriptionDetail: +) -> dict: data = await get_subscription_detail_async(subscription_id) - - # Convert nested data to Pydantic models - invoices = [] - for inv_data in data['invoices']: - payments = [Payment(**p) for p in inv_data['payments']] - invoices.append(Invoice(**{**inv_data, 'payments': payments})) - - service_incidents = [ServiceIncident(**si) for si in data['service_incidents']] - - return SubscriptionDetail(**{**data, 'invoices': invoices, 'service_incidents': service_incidents}) + return data @mcp.tool(description="Return invoice‑level payments list") async def get_invoice_payments( invoice_id: Annotated[int, "Invoice identifier value"], -) -> List[Payment]: +) -> list: data = await get_invoice_payments_async(invoice_id) - return [Payment(**r) for r in data] + return data @mcp.tool(description="Record a payment for a given invoice and get new outstanding balance") @@ -473,17 +464,15 @@ async def get_data_usage( start_date: Annotated[str, "Inclusive start date (YYYY-MM-DD)"], end_date: Annotated[str, "Inclusive end date (YYYY-MM-DD)"], aggregate: Annotated[bool, "Set to true for aggregate statistics"] = False, -) -> List[DataUsageRecord] | Dict[str, Any]: +) -> list | Dict[str, Any]: result = await get_data_usage_async(subscription_id, start_date, end_date, aggregate) - if aggregate: - return result - return [DataUsageRecord(**r) for r in result] + return result @mcp.tool(description="List every active promotion (no filtering)") -async def get_promotions() -> List[Promotion]: +async def get_promotions() -> list: data = await get_promotions_async() - return [Promotion(**r) for r in data] + return data @mcp.tool( @@ -492,9 +481,9 @@ async def get_promotions() -> List[Promotion]: ) async def get_eligible_promotions( customer_id: Annotated[int, "Customer identifier value"], -) -> List[Promotion]: +) -> list: data = await get_eligible_promotions_async(customer_id) - return [Promotion(**r) for r in data] + return data # ─── Knowledge Base Search ─────────────────────────────────────────────── @@ -502,27 +491,27 @@ async def get_eligible_promotions( async def search_knowledge_base( query: Annotated[str, "Natural language query"], topk: Annotated[int, "Number of top documents to return"] = 3, -) -> List[KBDoc]: +) -> list: data = await search_knowledge_base_async(query, topk) - return [KBDoc(**r) for r in data] + return data # ─── Security Logs ─────────────────────────────────────────────────────── @mcp.tool(description="Security events for a customer (newest first)") async def get_security_logs( customer_id: Annotated[int, "Customer identifier value"], -) -> List[SecurityLog]: +) -> list: data = await get_security_logs_async(customer_id) - return [SecurityLog(**r) for r in data] + return data # ─── Orders ────────────────────────────────────────────────────────────── @mcp.tool(description="All orders placed by a customer") async def get_customer_orders( customer_id: Annotated[int, "Customer identifier value"], -) -> List[Order]: +) -> list: data = await get_customer_orders_async(customer_id) - return [Order(**r) for r in data] + return data # ─── Support Tickets ──────────────────────────────────────────────────── @@ -530,9 +519,9 @@ async def get_customer_orders( async def get_support_tickets( customer_id: Annotated[int, "Customer identifier value"], open_only: Annotated[bool, "Filter to open tickets"] = False, -) -> List[SupportTicket]: +) -> list: data = await get_support_tickets_async(customer_id, open_only) - return [SupportTicket(**r) for r in data] + return data @mcp.tool(description="Create a new support ticket for a customer") @@ -543,9 +532,9 @@ async def create_support_ticket( priority: Annotated[str, "Ticket priority"], subject: Annotated[str, "Ticket subject"], description: Annotated[str, "Ticket description"], -) -> SupportTicket: +) -> dict: data = await create_support_ticket_async(customer_id, subscription_id, category, priority, subject, description) - return SupportTicket(**data) + return data # ─── Products ──────────────────────────────────────────────────────────── @@ -560,17 +549,17 @@ class Product(BaseModel): @mcp.tool(description="List / search available products (optional category filter)") async def get_products( category: Annotated[Optional[str], "Optional category filter"] = None, -) -> List[Product]: +) -> list: data = await get_products_async(category) - return [Product(**r) for r in data] + return data @mcp.tool(description="Return a single product by ID") async def get_product_detail( product_id: Annotated[int, "Product identifier value"], -) -> Product: +) -> dict: data = await get_product_detail_async(product_id) - return Product(**data) + return data # ─── Update Subscription ──────────────────────────────────────────────── From 85c574faa41e68c7d5c2b0571b20e3171348ece1 Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 10:35:08 -0800 Subject: [PATCH 05/37] chore: reduce anomaly probability to 1% for controlled demo pace --- agentic_ai/workflow/fraud_detection_durable/backend.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agentic_ai/workflow/fraud_detection_durable/backend.py b/agentic_ai/workflow/fraud_detection_durable/backend.py index 729d77cd6..dfe8f550e 100644 --- a/agentic_ai/workflow/fraud_detection_durable/backend.py +++ b/agentic_ai/workflow/fraud_detection_durable/backend.py @@ -262,7 +262,7 @@ async def broadcast(self, instance_id: str, message: dict): event_producer = EventProducer( interval_seconds=float(os.getenv("EVENT_INTERVAL_SECONDS", "3.0")), - anomaly_probability=float(os.getenv("ANOMALY_PROBABILITY", "0.08")), + anomaly_probability=float(os.getenv("ANOMALY_PROBABILITY", "0.01")), ) _event_producer_task: asyncio.Task | None = None From 2c193580cf3f7c7cb1467b46e11db581f2bbde5f Mon Sep 17 00:00:00 2001 From: James Nguyen Date: Thu, 19 Feb 2026 11:01:34 -0800 Subject: [PATCH 06/37] Reduce anomaly probability to 1% for controlled demo pace (#406) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * chore: reduce anomaly probability to 1% for controlled demo pace --------- Co-authored-by: James N. From 6c8527c611a6331c3fc9708ad45902552106435b Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 11:17:21 -0800 Subject: [PATCH 07/37] add mcp_agent_demo --- agentic_ai/agents/mcp_agent_demo/README.md | 239 +++ .../agents/mcp_agent_demo/mcp_client_agent.py | 89 + .../mcp_agent_demo/mcp_client_stateful.py | 120 ++ .../agents/mcp_agent_demo/mcp_server.py | 143 ++ .../mcp_agent_demo/mcp_server_stateful.py | 210 +++ .../agents/mcp_agent_demo/pyproject.toml | 16 + agentic_ai/agents/mcp_agent_demo/uv.lock | 1545 +++++++++++++++++ .../mcp_agent_demo/workflow_local_remote.py | 124 ++ 8 files changed, 2486 insertions(+) create mode 100644 agentic_ai/agents/mcp_agent_demo/README.md create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_server.py create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py create mode 100644 agentic_ai/agents/mcp_agent_demo/pyproject.toml create mode 100644 agentic_ai/agents/mcp_agent_demo/uv.lock create mode 100644 agentic_ai/agents/mcp_agent_demo/workflow_local_remote.py diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md new file mode 100644 index 000000000..8a57fb3d2 --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -0,0 +1,239 @@ +# MCP Agent Demo — Agent Framework + +This demo shows five capabilities of the Microsoft Agent Framework: + +| # | What | Script | +|---|------|--------| +| 1 | **Expose an agent as an MCP server** (stateless HTTP) | `mcp_server.py` | +| 2 | **Consume the agent-powered MCP service** from another agent | `mcp_client_agent.py` | +| 3 | **Workflow orchestrating local + remote (MCP) agents** | `workflow_local_remote.py` | +| 4 | **Stateful agent-as-MCP server** (multi-turn sessions) | `mcp_server_stateful.py` | +| 5 | **Stateful multi-turn client agent** conversation via MCP | `mcp_client_stateful.py` | + +## Architecture + +### Stateless Mode (Scripts 1–3) + +``` +┌──────────────────────────────────────────────────────────────┐ +│ Workflow (SequentialBuilder) │ +│ │ +│ ┌──────────────┐ ┌─────────────────────────────┐ │ +│ │ Local Agent │ ───▶ │ Remote Agent (via MCP tool) │ │ +│ │ "Researcher" │ │ "Expert Advisor" │ │ +│ │ (in-process) │ │ (MCPStreamableHTTPTool) │ │ +│ └──────────────┘ └──────────┬──────────────────┘ │ +│ │ HTTP │ +└──────────────────────────────────────┼───────────────────────┘ + ▼ + ┌────────────────────────┐ + │ MCP Server (port 8002)│ + │ Expert Advisor Agent │ + │ (stateless_http=True) │ + └────────────────────────┘ +``` + +### Stateful Mode (Scripts 4–5) + +``` +Client Agent (Coordinator) Server (FastMCP v3) +┌───────────────────────────┐ ┌──────────────────────────────────┐ +│ MCPStreamableHTTPTool │ ── session ──→ │ mcp-session-id → AgentSession │ +│ (auto-sends session ID │ preserved │ ↓ │ +│ via mcp-session-id hdr) │ │ Agent.run(msg, session=session) │ +│ │ │ → InMemoryHistoryProvider │ +│ Turn 1: "What risks?" │ ─────────────→ │ → history auto-managed │ +│ Turn 2: "Market data?" │ ─────────────→ │ → builds on prior turns │ +│ Turn 3: "Summarize all" │ ─────────────→ │ → references full context │ +└───────────────────────────┘ └──────────────────────────────────┘ +``` + +**Key difference:** In stateful mode, the server uses PrefectHQ's +[FastMCP v3](https://github.com/PrefectHQ/fastmcp) (default `stateless_http=False`) +and maps each MCP `session_id` to an `AgentSession` that accumulates conversation +history across tool calls. + +## Prerequisites + +- Azure OpenAI credentials in `mcp/.env` (already configured) +- Python 3.12+, `uv` + +## Quick Start + +### 1. Start the MCP Agent Server + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv sync +uv run python mcp_server.py +``` + +This starts an MCP server on `http://localhost:8002/mcp` that exposes an +"Expert Advisor" agent as a tool via the MCP Streamable HTTP transport. + +### 2. Run the Client Agent (in a second terminal) + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv run python mcp_client_agent.py +``` + +This creates a local agent that connects to the MCP server and delegates +questions to the remote Expert Advisor agent. + +### 3. Run the Workflow Demo (in a second terminal) + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv run python workflow_local_remote.py +``` + +This demonstrates a **SequentialBuilder** workflow that chains: +1. A **local Researcher agent** (in-process) → drafts initial analysis +2. A **remote Expert Advisor agent** (via MCP) → reviews and enhances + +--- + +### 4. Start the Stateful MCP Agent Server + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv run python mcp_server_stateful.py +``` + +This starts a **stateful** MCP server on `http://localhost:8002/mcp` using +[FastMCP v3](https://github.com/PrefectHQ/fastmcp). Each client session gets +its own `AgentSession`, so the expert agent remembers the full conversation +history across tool calls. + +Tools exposed: `chat_with_expert`, `get_session_info`, `reset_conversation` + +### 5. Run the Stateful Multi-Turn Client (in a second terminal) + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv run python mcp_client_stateful.py +``` + +This runs a 3-turn conversation where each follow-up builds on the prior: +1. "What are the main risks of expanding into technology?" +2. "Based on that analysis, what does market data look like?" +3. "Summarize everything we discussed into an executive recommendation." + +The remote expert references prior turns because the `mcp-session-id` header +ties all requests to the same server-side `AgentSession`. + +## How It Works + +### Exposing an Agent as MCP Server + +```python +from agent_framework import Agent, tool +from agent_framework.azure import AzureOpenAIChatClient + +# Create an agent with tools +agent = Agent(client=AzureOpenAIChatClient(...), tools=[...]) + +# Convert to MCP server (low-level Server from mcp SDK) +server = agent.as_mcp_server(server_name="ExpertAdvisor") + +# Serve over HTTP using FastMCP's streamable HTTP transport +from mcp.server.fastmcp import FastMCP +mcp_app = FastMCP("ExpertAdvisor", stateless_http=True, json_response=True) +# ... register agent tool ... +mcp_app.run(transport="streamable-http", port=8002) +``` + +### Consuming MCP Service from Another Agent + +```python +from agent_framework import Agent, MCPStreamableHTTPTool +from agent_framework.azure import AzureOpenAIChatClient + +async with MCPStreamableHTTPTool( + name="expert_advisor", + url="http://localhost:8002/mcp", +) as mcp_tool: + async with Agent( + client=AzureOpenAIChatClient(...), + tools=mcp_tool, + ) as agent: + result = await agent.run("Analyze this business scenario...") +``` + +### Workflow with Local + Remote Agents + +```python +from agent_framework.orchestrations import SequentialBuilder + +# Local agent (in-process) +researcher = client.as_agent(name="researcher", instructions="...") + +# Remote agent wrapper (via MCP tool) +remote_expert = Agent(client=client, tools=mcp_tool, name="expert") + +# Sequential workflow: researcher → expert +workflow = SequentialBuilder(participants=[researcher, remote_expert]).build() +result = await workflow.run("Analyze the market for electric bikes") +``` + +### Stateful Agent-as-MCP Server (Multi-Turn) + +The stateful server uses [FastMCP v3](https://github.com/PrefectHQ/fastmcp) +instead of `mcp.server.fastmcp.FastMCP` from the base MCP SDK. Two things +make multi-turn work: + +1. **Server side:** Each MCP `session_id` maps to an `AgentSession`. + Passing the same session to `agent.run()` lets the framework's + `InMemoryHistoryProvider` accumulate messages automatically. + +2. **Client side:** `MCPStreamableHTTPTool` sends the `mcp-session-id` + header on every request after initialization — no extra code needed. + +```python +# Server — stateful MCP with session-scoped agent history +from fastmcp import FastMCP # PrefectHQ fastmcp v3 +from fastmcp.server.context import Context +from agent_framework import Agent +from agent_framework._sessions import AgentSession + +agent_sessions: dict[str, AgentSession] = {} +mcp_server = FastMCP("StatefulExpert") + +@mcp_server.tool +async def chat_with_expert(message: str, ctx: Context) -> str: + session_id = ctx.session_id + if session_id not in agent_sessions: + agent_sessions[session_id] = AgentSession() + session = agent_sessions[session_id] + + async with agent: + response = await agent.run(message, session=session) + return response.text + +mcp_server.run(transport="streamable-http", host="0.0.0.0", port=8002) +``` + +```python +# Client — multi-turn conversation (session auto-managed) +from agent_framework import Agent, MCPStreamableHTTPTool + +async with MCPStreamableHTTPTool( + name="expert", url="http://localhost:8002/mcp" +) as mcp_tool: + async with Agent(client=client, tools=mcp_tool) as coordinator: + r1 = await coordinator.run("What are the risks?") # Turn 1 + r2 = await coordinator.run("Show me market data.") # Turn 2 + r3 = await coordinator.run("Summarize everything.") # Turn 3 + # The expert remembers all prior turns ✓ +``` + +## Dependencies + +| Package | Purpose | +|---------|---------| +| `agent-framework-core` | Agent, MCPStreamableHTTPTool, tool decorator | +| `agent-framework-orchestrations` | SequentialBuilder for workflows | +| `mcp` | MCP SDK (used by stateless server via `mcp.server.fastmcp.FastMCP`) | +| `fastmcp` | PrefectHQ FastMCP v3 (used by stateful server — session state support) | +| `python-dotenv` | Load credentials from `mcp/.env` | diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py b/agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py new file mode 100644 index 000000000..8bd4fb6f9 --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py @@ -0,0 +1,89 @@ +""" +Part 2 — Consume the agent-powered MCP service from another agent. + +This script creates a local "Coordinator" agent that connects to the +remote Expert Advisor MCP server (Part 1) and delegates business +analysis questions to it. + +Prerequisites: + mcp_server.py must be running on http://localhost:8002/mcp + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_client_agent.py +""" + +import asyncio +import os +import sys + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, MCPStreamableHTTPTool +from agent_framework.azure import AzureOpenAIChatClient + + +async def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + mcp_server_url = "http://localhost:8002/mcp" + print(f"🔗 Connecting to MCP server at {mcp_server_url} ...") + + # 1) Create the MCPStreamableHTTPTool — this connects to the remote agent + async with MCPStreamableHTTPTool( + name="expert_advisor", + description="Remote Expert Advisor agent accessible via MCP — can analyze risk, look up market data, and summarize findings.", + url=mcp_server_url, + ) as mcp_tool: + print(f"✅ Connected! Tools available from MCP server: {[t.name for t in mcp_tool.functions]}") + + # 2) Create a local Coordinator agent that wraps the MCP tool + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + async with Agent( + client=client, + name="Coordinator", + instructions=( + "You are a project coordinator. When users ask business strategy questions, " + "use the expert_advisor MCP tool to get expert analysis. Present the results " + "clearly and add your own coordination notes." + ), + tools=mcp_tool, + ) as coordinator: + # 3) Ask some questions + queries = [ + "What are the main risks if we expand into the technology sector?", + "Give me current market data for the healthcare sector and summarize it.", + ] + + for query in queries: + print(f"\n{'='*60}") + print(f"📝 User: {query}") + print(f"{'='*60}") + + response = await coordinator.run(query) + + print(f"\n🤖 Coordinator:\n{response.text}") + print(f"{'─'*60}") + + print("\n✅ Demo complete!") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py b/agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py new file mode 100644 index 000000000..88ec20689 --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py @@ -0,0 +1,120 @@ +""" +Stateful MCP Client — multi-turn agent-to-agent conversation. + +This script demonstrates a local agent-framework Agent having a multi-turn, +stateful conversation with a remote Expert Advisor agent served via MCP. + +The key insight: + • MCPStreamableHTTPTool maintains the MCP session (sends mcp-session-id + header automatically), so the *remote* agent remembers prior turns. + • The local coordinator agent also maintains its own conversation context. + +Prerequisites: + mcp_server_stateful.py must be running on http://localhost:8002/mcp + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_client_stateful.py +""" + +import asyncio +import os +import sys + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, MCPStreamableHTTPTool +from agent_framework.azure import AzureOpenAIChatClient + + +async def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + mcp_server_url = "http://localhost:8002/mcp" + print(f"🔗 Connecting to stateful MCP server at {mcp_server_url} ...") + + # ── Connect to the stateful MCP server ────────────────────────────── + # MCPStreamableHTTPTool maintains the MCP session automatically. + # All calls within this context block share the same mcp-session-id, + # so the remote agent retains conversation history. + async with MCPStreamableHTTPTool( + name="stateful_expert", + description=( + "Remote Expert Advisor agent (stateful). Supports multi-turn " + "conversations — the expert remembers what was discussed earlier. " + "Tools: chat_with_expert, get_session_info, reset_conversation." + ), + url=mcp_server_url, + ) as mcp_tool: + tool_names = [t.name for t in mcp_tool.functions] + print(f"✅ Connected! Tools available: {tool_names}") + + # ── Create the local coordinator agent ────────────────────────── + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + async with Agent( + client=client, + name="MultiTurnCoordinator", + instructions=( + "You coordinate multi-turn conversations with a remote expert advisor.\n\n" + "RULES:\n" + "1. When the user asks a question, use the chat_with_expert tool to send it.\n" + "2. The expert remembers previous turns — you can ask follow-up questions.\n" + "3. Present the expert's response clearly, adding brief coordination notes.\n" + "4. If asked for session info, use the get_session_info tool.\n" + "5. If asked to reset, use the reset_conversation tool.\n" + "6. Do NOT repeat the full question in your tool call — keep it concise." + ), + tools=mcp_tool, + ) as coordinator: + + # ── Multi-turn conversation ───────────────────────────────── + # Each question builds on the previous one. The remote expert + # maintains state across calls thanks to the stateful MCP session. + conversation = [ + "What are the main risks if we expand into the technology sector?", + "Based on that risk analysis, what does current market data for technology look like? Are the risks justified by the opportunity?", + "Now summarize everything we've discussed — the risks AND the market data — into a brief executive recommendation.", + ] + + for i, question in enumerate(conversation, 1): + print(f"\n{'='*70}") + print(f" TURN {i}") + print(f"{'='*70}") + print(f"📝 User: {question}\n") + + response = await coordinator.run(question) + + print(f"🤖 Coordinator:\n{response.text}") + print(f"{'─'*70}") + + # ── Show session metadata ─────────────────────────────────── + print(f"\n{'='*70}") + print(" SESSION INFO") + print(f"{'='*70}") + info_response = await coordinator.run( + "Use the get_session_info tool and tell me how many turns we've had." + ) + print(f"📊 {info_response.text}") + + print("\n✅ Stateful multi-turn demo complete!") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server.py b/agentic_ai/agents/mcp_agent_demo/mcp_server.py new file mode 100644 index 000000000..1fda8ceea --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server.py @@ -0,0 +1,143 @@ +""" +Part 1 — Expose an Agent-Framework agent as an MCP HTTP server. + +This script creates an Azure OpenAI–powered agent with domain tools, +then serves it as an MCP Streamable-HTTP endpoint on port 8002. + +Any MCP client (Claude Desktop, VS Code Copilot, or another agent using +MCPStreamableHTTPTool) can connect to http://localhost:8002/mcp and +invoke the agent. + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_server.py +""" + +import asyncio +import os +import sys +from typing import Annotated + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, tool +from agent_framework.azure import AzureOpenAIChatClient +from mcp.server.fastmcp import FastMCP + +# ── Domain tools the agent can use ────────────────────────────────────────── + +@tool(approval_mode="never_require") +def analyze_risk( + scenario: Annotated[str, "A brief description of the business scenario to assess"], +) -> Annotated[str, "A risk analysis summary"]: + """Analyze business risk for a given scenario and return a structured assessment.""" + # Simulated risk analysis — in production this would call a model or DB + return ( + f"Risk Analysis for: {scenario}\n" + "─────────────────────────────\n" + "• Market Risk: MEDIUM — competitive landscape is evolving\n" + "• Financial Risk: LOW — strong cash position\n" + "• Operational Risk: MEDIUM — supply chain dependencies noted\n" + "• Regulatory Risk: LOW — compliant with current frameworks\n" + "• Overall Rating: MEDIUM\n" + "• Recommendation: Proceed with standard due diligence." + ) + + +@tool(approval_mode="never_require") +def get_market_data( + sector: Annotated[str, "Industry sector to look up, e.g. 'technology', 'healthcare'"], +) -> Annotated[str, "Market data summary for the sector"]: + """Retrieve current market data for a given industry sector.""" + data = { + "technology": "Tech sector: YTD +18%, P/E 28x, top movers: AI infrastructure, cloud security.", + "healthcare": "Healthcare sector: YTD +8%, P/E 22x, top movers: GLP-1 drugs, digital health.", + "energy": "Energy sector: YTD +5%, P/E 14x, top movers: renewables, grid storage.", + "finance": "Finance sector: YTD +12%, P/E 16x, top movers: fintech, digital payments.", + } + return data.get(sector.lower(), f"No data available for sector '{sector}'. Known sectors: {', '.join(data.keys())}") + + +@tool(approval_mode="never_require") +def summarize_findings( + text: Annotated[str, "The text to summarize"], + max_sentences: Annotated[int, "Maximum number of sentences in the summary"] = 3, +) -> Annotated[str, "A concise summary"]: + """Produce a concise summary of the provided text.""" + sentences = [s.strip() for s in text.replace("\n", " ").split(".") if s.strip()] + summary = ". ".join(sentences[:max_sentences]) + "." + return f"Summary ({max_sentences} sentences max): {summary}" + + +# ── Build and serve ───────────────────────────────────────────────────────── + +def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + print(f"🔧 Azure OpenAI endpoint : {endpoint}") + print(f"🔧 Deployment : {deployment}") + + # 1) Create the agent-framework Agent + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + agent = Agent( + client=client, + name="ExpertAdvisor", + description=( + "An expert business advisor that can analyze risk, retrieve market data, " + "and summarize findings. Ask it any business strategy question." + ), + instructions=( + "You are an expert business strategy advisor. Use your tools to provide " + "thorough, data-backed analysis. Always call analyze_risk for risk questions, " + "get_market_data for sector insights, and summarize_findings to condense results." + ), + tools=[analyze_risk, get_market_data, summarize_findings], + ) + + # 2) Create a FastMCP wrapper that delegates to the agent + # We register the agent as a single MCP tool so any MCP client can invoke it. + mcp_server = FastMCP( + "ExpertAdvisor", + stateless_http=True, + json_response=True, + host="0.0.0.0", + port=8002, + ) + + @mcp_server.tool() + async def ask_expert( + question: Annotated[str, "The business question to ask the expert advisor"], + ) -> str: + """Ask the Expert Advisor agent a business strategy question. It can analyze risk, look up market data, and summarize findings.""" + async with agent: + response = await agent.run(question) + return response.text + + print("──────────────────────────────────────────────────") + print("🚀 MCP Agent Server starting on http://localhost:8002/mcp") + print(" Transport: Streamable HTTP (stateless)") + print(" Tools exposed: ask_expert (wraps ExpertAdvisor agent)") + print("──────────────────────────────────────────────────") + + mcp_server.run(transport="streamable-http") + + +if __name__ == "__main__": + main() diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py b/agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py new file mode 100644 index 000000000..fc96263fc --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py @@ -0,0 +1,210 @@ +""" +Stateful Agent-as-MCP Server — multi-turn conversations with session memory. + +This script exposes an agent-framework Agent as a stateful MCP HTTP server +using PrefectHQ's FastMCP v3. Each MCP client session gets its own +AgentSession, so conversation history is preserved across tool calls. + +The server exposes three MCP tools: + • chat_with_expert — send a message; the expert remembers prior turns + • get_session_info — inspect turn count / session metadata + • reset_conversation — clear history for the current session + +Architecture: + ┌────────────────────────────────────────────┐ + │ FastMCP (stateful, streamable-http) │ + │ │ + │ MCP session_id → AgentSession (history) │ + │ ↓ │ + │ Agent("ExpertAdvisor") + domain tools │ + │ • analyze_risk │ + │ • get_market_data │ + │ • summarize_findings │ + └────────────────────────────────────────────┘ + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_server_stateful.py +""" + +import os +import sys +from typing import Annotated + +from dotenv import load_dotenv + +# ── Load credentials ──────────────────────────────────────────────────────── +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, tool +from agent_framework._sessions import AgentSession +from agent_framework.azure import AzureOpenAIChatClient +from fastmcp import FastMCP +from fastmcp.server.context import Context + +# ── Domain tools the agent can use ────────────────────────────────────────── + + +@tool(approval_mode="never_require") +def analyze_risk( + scenario: Annotated[str, "A brief description of the business scenario to assess"], +) -> Annotated[str, "A risk analysis summary"]: + """Analyze business risk for a given scenario and return a structured assessment.""" + return ( + f"Risk Analysis for: {scenario}\n" + "─────────────────────────────\n" + "• Market Risk: MEDIUM — competitive landscape is evolving\n" + "• Financial Risk: LOW — strong cash position\n" + "• Operational Risk: MEDIUM — supply chain dependencies noted\n" + "• Regulatory Risk: LOW — compliant with current frameworks\n" + "• Overall Rating: MEDIUM\n" + "• Recommendation: Proceed with standard due diligence." + ) + + +@tool(approval_mode="never_require") +def get_market_data( + sector: Annotated[str, "Industry sector to look up, e.g. 'technology', 'healthcare'"], +) -> Annotated[str, "Market data summary for the sector"]: + """Retrieve current market data for a given industry sector.""" + data = { + "technology": "Tech sector: YTD +18%, P/E 28x, top movers: AI infrastructure, cloud security.", + "healthcare": "Healthcare sector: YTD +8%, P/E 22x, top movers: GLP-1 drugs, digital health.", + "energy": "Energy sector: YTD +5%, P/E 14x, top movers: renewables, grid storage.", + "finance": "Finance sector: YTD +12%, P/E 16x, top movers: fintech, digital payments.", + } + return data.get( + sector.lower(), + f"No data available for sector '{sector}'. Known sectors: {', '.join(data.keys())}", + ) + + +@tool(approval_mode="never_require") +def summarize_findings( + text: Annotated[str, "The text to summarize"], + max_sentences: Annotated[int, "Maximum number of sentences in the summary"] = 3, +) -> Annotated[str, "A concise summary"]: + """Produce a concise summary of the provided text.""" + sentences = [s.strip() for s in text.replace("\n", " ").split(".") if s.strip()] + summary = ". ".join(sentences[:max_sentences]) + "." + return f"Summary ({max_sentences} sentences max): {summary}" + + +# ── Build agent and server ────────────────────────────────────────────────── + + +def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + print(f"🔧 Azure OpenAI endpoint : {endpoint}") + print(f"🔧 Deployment : {deployment}") + + # 1) Create the agent-framework Agent + llm_client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + agent = Agent( + client=llm_client, + name="ExpertAdvisor", + description=( + "An expert business advisor that can analyze risk, retrieve market data, " + "and summarize findings. Remembers the full conversation history." + ), + instructions=( + "You are an expert business strategy advisor. Use your tools to provide " + "thorough, data-backed analysis. Always call analyze_risk for risk questions, " + "get_market_data for sector insights, and summarize_findings to condense results.\n\n" + "IMPORTANT: You are in a multi-turn conversation. Reference and build upon " + "earlier turns. If the user asks you to summarize or follow up, recall " + "what was discussed previously." + ), + tools=[analyze_risk, get_market_data, summarize_findings], + ) + + # 2) Session store: MCP session_id → AgentSession + # Each MCP client gets its own conversation thread. + agent_sessions: dict[str, AgentSession] = {} + # Track turn count per session (simple counter) + session_turns: dict[str, int] = {} + + # 3) Create the FastMCP server (stateful by default — sessions persist) + mcp_server = FastMCP("StatefulExpertAdvisor") + + # ── MCP tools ─────────────────────────────────────────────────────── + + @mcp_server.tool + async def chat_with_expert( + message: Annotated[str, "Your message to the expert advisor"], + ctx: Context, + ) -> str: + """Send a message to the Expert Advisor. The conversation history is + maintained across calls within the same MCP session, enabling + multi-turn follow-ups and context-aware responses.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — processing message") + + # Get-or-create AgentSession for this MCP session + if session_id not in agent_sessions: + agent_sessions[session_id] = AgentSession() + session_turns[session_id] = 0 + await ctx.info("New conversation started") + + session_turns[session_id] += 1 + turn = session_turns[session_id] + await ctx.info(f"Processing turn {turn}") + + session = agent_sessions[session_id] + + # Run the agent with session history + async with agent: + response = await agent.run(message, session=session) + + return response.text + + @mcp_server.tool + async def get_session_info(ctx: Context) -> dict: + """Get metadata about the current MCP session and conversation state.""" + session_id = ctx.session_id + turn_count = session_turns.get(session_id, 0) + return { + "session_id": session_id, + "turn_count": turn_count, + "has_history": session_id in agent_sessions, + } + + @mcp_server.tool + async def reset_conversation(ctx: Context) -> str: + """Clear the conversation history for the current MCP session.""" + session_id = ctx.session_id + if session_id in agent_sessions: + del agent_sessions[session_id] + session_turns.pop(session_id, None) + return f"Conversation history cleared for session {session_id[:8]}…" + return "No conversation history to clear." + + # ── Launch ────────────────────────────────────────────────────────── + + print("──────────────────────────────────────────────────") + print("🚀 Stateful MCP Agent Server") + print(" URL: http://localhost:8002/mcp") + print(" Transport: Streamable HTTP (stateful sessions)") + print(" Tools: chat_with_expert, get_session_info, reset_conversation") + print("──────────────────────────────────────────────────") + + mcp_server.run(transport="streamable-http", host="0.0.0.0", port=8002) + + +if 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Advisor" agent (via MCP) reviews and enhances it + +Prerequisites: + mcp_server.py must be running on http://localhost:8002/mcp + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python workflow_local_remote.py +""" + +import asyncio +import os +import sys +from typing import Any, cast + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, MCPStreamableHTTPTool, Message +from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.orchestrations import SequentialBuilder + + +async def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + mcp_server_url = "http://localhost:8002/mcp" + print(f"🔗 Connecting to remote Expert Advisor MCP server at {mcp_server_url} ...") + + # ── Set up the MCP connection to the remote agent ─────────────────────── + async with MCPStreamableHTTPTool( + name="expert_advisor", + description="Remote Expert Advisor accessible via MCP — can analyze risk, look up market data, and summarize findings.", + url=mcp_server_url, + ) as mcp_tool: + print(f"✅ MCP connected. Remote tools: {[t.name for t in mcp_tool.functions]}") + + # ── Create the two agents ─────────────────────────────────────────── + + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + # Agent 1: Local Researcher (runs entirely in-process, no MCP) + researcher = client.as_agent( + name="researcher", + instructions=( + "You are a market researcher. Given a business topic, provide a " + "concise initial analysis covering opportunity, competition, and " + "key trends. Keep it to 3-4 paragraphs. Your output will be " + "reviewed by an expert advisor next." + ), + ) + + # Agent 2: Remote Expert (enhanced with MCP tools from the server) + expert = Agent( + client=client, + name="expert_reviewer", + instructions=( + "You are a senior business strategy expert. You will receive a " + "researcher's initial analysis as context. Use your expert_advisor " + "MCP tools (ask_expert) to run risk analysis and get market data, " + "then provide an enhanced, final recommendation that combines " + "the researcher's draft with your expert tools' output." + ), + tools=mcp_tool, + ) + + # ── Build the Sequential Workflow ─────────────────────────────────── + # Researcher drafts → Expert reviews and enhances + workflow = SequentialBuilder(participants=[researcher, expert]).build() + + # ── Run the workflow ──────────────────────────────────────────────── + topic = "Expanding a mid-size SaaS company into the healthcare AI market" + print(f"\n{'='*70}") + print(f"📋 Workflow Topic: {topic}") + print(f"{'='*70}") + print(" Step 1: Local Researcher → draft analysis") + print(" Step 2: Remote Expert (MCP) → enhanced recommendation") + print(f"{'='*70}\n") + + outputs: list[list[Message]] = [] + async for event in workflow.run(topic, stream=True): + if event.type == "output": + outputs.append(cast(list[Message], event.data)) + + # ── Display results ───────────────────────────────────────────────── + if outputs: + print("\n" + "="*70) + print("📊 WORKFLOW RESULTS") + print("="*70) + for msg in outputs[-1]: + name = msg.author_name or ("assistant" if msg.role == "assistant" else "user") + role_label = "🔬 Researcher" if name == "researcher" else "🎓 Expert Reviewer" if name == "expert_reviewer" else f"👤 {name}" + print(f"\n{'─'*70}") + print(f"{role_label}:") + print(f"{'─'*70}") + print(msg.text) + + print(f"\n{'='*70}") + print("✅ Workflow complete — local agent + remote MCP agent orchestrated!") + print(f"{'='*70}") + + +if __name__ == "__main__": + asyncio.run(main()) From a5394beb4b1b01420c53abc5acec94bc37f8a68b Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 12:40:53 -0800 Subject: [PATCH 08/37] Add hybrid MCP server (strict-schema + natural-language tools) and typed-contract workflow Scripts 6-8 for the MCP agent demo: - workflow_typed_contracts.py: 4-agent IT security incident response pipeline with Pydantic-enforced contracts at every boundary (SecurityAlert, ThreatAssessment, ImpactAnalysis, IncidentResponse) - mcp_server_hybrid.py: Single MCP endpoint exposing BOTH tool types: * Strict-schema: triage_alert, assess_threat, create_response (Pydantic) * Natural-language: ask_security_advisor, explain_for_customer (prose) * Shared session state across all tools - mcp_client_hybrid.py: 5-step incident flow using both tool types with explicit context passing between steps - README.md: Updated from 6 to 8 capabilities with Hybrid Mode architecture diagram and Quick Start sections 7-8 --- agentic_ai/agents/mcp_agent_demo/README.md | 165 ++++- .../mcp_agent_demo/mcp_client_hybrid.py | 280 ++++++++ .../mcp_agent_demo/mcp_server_hybrid.py | 605 +++++++++++++++++ .../workflow_typed_contracts.py | 621 ++++++++++++++++++ 4 files changed, 1670 insertions(+), 1 deletion(-) create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py create mode 100644 agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md index 8a57fb3d2..d1e7549d3 100644 --- a/agentic_ai/agents/mcp_agent_demo/README.md +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -1,6 +1,6 @@ # MCP Agent Demo — Agent Framework -This demo shows five capabilities of the Microsoft Agent Framework: +This demo shows eight capabilities of the Microsoft Agent Framework: | # | What | Script | |---|------|--------| @@ -9,6 +9,9 @@ This demo shows five capabilities of the Microsoft Agent Framework: | 3 | **Workflow orchestrating local + remote (MCP) agents** | `workflow_local_remote.py` | | 4 | **Stateful agent-as-MCP server** (multi-turn sessions) | `mcp_server_stateful.py` | | 5 | **Stateful multi-turn client agent** conversation via MCP | `mcp_client_stateful.py` | +| 6 | **Typed-contract multi-agent workflow** (structured data exchange) | `workflow_typed_contracts.py` | +| 7 | **Hybrid MCP server** — strict-schema + natural-language tools in one endpoint | `mcp_server_hybrid.py` | +| 8 | **Hybrid client** — demonstrates both tool types in a single incident flow | `mcp_client_hybrid.py` | ## Architecture @@ -53,6 +56,40 @@ Client Agent (Coordinator) Server (FastMCP v3) and maps each MCP `session_id` to an `AgentSession` that accumulates conversation history across tool calls. +### Hybrid Mode (Scripts 7–8) + +``` +┌─────────────────────── Hybrid MCP Server (port 8002) ───────────────────────┐ +│ │ +│ STRICT-SCHEMA TOOLS (machines consume) NL TOOLS (humans consume) │ +│ ┌──────────────────────────────────┐ ┌────────────────────────────┐ │ +│ │ triage_alert │ │ ask_security_advisor │ │ +│ │ raw text → SecurityAlert │ │ free-form Q&A, policy │ │ +│ │ (Pydantic, 12 fields) │ │ advice, best practices │ │ +│ ├──────────────────────────────────┤ ├────────────────────────────┤ │ +│ │ assess_threat │ │ explain_for_customer │ │ +│ │ SecurityAlert → ThreatAssess. │ │ translate technical │ │ +│ │ (MITRE ATT&CK, score 0-100) │ │ incident → plain English │ │ +│ ├──────────────────────────────────┤ │ for executives, techs, │ │ +│ │ create_response │ │ or compliance officers │ │ +│ │ ThreatAssess. → IncidentResp. │ └────────────────────────────┘ │ +│ │ (remediation actions, SLA) │ │ +│ └──────────────────────────────────┘ ┌────────────────────────────┐ │ +│ │ get_session_info │ │ +│ Session state: last_alert, last_threat, │ reset_session │ │ +│ last_response shared across all tools └────────────────────────────┘ │ +└──────────────────────────────────────────────────────────────────────────────┘ +``` + +**Key insight:** Real MSP platforms need **both** tool types coexisting: +- Strict-schema tools drive automation (isolation, blocking, SLA timers) — + wrong data means wrong remediation and a customer breach. +- Natural-language tools help humans understand incidents and communicate + with customers — no schema needed, prose is the point. + +Both share the same session state, so `explain_for_customer` can reference +the `IncidentResponse` that `create_response` just produced. + ## Prerequisites - Azure OpenAI credentials in `mcp/.env` (already configured) @@ -123,6 +160,90 @@ This runs a 3-turn conversation where each follow-up builds on the prior: The remote expert references prior turns because the `mcp-session-id` header ties all requests to the same server-side `AgentSession`. +### 6. Run the Typed-Contract Workflow (standalone — no server needed) + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv run python workflow_typed_contracts.py +``` + +This demonstrates **strict typed data exchange** between agents in an +IT security incident response pipeline — the key advantage of +framework-native orchestration over natural-language protocols like A2A. +Built for an MSP / IT management platform where wrong data at any step +means wrong automated response and a customer breach. + +``` + ┌──────────────┐ SecurityAlert ┌──────────────┐ ThreatAssessment + │ Alert │ ────────────────▶ │ Threat │ ────────────────────▶ + │ Triage │ (Pydantic) │ Intel │ (Pydantic) + └──────────────┘ └──────────────┘ + │ + ┌──────────────┐ IncidentResponse ▼ + │ Response │ ◀──────────────── ┌──────────────┐ ImpactAnalysis + │ Orchestrator│ (Pydantic) │ Impact │ ────────────────────▶ + └──────────────┘ │ Analyzer │ (Pydantic) + │ └──────────────┘ + ▼ + IncidentResponse (drives automated remediation — isolation, blocking, SLA) +``` + +Each arrow represents a **Pydantic-validated contract**: +- `SecurityAlert` — 12 fields, enum-constrained severity & alert source +- `ThreatAssessment` — 11 fields, MITRE ATT&CK vectors, threat score (0-100) +- `ImpactAnalysis` — 11 fields, blast-radius scope enum, endpoint counts +- `IncidentResponse` — 15 fields, ordered remediation actions, SLA deadlines + +If any agent produces output that violates the schema, the pipeline fails +fast with a Pydantic `ValidationError` — no silent corruption downstream. +In IT security, "approximately right" gets customers breached. + +--- + +### 7. Start the Hybrid MCP Server + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv run python mcp_server_hybrid.py +``` + +This starts a **hybrid** MCP server on `http://localhost:8002/mcp` that +exposes **both** strict-schema tools and natural-language tools: + +| Tool | Type | Input → Output | +|------|------|----------------| +| `triage_alert` | Strict schema | raw text → `SecurityAlert` (Pydantic) | +| `assess_threat` | Strict schema | `SecurityAlert` → `ThreatAssessment` (Pydantic) | +| `create_response` | Strict schema | `ThreatAssessment` → `IncidentResponse` (Pydantic) | +| `ask_security_advisor` | Natural language | free-form question → prose answer | +| `explain_for_customer` | Natural language | incident context → plain-English summary | +| `get_session_info` | Utility | — → session metadata | +| `reset_session` | Utility | — → clears session state | + +Session state is shared: strict tools store their outputs (`last_alert`, +`last_threat`, `last_response`) so natural-language tools can reference them. + +### 8. Run the Hybrid Client (in a second terminal) + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv run python mcp_client_hybrid.py +``` + +This runs a 5-step incident flow that uses **both** tool types: + +1. **`triage_alert`** (strict) — parse raw SIEM alert → `SecurityAlert` +2. **`assess_threat`** (strict) — analyze alert → `ThreatAssessment` with MITRE ATT&CK mapping +3. **`create_response`** (strict) — build response plan → `IncidentResponse` with remediation actions +4. **`ask_security_advisor`** (NL) — "What compliance obligations apply?" +5. **`explain_for_customer`** (NL) — draft executive-friendly incident notification + +Strict tools validate with Pydantic (fail fast on bad data). +Natural-language tools produce human-readable prose. +Both share the same MCP session. + +--- + ## How It Works ### Exposing an Agent as MCP Server @@ -228,6 +349,47 @@ async with MCPStreamableHTTPTool( # The expert remembers all prior turns ✓ ``` +### Typed-Contract Workflow (Structured Data Exchange) + +The typed-contract demo shows how `response_format` with Pydantic models +creates strict data boundaries between agents — no natural language needed +at the inter-agent boundary: + +```python +from pydantic import BaseModel, Field +from agent_framework import Agent, AgentResponse + +# Define the CONTRACT between Agent 1 and Agent 2 +class ThreatAssessment(BaseModel): + threat_score: float = Field(ge=0, le=100) + attack_vector: AttackVector # MITRE ATT&CK enum + mitre_techniques: list[str] # e.g. ["T1566.001", "T1059.001"] + confidence_pct: float = Field(ge=0, le=100) + +# Agent 1 produces typed output +response: AgentResponse = await threat_intel_agent.run( + f"Analyze this alert:\n{alert.model_dump_json()}", + options={"response_format": ThreatAssessment}, # ← enforced schema +) +threat = cast(ThreatAssessment, response.value) # ← validated Python object + +# Agent 2 consumes typed input (not prose!) +impact_response = await impact_agent.run( + f"Assess blast radius:\n{threat.model_dump_json()}", + options={"response_format": ImpactAnalysis}, +) +``` + +**Why this matters vs. A2A / natural-language passing:** + +| Aspect | Natural Language (A2A) | Typed Contracts (this demo) | +|--------|----------------------|-----------------------------| +| Data format | Prose: "threat seems critical" | `threat.threat_score = 85.0` (float, 0-100) | +| Validation | None — hope the LLM got it right | Pydantic validates at runtime | +| Downstream action | LLM must re-interpret prose | Direct attribute → API call | +| Failure mode | Silent corruption → wrong playbook | `ValidationError` — fail fast | +| SLA tracking | "Pretty urgent" → which timer? | `severity=CRITICAL` → 15-min SLA | + ## Dependencies | Package | Purpose | @@ -236,4 +398,5 @@ async with MCPStreamableHTTPTool( | `agent-framework-orchestrations` | SequentialBuilder for workflows | | `mcp` | MCP SDK (used by stateless server via `mcp.server.fastmcp.FastMCP`) | | `fastmcp` | PrefectHQ FastMCP v3 (used by stateful server — session state support) | +| `pydantic` | Typed contracts / schema validation (transitive via agent-framework-core) | | `python-dotenv` | Load credentials from `mcp/.env` | diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py b/agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py new file mode 100644 index 000000000..a9d2b121d --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py @@ -0,0 +1,280 @@ +""" +Hybrid MCP Client — demonstrates calling BOTH strict-schema and +natural-language tools from the same MCP server in one incident flow. + +This client simulates a realistic MSP SOC workflow: + + Phase 1 (STRICT SCHEMA): + 1. Feed a raw EDR alert → triage_alert → SecurityAlert (Pydantic) + 2. Pass SecurityAlert → assess_threat → ThreatAssessment (Pydantic) + 3. Pass ThreatAssessment → create_response → IncidentResponse (Pydantic) + + Phase 2 (NATURAL LANGUAGE): + 4. "What's our SLA obligation for this severity?" → ask_security_advisor + 5. "Draft a customer notification" → explain_for_customer + + The point: strict schema for automation (isolation, blocking, SLA timers), + natural language for humans (advisor questions, customer emails). + Same server, same session, both tool types working together. + +Prerequisites: + mcp_server_hybrid.py must be running on http://localhost:8002/mcp + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_client_hybrid.py +""" + +import asyncio +import json +import os +import sys + +from dotenv import load_dotenv + +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, MCPStreamableHTTPTool +from agent_framework.azure import AzureOpenAIChatClient + +# ── The same raw EDR alert used in the typed-contracts demo ───────────────── + +RAW_ALERT = """ +=== EDR ALERT — Managed Endpoint Security === +Timestamp: 2026-02-19T14:23:17Z +Source: Endpoint Detection & Response (EDR Agent v4.2) +Tenant: Contoso Financial Services (tenant: contoso-fin-2024) + +ALERT: Suspicious PowerShell execution detected on CONTOSO-DC01 + +Endpoint: CONTOSO-DC01 (10.42.1.5) — Domain Controller +User Context: CONTOSO\\svc_backup (service account) + +Observed Activity: +- PowerShell process spawned by svc_backup at 14:23:17Z +- Encoded command detected: Base64-encoded Invoke-Mimikatz variant +- LSASS memory access attempted (credential dumping pattern) +- Outbound connection to 185.220.101.42:443 (known C2 infrastructure) +- Kerberos ticket request anomaly: TGT requested for domain admin group +- Shadow copy deletion command queued (vssadmin delete shadows) + +File Hash (SHA-256): a1b2c3d4e5f67890abcdef1234567890abcdef1234567890abcdef1234567890 +Secondary Hash: f0e1d2c3b4a596870fedcba9876543210fedcba9876543210fedcba987654321 +DNS Query: update-service.kfrp[.]xyz (suspicious DGA-like domain) + +Additional Telemetry: +- 3 failed RDP attempts from CONTOSO-WS-047 to CONTOSO-DC01 prior to event +- svc_backup account password last changed 847 days ago +- Similar PowerShell pattern seen on CONTOSO-FS01 (file server) 47 min earlier +- No MFA enforcement on service accounts per tenant policy + +Raw Log: EventID=4688|ProcessName=powershell.exe|CommandLine=-enc +SQBtAHAAbwByAHQALQBNAG8AZAB1AGwAZQAgAC4AXABJAHaA...|ParentProcess=cmd.exe +|User=CONTOSO\\svc_backup|LogonType=3|SourceIP=10.42.3.47 +""" + + +async def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + mcp_url = "http://localhost:8002/mcp" + print("=" * 78) + print("🛡️ HYBRID MCP DEMO — Strict Schema + Natural Language in One Server") + print("=" * 78) + print() + print(" This demo processes a security incident through the SAME MCP server") + print(" using BOTH tool types:") + print() + print(" Phase 1 — STRICT SCHEMA (drives automation):") + print(" triage_alert → assess_threat → create_response") + print(" Output: Pydantic-validated JSON → APIs, SLA timers, tickets") + print() + print(" Phase 2 — NATURAL LANGUAGE (helps humans):") + print(" ask_security_advisor → explain_for_customer") + print(" Output: plain English prose → analysts, customers, executives") + print() + print(f" Connecting to: {mcp_url}") + print() + + async with MCPStreamableHTTPTool( + name="security_ops", + description=( + "Hybrid security operations server with BOTH strict-schema tools " + "(triage_alert, assess_threat, create_response) AND natural-language " + "tools (ask_security_advisor, explain_for_customer). Use strict tools " + "for structured incident data, natural tools for human questions." + ), + url=mcp_url, + ) as mcp_tool: + tool_names = [t.name for t in mcp_tool.functions] + print(f" ✅ Connected — {len(tool_names)} tools: {', '.join(tool_names)}") + print() + + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + async with Agent( + client=client, + name="SOC_Analyst", + instructions=( + "You are a SOC analyst using a hybrid security operations platform.\n\n" + "You have access to TWO TYPES of tools:\n\n" + "STRICT-SCHEMA tools (for automation — always use these for incident data):\n" + " • triage_alert: Pass raw alert text → get SecurityAlert JSON\n" + " • assess_threat: Pass SecurityAlert JSON → get ThreatAssessment JSON\n" + " • create_response: Pass ThreatAssessment JSON + endpoint/account details\n" + " → get IncidentResponse JSON\n\n" + "NATURAL-LANGUAGE tools (for human questions and communication):\n" + " • ask_security_advisor: Ask any security policy/practice question\n" + " • explain_for_customer: Translate technical details to plain English\n\n" + "RULES:\n" + "1. For incident processing, ALWAYS use strict-schema tools in order\n" + "2. Pass the FULL JSON output from one strict tool as input to the next\n" + "3. For questions about policy/practice/SLA, use ask_security_advisor\n" + "4. For customer communication, use explain_for_customer\n" + "5. Present strict-schema results with key fields highlighted\n" + "6. Present natural-language results as-is with brief intro\n" + "7. Do NOT reformulate or summarize the raw alert — pass it directly" + ), + tools=mcp_tool, + ) as analyst: + + # ════════════════════════════════════════════════════════════ + # PHASE 1 — STRICT SCHEMA: Incident processing pipeline + # ════════════════════════════════════════════════════════════ + + print("━" * 78) + print(" PHASE 1 │ STRICT-SCHEMA TOOLS — Incident Processing Pipeline") + print("━" * 78) + + # Step 1: Triage + print() + print(" STEP 1 │ triage_alert: Raw EDR → SecurityAlert (12 typed fields)") + print(" " + "─" * 60) + r1 = await analyst.run( + f"Triage this raw alert using the triage_alert tool. " + f"Pass the ENTIRE raw text:\n\n{RAW_ALERT}" + ) + print(f"\n 🤖 Analyst:\n{_indent(r1.text)}") + + # Step 2: Threat Assessment (pass Step 1 output explicitly) + print() + print(" STEP 2 │ assess_threat: SecurityAlert → ThreatAssessment (11 typed fields)") + print(" " + "─" * 60) + r2 = await analyst.run( + "Now pass this SecurityAlert JSON to assess_threat for " + "threat intelligence analysis. Here is the SecurityAlert " + f"output from the previous step:\n\n{r1.text}" + ) + print(f"\n 🤖 Analyst:\n{_indent(r2.text)}") + + # Step 3: Response Plan (pass Step 2 output explicitly) + print() + print(" STEP 3 │ create_response: ThreatAssessment → IncidentResponse (15 typed fields)") + print(" " + "─" * 60) + r3 = await analyst.run( + "Now create an incident response plan using create_response. " + "Pass this ThreatAssessment JSON along with " + "affected_endpoints='CONTOSO-DC01,CONTOSO-FS01', " + "compromised_accounts='CONTOSO\\\\svc_backup,domain_admin', and " + f"data_at_risk=true.\n\nThreatAssessment from previous step:\n\n{r2.text}" + ) + print(f"\n 🤖 Analyst:\n{_indent(r3.text)}") + + # ════════════════════════════════════════════════════════════ + # PHASE 2 — NATURAL LANGUAGE: Human-facing tools + # ════════════════════════════════════════════════════════════ + + print() + print("━" * 78) + print(" PHASE 2 │ NATURAL-LANGUAGE TOOLS — Human-Facing Communication") + print("━" * 78) + + # Step 4: Ask advisor + print() + print(" STEP 4 │ ask_security_advisor: Policy/SLA question (free-form answer)") + print(" " + "─" * 60) + r4 = await analyst.run( + "Ask the security advisor: Given this incident involves credential " + "theft on a domain controller for a financial services tenant, " + "what are our SOC 2 and SOX compliance notification obligations? " + "What's the timeline?\n\n" + f"Incident context from prior steps:\n{r3.text}" + ) + print(f"\n 🤖 Analyst:\n{_indent(r4.text)}") + + # Step 5: Customer explanation + print() + print(" STEP 5 │ explain_for_customer: Plain-English customer notification") + print(" " + "─" * 60) + r5 = await analyst.run( + "Now use explain_for_customer to draft a notification for the " + "Contoso Financial Services executive team (audience: 'executive'). " + "Summarize the incident and what we're doing about it — no jargon.\n\n" + f"Full incident context:\n{r3.text}" + ) + print(f"\n 🤖 Analyst:\n{_indent(r5.text)}") + + # ════════════════════════════════════════════════════════════ + # SUMMARY + # ════════════════════════════════════════════════════════════ + + print() + print("=" * 78) + print("📊 DEMO SUMMARY — Same server, two tool types, one incident") + print("=" * 78) + print(""" + ┌────────────────────────────────────────────────────────────────────────┐ + │ STRICT-SCHEMA TOOLS │ NATURAL-LANGUAGE TOOLS │ + │ (Pydantic-validated JSON) │ (Free-form prose) │ + ├────────────────────────────────────────────────────────────────────────┤ + │ │ │ + │ triage_alert │ ask_security_advisor │ + │ → SecurityAlert (12 fields) │ → "Our SOC 2 obligations │ + │ → severity=HIGH (not "bad") │ require notification within │ + │ → Starts 30-min SLA timer │ 24 hours..." │ + │ │ → Human reads, understands, │ + │ assess_threat │ makes decision │ + │ → ThreatAssessment (11 flds) │ │ + │ → attack_vector=CRED_THEFT │ explain_for_customer │ + │ → Selects Mimikatz playbook │ → "We detected unauthorized │ + │ │ access to your network..." │ + │ create_response │ → CFO reads this, not the JSON │ + │ → IncidentResponse (15 flds) │ │ + │ → isolate_endpoint API call │ │ + │ → disable_account API call │ │ + │ → block_ip firewall rule │ │ + │ │ │ + │ WHO READS THIS: machines │ WHO READS THIS: humans │ + │ → APIs, automation, tickets │ → analysts, customers, executives │ + └────────────────────────────────────────────────────────────────────────┘ + + Both tool types coexist in ONE MCP server, ONE session, ONE incident. + The right tool for the right job. +""") + print("=" * 78) + print("✅ Hybrid demo complete — strict schemas for machines, prose for people.") + print("=" * 78) + + print() + + +def _indent(text: str, prefix: str = " ") -> str: + """Indent each line of text for nicer console output.""" + return "\n".join(f"{prefix}{line}" for line in text.split("\n")) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py b/agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py new file mode 100644 index 000000000..8e691c76b --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py @@ -0,0 +1,605 @@ +""" +Hybrid MCP Server — Strict-Schema + Natural-Language tools in one service. + +This MCP server demonstrates a realistic MSP / IT security platform that +exposes BOTH kinds of tools through a single endpoint: + + STRICT-SCHEMA tools (Pydantic-validated, drive automation): + • triage_alert — raw alert text → SecurityAlert (12 typed fields) + • assess_threat — SecurityAlert → ThreatAssessment (11 typed fields) + • create_response — ThreatAssessment + context → IncidentResponse (15 typed fields) + + NATURAL-LANGUAGE tools (conversational, for human consumption): + • ask_security_advisor — free-form security questions, best practices, policy + • explain_for_customer — translate a technical incident into plain English + for a non-technical customer/executive + +WHY this matters: + Strict-schema tools MUST return exact enums, scores, and lists because their + output feeds automated systems (endpoint isolation APIs, SLA timers, ticket + creation, compliance notifications). Getting severity wrong = wrong SLA = + contractual breach. + + Natural-language tools are fine with prose because a HUMAN reads the output. + "What's our patching policy?" doesn't need a Pydantic model — it needs a + clear, helpful paragraph. + + A real MSP platform needs BOTH. This demo shows them coexisting in one + MCP server, with the client agent choosing the right tool for each task. + +Architecture: + ┌─────────────────────────────────────────────────────────┐ + │ FastMCP (stateful, streamable-http, port 8002) │ + │ │ + │ ┌─ STRICT SCHEMA ──────────────────────────────────┐ │ + │ │ triage_alert → SecurityAlert (Pydantic) │ │ + │ │ assess_threat → ThreatAssessment (Pydantic) │ │ + │ │ create_response → IncidentResponse (Pydantic) │ │ + │ └──────────────────────────────────────────────────┘ │ + │ │ + │ ┌─ NATURAL LANGUAGE ───────────────────────────────┐ │ + │ │ ask_security_advisor → free-form text │ │ + │ │ explain_for_customer → plain-English narrative │ │ + │ └──────────────────────────────────────────────────┘ │ + │ │ + │ MCP session_id → AgentSession (history preserved) │ + └─────────────────────────────────────────────────────────┘ + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_server_hybrid.py + + # Then in another terminal: + uv run python mcp_client_hybrid.py +""" + +import json +import os +import sys +from datetime import datetime, timezone +from enum import Enum +from typing import Annotated, cast + +from dotenv import load_dotenv +from pydantic import BaseModel, Field + +# ── Load credentials ──────────────────────────────────────────────────────── +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, AgentResponse +from agent_framework._sessions import AgentSession +from agent_framework.azure import AzureOpenAIChatClient +from fastmcp import FastMCP +from fastmcp.server.context import Context + + +# ═══════════════════════════════════════════════════════════════════════════ +# PYDANTIC CONTRACTS — strict schemas for automation-critical tools +# ═══════════════════════════════════════════════════════════════════════════ + + +class AlertSource(str, Enum): + EDR = "edr" + SIEM = "siem" + NETWORK_IDS = "network_ids" + EMAIL_GATEWAY = "email_gateway" + VULNERABILITY_SCANNER = "vulnerability_scanner" + USER_REPORT = "user_report" + + +class Severity(str, Enum): + CRITICAL = "critical" # SLA: 15 min response, 1 hr containment + HIGH = "high" # SLA: 30 min response, 4 hr containment + MEDIUM = "medium" # SLA: 2 hr response, 24 hr containment + LOW = "low" # SLA: 8 hr response, 72 hr containment + INFORMATIONAL = "informational" + + +class AttackVector(str, Enum): + RANSOMWARE = "ransomware" + PHISHING = "phishing" + LATERAL_MOVEMENT = "lateral_movement" + CREDENTIAL_THEFT = "credential_theft" + MALWARE_EXECUTION = "malware_execution" + DATA_EXFILTRATION = "data_exfiltration" + PRIVILEGE_ESCALATION = "privilege_escalation" + COMMAND_AND_CONTROL = "command_and_control" + SUPPLY_CHAIN = "supply_chain" + UNKNOWN = "unknown" + + +class ThreatCategory(str, Enum): + APT = "apt" + COMMODITY_MALWARE = "commodity_malware" + INSIDER_THREAT = "insider_threat" + OPPORTUNISTIC_ATTACK = "opportunistic_attack" + TARGETED_ATTACK = "targeted_attack" + FALSE_POSITIVE = "false_positive" + + +class RemediationAction(str, Enum): + ISOLATE_ENDPOINT = "isolate_endpoint" + DISABLE_ACCOUNT = "disable_account" + BLOCK_IP = "block_ip" + BLOCK_HASH = "block_hash" + FORCE_PASSWORD_RESET = "force_password_reset" + REVOKE_SESSIONS = "revoke_sessions" + QUARANTINE_EMAIL = "quarantine_email" + DEPLOY_PATCH = "deploy_patch" + RESTORE_FROM_BACKUP = "restore_from_backup" + ESCALATE_TO_HUMAN = "escalate_to_human" + + +class ResponsePriority(str, Enum): + P1 = "P1" + P2 = "P2" + P3 = "P3" + P4 = "P4" + + +class ImpactScope(str, Enum): + SINGLE_ENDPOINT = "single_endpoint" + MULTIPLE_ENDPOINTS = "multiple_endpoints" + SUBNET = "subnet" + SITE = "site" + TENANT_WIDE = "tenant_wide" + CROSS_TENANT = "cross_tenant" + + +# ── Contract: triage_alert output ─────────────────────────────────────────── + +class SecurityAlert(BaseModel): + """Structured alert — every field drives downstream automation.""" + alert_id: str = Field(description="Unique alert ID (e.g. ALT-2026-00847)") + timestamp: str = Field(description="ISO 8601 timestamp of first detection") + source: AlertSource = Field(description="System that generated the alert") + severity: Severity = Field(description="Assessed severity level") + title: str = Field(description="One-line alert title") + description: str = Field(description="Concise description of observed activity") + affected_hostname: str = Field(description="Primary affected endpoint hostname") + affected_ip: str = Field(description="IP address of affected endpoint") + affected_user: str = Field(description="Username associated with the activity") + tenant_id: str = Field(description="MSP customer/tenant identifier") + indicators_of_compromise: list[str] = Field( + description="IOCs: file hashes, IPs, domains, registry keys" + ) + raw_log_snippet: str = Field(description="Relevant raw log excerpt (first 500 chars)") + + +# ── Contract: assess_threat output ────────────────────────────────────────── + +class ThreatAssessment(BaseModel): + """Enriched threat intelligence with confidence scores.""" + alert_id: str = Field(description="Alert ID for traceability") + threat_category: ThreatCategory = Field(description="Classified threat category") + attack_vector: AttackVector = Field(description="MITRE ATT&CK vector") + threat_score: float = Field(ge=0, le=100, description="Threat score 0-100") + known_threat_actor: str | None = Field( + description="Known actor/group name, or null" + ) + mitre_techniques: list[str] = Field( + description="MITRE ATT&CK technique IDs (e.g. T1003.001)" + ) + ioc_matches: list[str] = Field( + description="IOCs that matched threat intel feeds" + ) + confidence_pct: float = Field(ge=0, le=100, description="Confidence 0-100%") + is_known_campaign: bool = Field(description="True if matches known campaign") + recommended_severity_override: Severity | None = Field( + description="Override severity if intel warrants it, null to keep" + ) + intel_summary: str = Field(description="Brief threat intel narrative") + + +# ── Contract: create_response output ──────────────────────────────────────── + +class IncidentResponse(BaseModel): + """Automated response plan — drives real-world remediation APIs.""" + incident_id: str = Field(description="Generated incident ID (e.g. INC-2026-00412)") + alert_id: str = Field(description="Originating alert ID") + priority: ResponsePriority = Field(description="Response priority P1-P4") + final_severity: Severity = Field(description="Final severity after all analysis") + immediate_actions: list[RemediationAction] = Field( + description="Ordered remediation actions to execute NOW" + ) + endpoints_to_isolate: list[str] = Field(description="Hostnames to isolate") + accounts_to_disable: list[str] = Field(description="Accounts to disable/lock") + ips_to_block: list[str] = Field(description="IPs for firewall blocklist") + hashes_to_block: list[str] = Field(description="File hashes to block fleet-wide") + notification_list: list[str] = Field( + description="Roles/teams to notify (e.g. soc_lead, tenant_admin, ciso)" + ) + customer_communication_required: bool = Field( + description="True if MSP must notify customer per SLA" + ) + compliance_flags: list[str] = Field( + description="Regulatory flags (e.g. GDPR_72hr, SOX_incident)" + ) + sla_response_deadline: str = Field(description="ISO 8601 response deadline") + sla_containment_deadline: str = Field(description="ISO 8601 containment deadline") + decision_rationale: str = Field(description="Brief explanation of response decisions") + + +# ═══════════════════════════════════════════════════════════════════════════ +# SERVER +# ═══════════════════════════════════════════════════════════════════════════ + + +def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + print(f"🔧 Azure OpenAI endpoint : {endpoint}") + print(f"🔧 Deployment : {deployment}") + + llm_client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + now_iso = datetime.now(timezone.utc).isoformat() + + # ── Internal agents (not exposed directly — called by MCP tools) ──── + + triage_agent = Agent( + client=llm_client, + name="AlertTriageAgent", + instructions=( + "You are a Tier-1 SOC analyst performing initial alert triage for a " + "managed security services provider (MSP). Extract structured data " + "from raw SIEM/EDR alerts.\n" + "- Assign severity: critical=active ransomware/data exfil, " + "high=credential theft/lateral movement, medium=suspicious behavior, " + "low=policy violation, informational=benign anomaly\n" + "- Extract ALL IOCs: SHA-256 hashes, IPs, domains, registry keys\n" + "- alert_id format: ALT-YYYY-NNNNN\n" + f"- Current UTC time: {now_iso}" + ), + ) + + threat_agent = Agent( + client=llm_client, + name="ThreatIntelAgent", + instructions=( + "You are a threat intelligence analyst. Enrich alerts with MITRE ATT&CK " + "classification and threat intel.\n" + "- threat_score: base 50, +20 known campaign, +15 credential theft, " + "+10 per IOC match, -20 likely false positive\n" + "- confidence_pct: 90%+ only with multiple IOC matches\n" + "- Override severity ONLY if intel clearly contradicts triage" + ), + ) + + response_agent = Agent( + client=llm_client, + name="ResponseOrchestrator", + instructions=( + "You are the automated incident response engine for an MSP platform.\n" + "SLA matrix: CRITICAL=P1 (15min/1hr), HIGH=P2 (30min/4hr), " + "MEDIUM=P3 (2hr/24hr), LOW=P4 (8hr/72hr)\n" + "- Ransomware/data_exfil → isolate + disable accounts\n" + "- Credential theft → force password reset + revoke sessions\n" + "- Lateral movement → isolate ALL affected + at-risk endpoints\n" + "- If data_at_risk AND regulated → add compliance flags\n" + "- Customer communication required for P1 and P2\n" + "- incident_id format: INC-YYYY-NNNNN\n" + f"- Current UTC time: {now_iso}" + ), + ) + + # Natural-language advisor agent (no strict output — free-form helpful answers) + advisor_agent = Agent( + client=llm_client, + name="SecurityAdvisor", + instructions=( + "You are a senior security advisor for a managed IT platform serving " + "MSP customers. You answer security questions in clear, practical language.\n\n" + "Topics you cover:\n" + "- Security best practices (patching, MFA, zero trust, EDR config)\n" + "- Compliance guidance (SOC 2, HIPAA, GDPR, SOX, NIST CSF)\n" + "- Incident response procedures and playbook recommendations\n" + "- Risk assessment methodology and prioritization\n" + "- Vendor security evaluation\n" + "- Security architecture review\n\n" + "Style: concise, practical, actionable. Cite frameworks where relevant. " + "You are NOT producing structured data for automation — you are helping " + "humans understand security concepts and make informed decisions." + ), + ) + + # Customer-facing explanation agent + explainer_agent = Agent( + client=llm_client, + name="CustomerExplainer", + instructions=( + "You translate technical security incidents into clear, non-technical " + "language for MSP customers, executives, and board members.\n\n" + "Rules:\n" + "- NO jargon: replace 'lateral movement' with 'the attacker moved between computers'\n" + "- NO acronyms without explanation: 'EDR (endpoint protection software)'\n" + "- Include: what happened, what's at risk, what we're doing about it\n" + "- Tone: professional, reassuring, transparent — not alarmist\n" + "- Mention specific actions being taken to protect the customer\n" + "- Keep it under 200 words unless asked for more detail\n\n" + "Remember: the reader may be a CFO or office manager, not an IT person." + ), + ) + + # ── Session stores ────────────────────────────────────────────────── + agent_sessions: dict[str, AgentSession] = {} + session_turns: dict[str, int] = {} + # Store typed results per session for cross-tool reference + session_data: dict[str, dict] = {} + + # ── FastMCP server ────────────────────────────────────────────────── + mcp_server = FastMCP("HybridSecurityServer") + + # ════════════════════════════════════════════════════════════════════ + # STRICT-SCHEMA TOOLS — output is Pydantic-validated JSON + # These drive automation: isolation APIs, SLA timers, ticket creation + # ════════════════════════════════════════════════════════════════════ + + @mcp_server.tool + async def triage_alert( + raw_alert: Annotated[str, "Raw alert text from SIEM/EDR/IDS to triage"], + ctx: Context, + ) -> str: + """[STRICT SCHEMA] Triage a raw security alert into a structured + SecurityAlert with 12 typed fields (severity enum, IOC list, etc.). + Output drives SLA timers and downstream automation — must be exact.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — triaging alert (strict schema)") + + _ensure_session(session_id, agent_sessions, session_turns, session_data) + session_turns[session_id] += 1 + + async with triage_agent: + response: AgentResponse = await triage_agent.run( + f"Triage this security alert and extract structured data:\n\n{raw_alert}", + options={"response_format": SecurityAlert}, + ) + alert = cast(SecurityAlert, response.value) + + # Store for cross-tool reference + session_data[session_id]["last_alert"] = alert.model_dump(mode="json") + session_data[session_id]["last_alert_model"] = alert + + result = alert.model_dump(mode="json") + await ctx.info( + f"✅ SecurityAlert validated: severity={alert.severity.value}, " + f"host={alert.affected_hostname}, {len(alert.indicators_of_compromise)} IOCs" + ) + return json.dumps(result, indent=2) + + @mcp_server.tool + async def assess_threat( + alert_json: Annotated[str, "SecurityAlert JSON from triage_alert output"], + ctx: Context, + ) -> str: + """[STRICT SCHEMA] Analyze a triaged SecurityAlert with threat intelligence. + Returns ThreatAssessment with 11 typed fields (MITRE ATT&CK vectors, + threat score 0-100, confidence %). Drives playbook selection.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — assessing threat (strict schema)") + + _ensure_session(session_id, agent_sessions, session_turns, session_data) + session_turns[session_id] += 1 + + async with threat_agent: + response: AgentResponse = await threat_agent.run( + f"Analyze this security alert with threat intelligence:\n\n{alert_json}", + options={"response_format": ThreatAssessment}, + ) + threat = cast(ThreatAssessment, response.value) + + session_data[session_id]["last_threat"] = threat.model_dump(mode="json") + session_data[session_id]["last_threat_model"] = threat + + result = threat.model_dump(mode="json") + await ctx.info( + f"✅ ThreatAssessment validated: vector={threat.attack_vector.value}, " + f"score={threat.threat_score:.0f}/100, confidence={threat.confidence_pct:.0f}%" + ) + return json.dumps(result, indent=2) + + @mcp_server.tool + async def create_response( + threat_json: Annotated[str, "ThreatAssessment JSON from assess_threat output"], + affected_endpoints: Annotated[str, "Comma-separated hostnames of affected endpoints"], + compromised_accounts: Annotated[str, "Comma-separated compromised user accounts"], + data_at_risk: Annotated[bool, "Whether sensitive/regulated data may be exposed"], + ctx: Context, + ) -> str: + """[STRICT SCHEMA] Generate an automated incident response plan. + Returns IncidentResponse with 15 typed fields including ordered + remediation actions, SLA deadlines, and compliance flags. + This output DIRECTLY drives endpoint isolation and account lockout APIs.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — creating response plan (strict schema)") + + _ensure_session(session_id, agent_sessions, session_turns, session_data) + session_turns[session_id] += 1 + + endpoints_list = [e.strip() for e in affected_endpoints.split(",")] + accounts_list = [a.strip() for a in compromised_accounts.split(",")] + + prompt = ( + "Generate the incident response plan with specific automated actions.\n\n" + f"Threat assessment:\n{threat_json}\n\n" + f"Affected endpoints: {json.dumps(endpoints_list)}\n" + f"Compromised accounts: {json.dumps(accounts_list)}\n" + f"Data at risk: {data_at_risk}\n" + ) + + # Pull in alert context if available + alert_data = session_data[session_id].get("last_alert") + if alert_data: + prompt += f"\nOriginal alert context: severity={alert_data.get('severity')}, tenant={alert_data.get('tenant_id')}\n" + + async with response_agent: + response: AgentResponse = await response_agent.run( + prompt, + options={"response_format": IncidentResponse}, + ) + incident = cast(IncidentResponse, response.value) + + session_data[session_id]["last_response"] = incident.model_dump(mode="json") + session_data[session_id]["last_response_model"] = incident + + result = incident.model_dump(mode="json") + await ctx.info( + f"✅ IncidentResponse validated: {incident.priority.value}, " + f"{len(incident.immediate_actions)} actions, " + f"isolate={incident.endpoints_to_isolate}" + ) + return json.dumps(result, indent=2) + + # ════════════════════════════════════════════════════════════════════ + # NATURAL-LANGUAGE TOOLS — output is free-form text for humans + # No Pydantic validation — prose answers for questions/explanations + # ════════════════════════════════════════════════════════════════════ + + @mcp_server.tool + async def ask_security_advisor( + question: Annotated[str, "Any security question — policy, best practices, compliance, architecture"], + ctx: Context, + ) -> str: + """[NATURAL LANGUAGE] Ask a senior security advisor any question. + Returns a free-form, practical answer. Good for: security policy, + compliance guidance, best practices, risk methodology, architecture. + The advisor remembers conversation context within the session.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — consulting advisor (natural language)") + + _ensure_session(session_id, agent_sessions, session_turns, session_data) + session_turns[session_id] += 1 + + session = agent_sessions[session_id] + + # Add incident context if we have it + context_note = "" + if session_data[session_id].get("last_alert"): + alert = session_data[session_id]["last_alert"] + context_note = ( + f"\n\n[Context: there is an active incident — alert {alert.get('alert_id')}, " + f"severity {alert.get('severity')}, affecting {alert.get('affected_hostname')} " + f"at tenant {alert.get('tenant_id')}. Reference this if the question relates to it.]" + ) + + async with advisor_agent: + response = await advisor_agent.run( + question + context_note, + session=session, + ) + + return response.text + + @mcp_server.tool + async def explain_for_customer( + incident_context: Annotated[str, "Technical incident details or JSON to translate"], + audience: Annotated[str, "Who will read this: 'executive', 'office_manager', 'board', or 'technical_lead'"] = "executive", + ctx: Context = None, + ) -> str: + """[NATURAL LANGUAGE] Translate a technical security incident into + plain English for a non-technical audience. Outputs a clear, jargon-free + explanation suitable for customer communication. Specify the audience + to adjust the tone and detail level.""" + session_id = ctx.session_id if ctx else "default" + if ctx: + await ctx.info(f"Session {session_id[:8]}… — drafting customer explanation (natural language)") + + _ensure_session(session_id, agent_sessions, session_turns, session_data) + session_turns[session_id] += 1 + + # Build context from session data + full_context = f"Audience: {audience}\n\nTechnical details:\n{incident_context}" + + # Enrich with any stored incident data + stored = session_data[session_id] + if stored.get("last_response"): + resp = stored["last_response"] + full_context += ( + f"\n\nIncident response plan:\n" + f"- Priority: {resp.get('priority')}\n" + f"- Severity: {resp.get('final_severity')}\n" + f"- Actions taken: {json.dumps(resp.get('immediate_actions', []))}\n" + f"- Customer communication required: {resp.get('customer_communication_required')}\n" + f"- Compliance flags: {json.dumps(resp.get('compliance_flags', []))}\n" + ) + + async with explainer_agent: + response = await explainer_agent.run(full_context) + + return response.text + + # ── Utility tools ─────────────────────────────────────────────────── + + @mcp_server.tool + async def get_session_info(ctx: Context) -> dict: + """Get metadata about the current session — turn count, available data.""" + session_id = ctx.session_id + stored = session_data.get(session_id, {}) + return { + "session_id": session_id, + "turn_count": session_turns.get(session_id, 0), + "has_alert": "last_alert" in stored, + "has_threat_assessment": "last_threat" in stored, + "has_response_plan": "last_response" in stored, + "alert_severity": stored.get("last_alert", {}).get("severity"), + "incident_priority": stored.get("last_response", {}).get("priority"), + } + + @mcp_server.tool + async def reset_session(ctx: Context) -> str: + """Clear all data and conversation history for the current session.""" + session_id = ctx.session_id + agent_sessions.pop(session_id, None) + session_turns.pop(session_id, None) + session_data.pop(session_id, None) + return f"Session {session_id[:8]}… cleared — all data and history reset." + + # ── Launch ────────────────────────────────────────────────────────── + + print("──────────────────────────────────────────────────────────────") + print("🛡️ Hybrid MCP Security Server") + print(" URL: http://localhost:8002/mcp") + print() + print(" STRICT-SCHEMA tools (automation-critical):") + print(" • triage_alert → SecurityAlert (12 fields)") + print(" • assess_threat → ThreatAssessment (11 fields)") + print(" • create_response → IncidentResponse (15 fields)") + print() + print(" NATURAL-LANGUAGE tools (human-facing):") + print(" • ask_security_advisor → free-form advisory") + print(" • explain_for_customer → plain-English translation") + print() + print(" Utility: get_session_info, reset_session") + print("──────────────────────────────────────────────────────────────") + + mcp_server.run(transport="streamable-http", host="0.0.0.0", port=8002) + + +def _ensure_session( + session_id: str, + sessions: dict[str, AgentSession], + turns: dict[str, int], + data: dict[str, dict], +) -> None: + """Lazily initialize session stores.""" + if session_id not in sessions: + sessions[session_id] = AgentSession() + turns[session_id] = 0 + data[session_id] = {} + + +if __name__ == "__main__": + main() diff --git a/agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py b/agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py new file mode 100644 index 000000000..9217e2654 --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py @@ -0,0 +1,621 @@ +""" +Part 4 — Typed-Contract Multi-Agent Workflow (Structured Data Exchange) + +Demonstrates WHY strict typed data exchange between agents matters in +business-critical multi-agent orchestrations. This security incident +response pipeline — built for an IT management / MSP platform — has +Pydantic-enforced INPUT and OUTPUT contracts at every agent boundary. +If any agent's output violates the schema the pipeline fails fast — +no silent corruption, no LLM "interpretation" of malformed data. + +Pipeline (IT Security Incident Response): + + ┌──────────────┐ SecurityAlert ┌──────────────┐ ThreatAssessment + │ Alert │ ────────────────▶ │ Threat │ ────────────────────▶ + │ Triage │ (Pydantic) │ Intel │ (Pydantic) + └──────────────┘ └──────────────┘ + │ + ┌──────────────┐ IncidentResponse ▼ + │ Response │ ◀──────────────── ┌──────────────┐ ImpactAnalysis + │ Orchestrator│ (Pydantic) │ Impact │ ────────────────────▶ + └──────────────┘ │ Analyzer │ (Pydantic) + │ └──────────────┘ + ▼ + IncidentResponse (Pydantic — drives automated remediation) + +Why typed contracts matter in IT security: + - Wrong severity enum → wrong SLA timer → breached customer SLA + - Misclassified attack vector → wrong containment playbook executed + - Ambiguous "some endpoints affected" → automated isolation skips hosts + - "Risk seems high" vs risk_score=87 → completely different escalation path + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python workflow_typed_contracts.py +""" + +import asyncio +import json +import os +import sys +from datetime import datetime, timezone +from enum import Enum +from typing import cast + +from dotenv import load_dotenv +from pydantic import BaseModel, Field + +# ── Load credentials ──────────────────────────────────────────────────────── +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + + +# ═══════════════════════════════════════════════════════════════════════════ +# CONTRACTS — Pydantic models that form the typed data-exchange boundary +# between agents. Each model is both a SCHEMA (for the LLM) and a +# VALIDATOR (for the runtime). +# +# In an MSP / IT management platform, these contracts drive automation: +# - Automated ticket creation (needs exact severity + category) +# - SLA timers (wrong severity = wrong SLA = contractual breach) +# - Playbook selection (wrong attack_vector = wrong containment) +# - Customer notifications (requires structured, auditable data) +# ═══════════════════════════════════════════════════════════════════════════ + + +class AlertSource(str, Enum): + """Where the raw alert originated.""" + EDR = "edr" # Endpoint Detection & Response + SIEM = "siem" # Security Information & Event Management + NETWORK_IDS = "network_ids" # Network Intrusion Detection System + EMAIL_GATEWAY = "email_gateway" + VULNERABILITY_SCANNER = "vulnerability_scanner" + USER_REPORT = "user_report" + + +class Severity(str, Enum): + """NIST-aligned severity levels — drives SLA timers.""" + CRITICAL = "critical" # SLA: 15 min response, 1 hr containment + HIGH = "high" # SLA: 30 min response, 4 hr containment + MEDIUM = "medium" # SLA: 2 hr response, 24 hr containment + LOW = "low" # SLA: 8 hr response, 72 hr containment + INFORMATIONAL = "informational" + + +class AttackVector(str, Enum): + """MITRE ATT&CK-aligned attack vector classification.""" + RANSOMWARE = "ransomware" + PHISHING = "phishing" + LATERAL_MOVEMENT = "lateral_movement" + CREDENTIAL_THEFT = "credential_theft" + MALWARE_EXECUTION = "malware_execution" + DATA_EXFILTRATION = "data_exfiltration" + PRIVILEGE_ESCALATION = "privilege_escalation" + COMMAND_AND_CONTROL = "command_and_control" + SUPPLY_CHAIN = "supply_chain" + UNKNOWN = "unknown" + + +class SecurityAlert(BaseModel): + """Contract: Alert Triage Agent → Threat Intel Agent. + + Structured extraction from raw SIEM/EDR noise into actionable alert. + Every field drives downstream automation — no ambiguity allowed. + """ + alert_id: str = Field(description="Unique alert identifier (e.g. ALT-2026-00847)") + timestamp: str = Field(description="ISO 8601 timestamp of first detection") + source: AlertSource = Field(description="System that generated the alert") + severity: Severity = Field(description="Triage-assessed severity level") + title: str = Field(description="One-line alert title") + description: str = Field(description="Concise description of observed activity") + affected_hostname: str = Field(description="Primary affected endpoint hostname") + affected_ip: str = Field(description="IP address of affected endpoint") + affected_user: str = Field(description="Username associated with the activity") + tenant_id: str = Field(description="MSP customer/tenant identifier") + indicators_of_compromise: list[str] = Field( + description="List of IOCs: file hashes, IPs, domains, registry keys" + ) + raw_log_snippet: str = Field(description="Relevant log excerpt (first 500 chars)") + + +class ThreatCategory(str, Enum): + """Threat categorization — determines which playbook runs.""" + APT = "apt" # Advanced Persistent Threat + COMMODITY_MALWARE = "commodity_malware" + INSIDER_THREAT = "insider_threat" + OPPORTUNISTIC_ATTACK = "opportunistic_attack" + TARGETED_ATTACK = "targeted_attack" + FALSE_POSITIVE = "false_positive" + + +class ThreatAssessment(BaseModel): + """Contract: Threat Intel Agent → Impact Analyzer. + + Enriched intelligence assessment with confidence scores. + threat_score drives escalation; attack_vector selects containment playbook. + """ + alert_id: str = Field(description="Alert ID (pass-through for traceability)") + threat_category: ThreatCategory = Field(description="Classified threat category") + attack_vector: AttackVector = Field(description="MITRE ATT&CK vector classification") + threat_score: float = Field( + ge=0, le=100, description="Threat confidence score 0-100 (higher = more dangerous)" + ) + known_threat_actor: str | None = Field( + description="Known threat actor/group name, or null if unknown" + ) + mitre_techniques: list[str] = Field( + description="MITRE ATT&CK technique IDs (e.g. T1566.001, T1059.001)" + ) + ioc_matches: list[str] = Field( + description="IOCs that matched known threat intelligence feeds" + ) + confidence_pct: float = Field( + ge=0, le=100, description="Analyst confidence in classification (0-100%)" + ) + is_known_campaign: bool = Field( + description="True if IOCs match a known active campaign" + ) + recommended_severity_override: Severity | None = Field( + description="Override triage severity if intel warrants it, null to keep original" + ) + intel_summary: str = Field(description="Brief threat intelligence narrative") + + +class ImpactScope(str, Enum): + """Blast radius classification — drives isolation scope.""" + SINGLE_ENDPOINT = "single_endpoint" + MULTIPLE_ENDPOINTS = "multiple_endpoints" + SUBNET = "subnet" + SITE = "site" + TENANT_WIDE = "tenant_wide" + CROSS_TENANT = "cross_tenant" # MSP nightmare scenario + + +class ImpactAnalysis(BaseModel): + """Contract: Impact Analyzer → Response Orchestrator. + + Precise blast-radius assessment across the managed endpoint fleet. + Numbers here drive automated isolation — wrong count = missed endpoints. + """ + alert_id: str = Field(description="Alert ID (pass-through for traceability)") + impact_scope: ImpactScope = Field(description="Blast radius classification") + affected_endpoint_count: int = Field( + ge=0, description="Number of confirmed affected endpoints" + ) + at_risk_endpoint_count: int = Field( + ge=0, description="Number of endpoints at risk but not yet confirmed compromised" + ) + affected_hostnames: list[str] = Field(description="List of confirmed affected hostnames") + compromised_accounts: list[str] = Field( + description="User accounts confirmed or suspected compromised" + ) + data_at_risk: bool = Field( + description="True if sensitive/regulated data may be exposed" + ) + business_services_affected: list[str] = Field( + description="Business services/applications impacted (e.g. 'Exchange', 'ERP', 'File Server')" + ) + lateral_movement_detected: bool = Field( + description="True if evidence of lateral movement across hosts" + ) + estimated_dwell_time_hours: float = Field( + ge=0, description="Estimated hours the threat has been present" + ) + impact_summary: str = Field(description="Brief impact assessment narrative") + + +class RemediationAction(str, Enum): + """Specific automated actions the response system can execute.""" + ISOLATE_ENDPOINT = "isolate_endpoint" + DISABLE_ACCOUNT = "disable_account" + BLOCK_IP = "block_ip" + BLOCK_HASH = "block_hash" + FORCE_PASSWORD_RESET = "force_password_reset" + REVOKE_SESSIONS = "revoke_sessions" + QUARANTINE_EMAIL = "quarantine_email" + DEPLOY_PATCH = "deploy_patch" + RESTORE_FROM_BACKUP = "restore_from_backup" + ESCALATE_TO_HUMAN = "escalate_to_human" + + +class ResponsePriority(str, Enum): + """Incident response priority — maps to SLA commitments.""" + P1 = "P1" # Immediate automated action + SOC notification + P2 = "P2" # Automated action within 15 min + analyst review + P3 = "P3" # Queued for analyst review, auto-action optional + P4 = "P4" # Logged, no immediate action required + + +class IncidentResponse(BaseModel): + """Contract: Response Orchestrator → Automation Engine / Ticket System. + + This is the OUTPUT that drives real-world automated remediation. + Every field maps directly to an API call or ticket field. + Wrong data here = wrong containment = breach. + """ + incident_id: str = Field(description="Generated incident ID (e.g. INC-2026-00412)") + alert_id: str = Field(description="Originating alert ID (traceability)") + priority: ResponsePriority = Field(description="Response priority (P1-P4)") + final_severity: Severity = Field(description="Final assessed severity after all analysis") + immediate_actions: list[RemediationAction] = Field( + description="Ordered list of automated remediation actions to execute NOW" + ) + endpoints_to_isolate: list[str] = Field( + description="Specific hostnames to network-isolate" + ) + accounts_to_disable: list[str] = Field( + description="Specific accounts to disable/lock" + ) + ips_to_block: list[str] = Field(description="IPs to add to firewall blocklist") + hashes_to_block: list[str] = Field(description="File hashes to block across fleet") + notification_list: list[str] = Field( + description="Roles/teams to notify (e.g. 'soc_lead', 'tenant_admin', 'ciso')" + ) + customer_communication_required: bool = Field( + description="True if MSP must notify the customer per SLA" + ) + compliance_flags: list[str] = Field( + description="Regulatory notifications needed (e.g. 'GDPR_72hr', 'HIPAA_breach', 'SOC2_incident')" + ) + sla_response_deadline: str = Field( + description="ISO 8601 deadline for initial response per SLA" + ) + sla_containment_deadline: str = Field( + description="ISO 8601 deadline for containment per SLA" + ) + decision_rationale: str = Field(description="Brief explanation of response decisions") + + +# ═══════════════════════════════════════════════════════════════════════════ +# PIPELINE — sequential agent chain with typed contracts at each boundary +# ═══════════════════════════════════════════════════════════════════════════ + + +async def main() -> None: + from agent_framework import Agent, AgentResponse + from agent_framework.azure import AzureOpenAIChatClient + + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + now = datetime.now(timezone.utc) + + # ── Agent 1: Alert Triage ─────────────────────────────────────────── + triage_agent = Agent( + client=client, + name="AlertTriageAgent", + instructions=( + "You are a Tier-1 SOC analyst performing initial alert triage for a " + "managed security services provider (MSP). You receive raw SIEM/EDR " + "alerts and extract structured, actionable security alert data.\n\n" + "Rules:\n" + "- Assign severity based on: critical=active ransomware/data exfil, " + "high=credential theft/lateral movement, medium=suspicious behavior, " + "low=policy violation, informational=benign anomaly\n" + "- Extract ALL IOCs: file hashes (SHA-256), IP addresses, domains, " + "registry keys, email addresses\n" + "- alert_id format: ALT-YYYY-NNNNN\n" + "- Include the raw log snippet (first 500 chars)\n" + "- Be precise with hostnames, IPs, and usernames — these drive automation\n" + f"- Current UTC time: {now.isoformat()}" + ), + ) + + # ── Agent 2: Threat Intelligence ──────────────────────────────────── + threat_intel_agent = Agent( + client=client, + name="ThreatIntelAgent", + instructions=( + "You are a threat intelligence analyst. You receive a structured " + "SecurityAlert and produce a ThreatAssessment by enriching it with " + "threat intelligence.\n\n" + "Rules:\n" + "- Classify using MITRE ATT&CK framework — list specific technique IDs\n" + "- threat_score formula: base 50, +20 if matches known campaign, " + "+15 if credential theft involved, +10 per IOC matching known feeds, " + "-20 if likely false positive\n" + "- confidence_pct: 90%+ only if multiple IOC matches, 60-89% for " + "behavioral match, <60% for heuristic-only\n" + "- Override severity ONLY if intel clearly contradicts triage assessment\n" + "- Mark is_known_campaign=true only if IOCs match documented campaigns\n" + "- For unknown actors, set known_threat_actor to null" + ), + ) + + # ── Agent 3: Impact Analysis ──────────────────────────────────────── + impact_agent = Agent( + client=client, + name="ImpactAnalyzer", + instructions=( + "You are an impact assessment specialist for a managed IT platform. " + "You receive a ThreatAssessment and determine the blast radius across " + "the managed endpoint fleet.\n\n" + "Rules:\n" + "- Scope: single_endpoint (1 host), multiple_endpoints (2-10), " + "subnet (same /24), site (same location), tenant_wide (all customer endpoints), " + "cross_tenant (MSP-level compromise affecting multiple customers)\n" + "- If lateral_movement is the attack vector, assume at_risk_endpoint_count " + "is at least 3x the confirmed affected count\n" + "- Check if affected services include any regulated data " + "(healthcare→HIPAA, financial→SOX, EU data→GDPR)\n" + "- estimated_dwell_time: check timestamps, assume minimum 1 hour if " + "first detection was from automated tools, 24+ hours if from user report\n" + "- List specific business services that rely on affected endpoints" + ), + ) + + # ── Agent 4: Response Orchestration ───────────────────────────────── + response_agent = Agent( + client=client, + name="ResponseOrchestrator", + instructions=( + "You are the automated incident response engine for an MSP platform. " + "You receive an ImpactAnalysis and produce the final IncidentResponse " + "that drives automated remediation and ticket creation.\n\n" + "SLA matrix (from detection to response / containment):\n" + "- CRITICAL: P1, respond 15 min, contain 1 hr\n" + "- HIGH: P2, respond 30 min, contain 4 hrs\n" + "- MEDIUM: P3, respond 2 hrs, contain 24 hrs\n" + "- LOW: P4, respond 8 hrs, contain 72 hrs\n\n" + "Response rules:\n" + "- Ransomware/data_exfil → ALWAYS isolate endpoints + disable accounts\n" + "- Credential theft → force password reset + revoke sessions\n" + "- Lateral movement → isolate ALL affected + at-risk endpoints\n" + "- Phishing → quarantine email + block sender IP/domain\n" + "- If data_at_risk=true AND regulated data → add compliance flags\n" + "- Customer communication required for P1 and P2\n" + "- Always notify soc_lead; add tenant_admin for P1-P2, ciso for P1\n" + "- incident_id format: INC-YYYY-NNNNN\n" + f"- Current UTC time: {now.isoformat()}\n" + "- Calculate SLA deadlines from current time based on severity" + ), + ) + + # ── Raw security alert (simulating SIEM/EDR output) ───────────────── + + raw_alert = """ + === EDR ALERT — Managed Endpoint Security === + Timestamp: 2026-02-19T14:23:17Z + Source: Endpoint Detection & Response (EDR Agent v4.2) + Tenant: Contoso Financial Services (tenant: contoso-fin-2024) + + ALERT: Suspicious PowerShell execution detected on CONTOSO-DC01 + + Endpoint: CONTOSO-DC01 (10.42.1.5) — Domain Controller + User Context: CONTOSO\\svc_backup (service account) + + Observed Activity: + - PowerShell process spawned by svc_backup at 14:23:17Z + - Encoded command detected: Base64-encoded Invoke-Mimikatz variant + - LSASS memory access attempted (credential dumping pattern) + - Outbound connection to 185.220.101.42:443 (known C2 infrastructure) + - Kerberos ticket request anomaly: TGT requested for domain admin group + - Shadow copy deletion command queued (vssadmin delete shadows) + + File Hash (SHA-256): a1b2c3d4e5f67890abcdef1234567890abcdef1234567890abcdef1234567890 + Secondary Hash: f0e1d2c3b4a596870fedcba9876543210fedcba9876543210fedcba987654321 + DNS Query: update-service.kfrp[.]xyz (suspicious DGA-like domain) + + Additional Telemetry: + - 3 failed RDP attempts from CONTOSO-WS-047 to CONTOSO-DC01 prior to event + - svc_backup account password last changed 847 days ago + - Similar PowerShell pattern seen on CONTOSO-FS01 (file server) 47 min earlier + - No MFA enforcement on service accounts per tenant policy + + Raw Log: EventID=4688|ProcessName=powershell.exe|CommandLine=-enc + SQBtAHAAbwByAHQALQBNAG8AZAB1AGwAZQAgAC4AXABJAHaA...|ParentProcess=cmd.exe + |User=CONTOSO\\svc_backup|LogonType=3|SourceIP=10.42.3.47 + """ + + print("=" * 78) + print("🛡️ IT SECURITY INCIDENT RESPONSE — Typed-Contract Multi-Agent Pipeline") + print("=" * 78) + print() + print(" Scenario: MSP platform processes a critical endpoint alert through a") + print(" 4-agent pipeline where EVERY hand-off is a Pydantic-validated contract.") + print(" Wrong data at any step → wrong automated response → customer breach.") + print() + print(" Why typed contracts, not natural language (A2A)?") + print(" • severity=CRITICAL → 15-min SLA timer starts (not 'seems pretty bad')") + print(" • attack_vector=CREDENTIAL_THEFT → Mimikatz playbook (not 'suspicious PS')") + print(" • endpoints_to_isolate=['DC01','FS01'] → exact API calls (not 'some hosts')") + print() + + # ──────────────────────────────────────────────────────────────────── + # STEP 1 → Alert Triage: raw EDR alert → SecurityAlert + # ──────────────────────────────────────────────────────────────────── + print("━" * 78) + print("STEP 1 │ AlertTriageAgent: Raw EDR Alert → SecurityAlert (typed)") + print("━" * 78) + print(f" Input : Raw EDR alert text ({len(raw_alert.strip())} chars)") + print(f" Output : SecurityAlert (12 typed fields, enum-constrained)") + print() + + step1_response: AgentResponse = await triage_agent.run( + f"Triage this security alert and extract structured data:\n\n{raw_alert}", + options={"response_format": SecurityAlert}, + ) + alert = cast(SecurityAlert, step1_response.value) + _print_contract("SecurityAlert", alert) + + # ──────────────────────────────────────────────────────────────────── + # STEP 2 → Threat Intel: SecurityAlert → ThreatAssessment + # ──────────────────────────────────────────────────────────────────── + print("━" * 78) + print("STEP 2 │ ThreatIntelAgent: SecurityAlert → ThreatAssessment (typed)") + print("━" * 78) + print(f" Input : SecurityAlert (validated Pydantic model)") + print(f" Output : ThreatAssessment (11 typed fields, range + enum constrained)") + print() + + step2_response: AgentResponse = await threat_intel_agent.run( + ( + "Analyze this security alert with threat intelligence and classify the threat.\n\n" + f"Alert data:\n{alert.model_dump_json(indent=2)}" + ), + options={"response_format": ThreatAssessment}, + ) + threat = cast(ThreatAssessment, step2_response.value) + _print_contract("ThreatAssessment", threat) + + # ──────────────────────────────────────────────────────────────────── + # STEP 3 → Impact Analysis: ThreatAssessment → ImpactAnalysis + # ──────────────────────────────────────────────────────────────────── + print("━" * 78) + print("STEP 3 │ ImpactAnalyzer: ThreatAssessment → ImpactAnalysis (typed)") + print("━" * 78) + print(f" Input : ThreatAssessment (validated Pydantic model)") + print(f" Output : ImpactAnalysis (11 typed fields, scope + count constrained)") + print() + + step3_response: AgentResponse = await impact_agent.run( + ( + "Assess the impact and blast radius of this threat.\n\n" + f"Threat assessment:\n{threat.model_dump_json(indent=2)}\n\n" + f"Additional context from original alert:\n" + f"- Affected endpoint: {alert.affected_hostname} ({alert.affected_ip})\n" + f"- Tenant: {alert.tenant_id}\n" + f"- IOCs: {json.dumps(alert.indicators_of_compromise)}\n" + f"- Raw log mentions: CONTOSO-FS01 also affected 47 min earlier, " + f"3 failed RDP attempts from CONTOSO-WS-047" + ), + options={"response_format": ImpactAnalysis}, + ) + impact = cast(ImpactAnalysis, step3_response.value) + _print_contract("ImpactAnalysis", impact) + + # ──────────────────────────────────────────────────────────────────── + # STEP 4 → Response: ImpactAnalysis → IncidentResponse + # ──────────────────────────────────────────────────────────────────── + print("━" * 78) + print("STEP 4 │ ResponseOrchestrator: ImpactAnalysis → IncidentResponse (typed)") + print("━" * 78) + print(f" Input : ImpactAnalysis (validated Pydantic model)") + print(f" Output : IncidentResponse (15 typed fields, action-ready)") + print() + + step4_response: AgentResponse = await response_agent.run( + ( + "Generate the incident response plan with specific automated actions.\n\n" + f"Impact analysis:\n{impact.model_dump_json(indent=2)}\n\n" + f"Threat context: {threat.attack_vector.value}, " + f"threat_score={threat.threat_score}, " + f"severity={alert.severity.value}\n" + f"Affected user: {alert.affected_user}\n" + f"IOCs to block: {json.dumps(alert.indicators_of_compromise)}" + ), + options={"response_format": IncidentResponse}, + ) + response = cast(IncidentResponse, step4_response.value) + _print_contract("IncidentResponse", response) + + # ──────────────────────────────────────────────────────────────────── + # SUMMARY — the full typed data chain + # ──────────────────────────────────────────────────────────────────── + print() + print("=" * 78) + print("📊 PIPELINE SUMMARY — Typed Contract Chain") + print("=" * 78) + print() + print(f" 📡 Raw EDR alert → free text ({len(raw_alert.strip())} chars)") + print(f" 🚨 SecurityAlert → {alert.severity.value.upper()} | " + f"{alert.affected_hostname} | {len(alert.indicators_of_compromise)} IOCs") + print(f" 🔍 ThreatAssessment → {threat.attack_vector.value} | " + f"score={threat.threat_score:.0f}/100 | " + f"confidence={threat.confidence_pct:.0f}%") + print(f" 💥 ImpactAnalysis → {impact.impact_scope.value} | " + f"{impact.affected_endpoint_count} affected | " + f"{impact.at_risk_endpoint_count} at risk") + print(f" 🛡️ IncidentResponse → {response.priority.value} | " + f"{len(response.immediate_actions)} actions | " + f"notify: {', '.join(response.notification_list)}") + print() + + # Show the automated actions that would execute + print(" ⚡ AUTOMATED ACTIONS (would execute immediately):") + for i, action in enumerate(response.immediate_actions, 1): + print(f" {i}. {action.value}") + if response.endpoints_to_isolate: + print(f" 🔌 Isolate: {', '.join(response.endpoints_to_isolate)}") + if response.accounts_to_disable: + print(f" 🔒 Disable: {', '.join(response.accounts_to_disable)}") + if response.ips_to_block: + print(f" 🚫 Block IPs: {', '.join(response.ips_to_block)}") + if response.compliance_flags: + print(f" 📋 Compliance: {', '.join(response.compliance_flags)}") + print(f" ⏱️ SLA response by: {response.sla_response_deadline}") + print(f" ⏱️ SLA contain by: {response.sla_containment_deadline}") + + print() + print("─" * 78) + print("🔑 WHY THIS MATTERS — natural language vs. typed contracts in IT security:") + print("─" * 78) + print(""" + ┌─────────────────────────────────┬────────────────────────────────────────┐ + │ Natural Language (A2A) │ Typed Contracts (this demo) │ + ├─────────────────────────────────┼────────────────────────────────────────┤ + │ "The threat seems pretty │ threat.threat_score = 85.0 │ + │ serious, probably critical" │ alert.severity = Severity.CRITICAL │ + │ → Which SLA timer starts? │ → 15-min SLA timer, no ambiguity │ + ├─────────────────────────────────┼────────────────────────────────────────┤ + │ "Looks like credential theft │ threat.attack_vector = │ + │ with some lateral movement" │ AttackVector.CREDENTIAL_THEFT │ + │ → Which playbook runs? │ → Mimikatz containment playbook │ + ├─────────────────────────────────┼────────────────────────────────────────┤ + │ "Several servers are affected, │ impact.affected_hostnames = │ + │ should probably isolate them" │ ["CONTOSO-DC01", "CONTOSO-FS01"] │ + │ → Which endpoints get API │ → Exact isolation API calls fired │ + │ isolation calls? │ │ + ├─────────────────────────────────┼────────────────────────────────────────┤ + │ "There might be compliance │ response.compliance_flags = │ + │ implications" │ ["SOX_incident", "SOC2_incident"] │ + │ → Which regulators get │ → Exact notification workflows │ + │ notified? Within what SLA? │ triggered with deadlines │ + └─────────────────────────────────┴────────────────────────────────────────┘ + + In IT security, "approximately right" gets customers breached. + Typed contracts ensure machines act on EXACT data, not interpretations. + """) + print("=" * 78) + print("✅ Pipeline complete — 4 agents, 4 typed contracts, zero ambiguity.") + print("=" * 78) + + +def _print_contract(name: str, model: BaseModel) -> None: + """Pretty-print a Pydantic model as the validated contract output.""" + data = model.model_dump(mode="json") + print(f" ✅ {name} — validated successfully ({len(data)} fields)") + print(f" {'─' * 60}") + for key, value in data.items(): + if isinstance(value, list): + val_str = json.dumps(value) + if len(val_str) > 100: + print(f" {key}: {val_str[:100]}...") + else: + print(f" {key}: {val_str}") + elif isinstance(value, float): + print(f" {key}: {value}") + else: + val_str = str(value) + if len(val_str) > 100: + print(f" {key}: {val_str[:100]}...") + else: + print(f" {key}: {value}") + print() + + +if __name__ == "__main__": + asyncio.run(main()) From 3eac2c36b39178f119665b7527210e68bdd9366c Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 17:31:59 -0800 Subject: [PATCH 09/37] Replace workflow_local_remote.py with simplified proxy agent (Script 3) - Delete workflow_local_remote.py (old Script 3 with extra LLM call) - Add workflow_proxy_agent.py: MCPProxyAgent (BaseAgent, no LLM) calls MCP tools directly via call_tool() zero local LLM overhead - Simplify MCPProxyAgent: extract common _call() method, module-level _last_text() helper, remove verbose static methods - Fix workflow_typed_contracts.py: add async with context managers - Update README: merge Script 9 into Script 3 position, update architecture diagram, Quick Start, How It Works sections --- agentic_ai/agents/mcp_agent_demo/README.md | 72 +++-- .../mcp_agent_demo/workflow_local_remote.py | 124 -------- .../mcp_agent_demo/workflow_proxy_agent.py | 288 ++++++++++++++++++ .../workflow_typed_contracts.py | 76 ++--- 4 files changed, 372 insertions(+), 188 deletions(-) delete mode 100644 agentic_ai/agents/mcp_agent_demo/workflow_local_remote.py create mode 100644 agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md index d1e7549d3..64f1162fb 100644 --- a/agentic_ai/agents/mcp_agent_demo/README.md +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -6,7 +6,7 @@ This demo shows eight capabilities of the Microsoft Agent Framework: |---|------|--------| | 1 | **Expose an agent as an MCP server** (stateless HTTP) | `mcp_server.py` | | 2 | **Consume the agent-powered MCP service** from another agent | `mcp_client_agent.py` | -| 3 | **Workflow orchestrating local + remote (MCP) agents** | `workflow_local_remote.py` | +| 3 | **Proxy agent workflow** — local agent + remote MCP agent (no intermediate LLM) | `workflow_proxy_agent.py` | | 4 | **Stateful agent-as-MCP server** (multi-turn sessions) | `mcp_server_stateful.py` | | 5 | **Stateful multi-turn client agent** conversation via MCP | `mcp_client_stateful.py` | | 6 | **Typed-contract multi-agent workflow** (structured data exchange) | `workflow_typed_contracts.py` | @@ -17,25 +17,25 @@ This demo shows eight capabilities of the Microsoft Agent Framework: ### Stateless Mode (Scripts 1–3) +Scripts 1 & 2 expose / consume an agent via MCP. Script 3 adds the +**proxy agent pattern** — a `BaseAgent` with NO LLM that calls the MCP +tool directly via `call_tool()`, eliminating wasteful intermediate reasoning. + ``` -┌──────────────────────────────────────────────────────────────┐ -│ Workflow (SequentialBuilder) │ -│ │ -│ ┌──────────────┐ ┌─────────────────────────────┐ │ -│ │ Local Agent │ ───▶ │ Remote Agent (via MCP tool) │ │ -│ │ "Researcher" │ │ "Expert Advisor" │ │ -│ │ (in-process) │ │ (MCPStreamableHTTPTool) │ │ -│ └──────────────┘ └──────────┬──────────────────┘ │ -│ │ HTTP │ -└──────────────────────────────────────┼───────────────────────┘ - ▼ - ┌────────────────────────┐ - │ MCP Server (port 8002)│ - │ Expert Advisor Agent │ - │ (stateless_http=True) │ - └────────────────────────┘ +SequentialBuilder Workflow +┌──────────────────┐ text output ┌──────────────────┐ direct call ┌───────────────────────┐ +│ Researcher │ ──────────────▶│ MCPProxyAgent │ ─────────────▶│ MCP Server (8002) │ +│ (LLM agent) │ │ (BaseAgent) │ call_tool() │ ExpertAdvisor Agent │ +│ │ │ • NO LLM client │ HTTP │ • analyze_risk │ +│ Drafts initial │ │ • Zero tokens │ │ • get_market_data │ +│ analysis │ │ • Deterministic │ │ • summarize_findings │ +└──────────────────┘ └──────────────────┘ └───────────────────────┘ + 1 LLM call 0 LLM calls Remote LLM call ``` +**Critical detail:** `run()` must be a regular `def` (not `async def`) — +`SequentialBuilder` iterates with `async for`, which needs an async iterable. + ### Stateful Mode (Scripts 4–5) ``` @@ -118,16 +118,18 @@ uv run python mcp_client_agent.py This creates a local agent that connects to the MCP server and delegates questions to the remote Expert Advisor agent. -### 3. Run the Workflow Demo (in a second terminal) +### 3. Run the Proxy Agent Workflow (in a second terminal) ```bash cd agentic_ai/agents/mcp_agent_demo -uv run python workflow_local_remote.py +uv run python workflow_proxy_agent.py ``` -This demonstrates a **SequentialBuilder** workflow that chains: -1. A **local Researcher agent** (in-process) → drafts initial analysis -2. A **remote Expert Advisor agent** (via MCP) → reviews and enhances +This demonstrates a **SequentialBuilder** workflow using the **proxy agent +pattern** — a `BaseAgent` with NO LLM that calls an MCP tool directly: +1. **Researcher** (LLM agent) → drafts initial analysis +2. **MCPProxyAgent** (no LLM) → forwards output directly to `ask_expert` + via `call_tool()` — zero local LLM overhead, deterministic routing --- @@ -282,19 +284,33 @@ async with MCPStreamableHTTPTool( result = await agent.run("Analyze this business scenario...") ``` -### Workflow with Local + Remote Agents +### Workflow with Proxy Agent (Local + Remote) ```python +from agent_framework import BaseAgent, MCPStreamableHTTPTool, Message from agent_framework.orchestrations import SequentialBuilder -# Local agent (in-process) +class MCPProxyAgent(BaseAgent): + """No LLM — directly calls a known MCP tool.""" + def __init__(self, *, mcp_tool, tool_name, param_name="question", **kw): + super().__init__(**kw) + self._mcp_tool, self._tool_name, self._param_name = mcp_tool, tool_name, param_name + + def run(self, messages=None, *, stream=False, **kw): # must be regular def + return self._stream(messages) if stream else self._impl(messages) + + async def _impl(self, messages): + result = await self._mcp_tool.call_tool(self._tool_name, **{self._param_name: text}) + ... + +# Local agent (LLM) researcher = client.as_agent(name="researcher", instructions="...") -# Remote agent wrapper (via MCP tool) -remote_expert = Agent(client=client, tools=mcp_tool, name="expert") +# Proxy agent (NO LLM — direct MCP call) +proxy = MCPProxyAgent(mcp_tool=mcp_tool, tool_name="ask_expert", name="proxy") -# Sequential workflow: researcher → expert -workflow = SequentialBuilder(participants=[researcher, remote_expert]).build() +# Sequential workflow: researcher → proxy (zero LLM waste) +workflow = SequentialBuilder(participants=[researcher, proxy]).build() result = await workflow.run("Analyze the market for electric bikes") ``` diff --git a/agentic_ai/agents/mcp_agent_demo/workflow_local_remote.py b/agentic_ai/agents/mcp_agent_demo/workflow_local_remote.py deleted file mode 100644 index 7839f5f5b..000000000 --- a/agentic_ai/agents/mcp_agent_demo/workflow_local_remote.py +++ /dev/null @@ -1,124 +0,0 @@ -""" -Part 3 — Workflow orchestrating a local agent + remote agent (via MCP). - -Demonstrates a SequentialBuilder workflow where: - Step 1: Local "Researcher" agent drafts an initial analysis (in-process) - Step 2: Remote "Expert Advisor" agent (via MCP) reviews and enhances it - -Prerequisites: - mcp_server.py must be running on http://localhost:8002/mcp - -Usage: - cd agentic_ai/agents/mcp_agent_demo - uv run python workflow_local_remote.py -""" - -import asyncio -import os -import sys -from typing import Any, cast - -from dotenv import load_dotenv - -# Load credentials from the shared mcp/.env -env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") -load_dotenv(env_path) - -from agent_framework import Agent, MCPStreamableHTTPTool, Message -from agent_framework.azure import AzureOpenAIChatClient -from agent_framework.orchestrations import SequentialBuilder - - -async def main() -> None: - endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") - api_key = os.environ.get("AZURE_OPENAI_API_KEY") - deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") - api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") - - if not endpoint or not api_key: - print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") - sys.exit(1) - - mcp_server_url = "http://localhost:8002/mcp" - print(f"🔗 Connecting to remote Expert Advisor MCP server at {mcp_server_url} ...") - - # ── Set up the MCP connection to the remote agent ─────────────────────── - async with MCPStreamableHTTPTool( - name="expert_advisor", - description="Remote Expert Advisor accessible via MCP — can analyze risk, look up market data, and summarize findings.", - url=mcp_server_url, - ) as mcp_tool: - print(f"✅ MCP connected. Remote tools: {[t.name for t in mcp_tool.functions]}") - - # ── Create the two agents ─────────────────────────────────────────── - - client = AzureOpenAIChatClient( - api_key=api_key, - endpoint=endpoint, - deployment_name=deployment, - api_version=api_version, - ) - - # Agent 1: Local Researcher (runs entirely in-process, no MCP) - researcher = client.as_agent( - name="researcher", - instructions=( - "You are a market researcher. Given a business topic, provide a " - "concise initial analysis covering opportunity, competition, and " - "key trends. Keep it to 3-4 paragraphs. Your output will be " - "reviewed by an expert advisor next." - ), - ) - - # Agent 2: Remote Expert (enhanced with MCP tools from the server) - expert = Agent( - client=client, - name="expert_reviewer", - instructions=( - "You are a senior business strategy expert. You will receive a " - "researcher's initial analysis as context. Use your expert_advisor " - "MCP tools (ask_expert) to run risk analysis and get market data, " - "then provide an enhanced, final recommendation that combines " - "the researcher's draft with your expert tools' output." - ), - tools=mcp_tool, - ) - - # ── Build the Sequential Workflow ─────────────────────────────────── - # Researcher drafts → Expert reviews and enhances - workflow = SequentialBuilder(participants=[researcher, expert]).build() - - # ── Run the workflow ──────────────────────────────────────────────── - topic = "Expanding a mid-size SaaS company into the healthcare AI market" - print(f"\n{'='*70}") - print(f"📋 Workflow Topic: {topic}") - print(f"{'='*70}") - print(" Step 1: Local Researcher → draft analysis") - print(" Step 2: Remote Expert (MCP) → enhanced recommendation") - print(f"{'='*70}\n") - - outputs: list[list[Message]] = [] - async for event in workflow.run(topic, stream=True): - if event.type == "output": - outputs.append(cast(list[Message], event.data)) - - # ── Display results ───────────────────────────────────────────────── - if outputs: - print("\n" + "="*70) - print("📊 WORKFLOW RESULTS") - print("="*70) - for msg in outputs[-1]: - name = msg.author_name or ("assistant" if msg.role == "assistant" else "user") - role_label = "🔬 Researcher" if name == "researcher" else "🎓 Expert Reviewer" if name == "expert_reviewer" else f"👤 {name}" - print(f"\n{'─'*70}") - print(f"{role_label}:") - print(f"{'─'*70}") - print(msg.text) - - print(f"\n{'='*70}") - print("✅ Workflow complete — local agent + remote MCP agent orchestrated!") - print(f"{'='*70}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py b/agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py new file mode 100644 index 000000000..1a167c7df --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py @@ -0,0 +1,288 @@ +""" +Part 3 — Proxy Agent Workflow: Direct MCP Tool Invocation Without LLM + +Demonstrates a "proxy agent" pattern where a SequentialBuilder workflow +calls a remote MCP agent DIRECTLY — no intermediate LLM reasoning step. + +Traditional approach: + Researcher (LLM) → Expert Agent (LLM reasons → picks tool) → MCP + ↑ unnecessary LLM call — extra cost & latency + +Proxy agent approach: + Researcher (LLM) → MCPProxyAgent (direct call_tool()) → MCP + ↑ zero LLM overhead — deterministic routing + +MCPProxyAgent subclasses BaseAgent (not Agent) — no LLM client. +Its run() directly calls a known MCP tool by name. The orchestrator +treats it as a normal participant, but internally it's just a +transparent bridge to the MCP endpoint. + +When to use: + - You KNOW which MCP tool to call (no routing decision needed) + - You want deterministic routing with zero hallucination risk + - You're optimizing for latency and cost in high-throughput pipelines + - Cross-team integration where the remote service is a black box + +Prerequisites: + mcp_server.py must be running on http://localhost:8002/mcp + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python workflow_proxy_agent.py +""" + +import asyncio +import os +import sys +import uuid +from typing import Any, cast + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import ( + AgentResponse, + AgentResponseUpdate, + BaseAgent, + Content, + MCPStreamableHTTPTool, + Message, +) +from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.orchestrations import SequentialBuilder + + +# ═══════════════════════════════════════════════════════════════════════════ +# MCPProxyAgent — a BaseAgent with NO LLM that directly calls an MCP tool +# ═══════════════════════════════════════════════════════════════════════════ + + +class MCPProxyAgent(BaseAgent): + """Agent with NO LLM that directly calls a known MCP tool via call_tool(). + + Eliminates the LLM reasoning step that would otherwise just pick the + same tool every time — deterministic, zero-cost routing. + """ + + def __init__( + self, + *, + mcp_tool: MCPStreamableHTTPTool, + tool_name: str, + name: str = "mcp_proxy", + description: str | None = None, + param_name: str = "question", + **kwargs: Any, + ) -> None: + super().__init__(name=name, description=description, **kwargs) + self._mcp_tool = mcp_tool + self._tool_name = tool_name + self._param_name = param_name + + def run(self, messages: Any = None, *, stream: bool = False, **kwargs: Any) -> Any: + """Forward input to the MCP tool — no LLM involved. + + Must be a regular def (not async def) because SequentialBuilder + does ``async for update in agent.run(stream=True)``. + """ + if stream: + return self._run_stream(messages) + return self._run_impl(messages) + + async def _run_impl(self, messages: Any) -> AgentResponse: + result_text = await self._call(messages) + return AgentResponse( + messages=[Message("assistant", [result_text], author_name=self.name)], + response_id=f"proxy-{uuid.uuid4().hex[:8]}", + agent_id=self.id, + ) + + async def _run_stream(self, messages: Any): + result_text = await self._call(messages) + yield AgentResponseUpdate( + contents=[Content.from_text(result_text)], + role="assistant", + author_name=self.name, + agent_id=self.id, + response_id=f"proxy-{uuid.uuid4().hex[:8]}", + ) + + async def _call(self, messages: Any) -> str: + """Extract input text, call the MCP tool, return result as string.""" + input_text = _last_text(messages) + result = await self._mcp_tool.call_tool( + self._tool_name, **{self._param_name: input_text} + ) + # call_tool returns Content objects — extract their .text + if isinstance(result, (list, tuple)): + return "\n".join( + c.text for c in result if hasattr(c, "text") and c.text + ) or str(result) + return result.text if hasattr(result, "text") and result.text else str(result) + + +def _last_text(messages: Any) -> str: + """Get the last agent's text output from the orchestrator's message list.""" + if isinstance(messages, str): + return messages + if isinstance(messages, Message): + return messages.text or "" + if isinstance(messages, (list, tuple)): + for m in reversed(messages): + text = m.text if isinstance(m, Message) else str(m) + if text and text.strip(): + return text + return str(messages) if messages else "" + + +# ═══════════════════════════════════════════════════════════════════════════ +# MAIN — demonstrate proxy agent in a SequentialBuilder workflow +# ═══════════════════════════════════════════════════════════════════════════ + + +async def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + mcp_server_url = "http://localhost:8002/mcp" + + print("=" * 78) + print("🔀 PROXY AGENT — Direct MCP Tool Invocation Without LLM Reasoning") + print("=" * 78) + print() + print(" This demo compares two approaches in a SequentialBuilder workflow:") + print() + print(" ❌ Traditional: Researcher → Expert(LLM+MCP) → MCP Server") + print(" ↳ Expert's LLM must reason about which tool to call (extra cost)") + print() + print(" ✅ Proxy Agent: Researcher → MCPProxyAgent → MCP Server") + print(" ↳ Proxy calls ask_expert directly — zero LLM overhead") + print() + + # ── Connect to the MCP server ─────────────────────────────────────── + async with MCPStreamableHTTPTool( + name="expert_advisor", + description="Remote Expert Advisor via MCP", + url=mcp_server_url, + ) as mcp_tool: + print(f"🔗 Connected to MCP server at {mcp_server_url}") + print(f" Available tools: {[t.name for t in mcp_tool.functions]}") + print() + + # ── Set up agents ─────────────────────────────────────────────── + + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + # Agent 1: Local Researcher (uses LLM) + researcher = client.as_agent( + name="researcher", + instructions=( + "You are a market researcher. Given a business topic, provide a " + "concise initial analysis covering opportunity, competition, and " + "key trends. Keep it to 3-4 paragraphs. Your output will be " + "reviewed by an expert advisor next." + ), + ) + + # Agent 2: MCPProxyAgent — NO LLM, direct MCP call + proxy_expert = MCPProxyAgent( + mcp_tool=mcp_tool, + tool_name="ask_expert", # The exact MCP tool to invoke + param_name="question", # Parameter name the tool expects + name="proxy_expert", + description="Proxy that directly calls the remote Expert Advisor MCP tool", + ) + + # ── Build workflow: Researcher → Proxy Expert ─────────────────── + workflow = SequentialBuilder( + participants=[researcher, proxy_expert], + ).build() + + topic = "Expanding a mid-size SaaS company into the healthcare AI market" + + print("━" * 78) + print(f"📋 Topic: {topic}") + print("━" * 78) + print(" Step 1: Researcher (LLM) → draft analysis") + print(" Step 2: MCPProxyAgent (NO LLM) → direct call to ask_expert MCP tool") + print("━" * 78) + print() + + # ── Run the workflow ──────────────────────────────────────────── + outputs: list[list[Message]] = [] + async for event in workflow.run(topic, stream=True): + if event.type == "output": + outputs.append(cast(list[Message], event.data)) + + # ── Display results ───────────────────────────────────────────── + if outputs: + print("=" * 78) + print("📊 WORKFLOW RESULTS") + print("=" * 78) + for msg in outputs[-1]: + name = msg.author_name or "unknown" + if name == "researcher": + label = "🔬 Researcher (LLM — 1 LLM call)" + elif name == "proxy_expert": + label = "🎯 Proxy Expert (NO local LLM — direct MCP call)" + else: + label = f"👤 {name}" + print(f"\n{'─' * 78}") + print(f"{label}:") + print(f"{'─' * 78}") + print(msg.text) + + print() + print("=" * 78) + print("📊 COMPARISON: Traditional vs. Proxy Agent") + print("=" * 78) + print(""" + ┌─────────────────────────────┬─────────────────────────────────────────┐ + │ Traditional │ Proxy Agent (this demo) │ + ├─────────────────────────────┼─────────────────────────────────────────┤ + │ 2 local LLM calls: │ 1 local LLM call: │ + │ • Researcher (LLM) │ • Researcher (LLM) │ + │ • Expert (LLM → pick tool) │ • ProxyAgent (direct call_tool) │ + ├─────────────────────────────┼─────────────────────────────────────────┤ + │ Expert LLM must reason │ No reasoning — tool_name is hardcoded │ + │ about which tool to call │ at configuration time │ + ├─────────────────────────────┼─────────────────────────────────────────┤ + │ Risk: LLM picks wrong tool │ Zero risk — deterministic routing │ + │ or hallucinates parameters │ with exact parameter forwarding │ + ├─────────────────────────────┼─────────────────────────────────────────┤ + │ Extra latency: ~1-3 sec │ Eliminated — direct HTTP call to MCP │ + │ for LLM to reason │ │ + ├─────────────────────────────┼─────────────────────────────────────────┤ + │ Extra cost: LLM tokens for │ Zero extra cost — no tokens consumed │ + │ tool selection reasoning │ for routing decision │ + ├─────────────────────────────┼─────────────────────────────────────────┤ + │ Good for: dynamic tool │ Good for: known integrations, │ + │ selection, exploration │ pipelines, cross-team orchestration │ + └─────────────────────────────┴─────────────────────────────────────────┘ + + The Proxy Agent pattern is ideal for intra-company agent orchestration + where you KNOW which remote service to call. The remote MCP server's + agent still does its own LLM reasoning internally — the proxy just + eliminates the redundant local LLM call that would only pick the tool. + """) + print("=" * 78) + print("✅ Workflow complete — proxy agent called MCP directly, zero local LLM waste.") + print("=" * 78) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py b/agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py index 9217e2654..5a19fc0ad 100644 --- a/agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py +++ b/agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py @@ -444,10 +444,11 @@ async def main() -> None: print(f" Output : SecurityAlert (12 typed fields, enum-constrained)") print() - step1_response: AgentResponse = await triage_agent.run( - f"Triage this security alert and extract structured data:\n\n{raw_alert}", - options={"response_format": SecurityAlert}, - ) + async with triage_agent: + step1_response: AgentResponse = await triage_agent.run( + f"Triage this security alert and extract structured data:\n\n{raw_alert}", + options={"response_format": SecurityAlert}, + ) alert = cast(SecurityAlert, step1_response.value) _print_contract("SecurityAlert", alert) @@ -461,13 +462,14 @@ async def main() -> None: print(f" Output : ThreatAssessment (11 typed fields, range + enum constrained)") print() - step2_response: AgentResponse = await threat_intel_agent.run( - ( - "Analyze this security alert with threat intelligence and classify the threat.\n\n" - f"Alert data:\n{alert.model_dump_json(indent=2)}" - ), - options={"response_format": ThreatAssessment}, - ) + async with threat_intel_agent: + step2_response: AgentResponse = await threat_intel_agent.run( + ( + "Analyze this security alert with threat intelligence and classify the threat.\n\n" + f"Alert data:\n{alert.model_dump_json(indent=2)}" + ), + options={"response_format": ThreatAssessment}, + ) threat = cast(ThreatAssessment, step2_response.value) _print_contract("ThreatAssessment", threat) @@ -481,19 +483,20 @@ async def main() -> None: print(f" Output : ImpactAnalysis (11 typed fields, scope + count constrained)") print() - step3_response: AgentResponse = await impact_agent.run( - ( - "Assess the impact and blast radius of this threat.\n\n" - f"Threat assessment:\n{threat.model_dump_json(indent=2)}\n\n" - f"Additional context from original alert:\n" - f"- Affected endpoint: {alert.affected_hostname} ({alert.affected_ip})\n" - f"- Tenant: {alert.tenant_id}\n" - f"- IOCs: {json.dumps(alert.indicators_of_compromise)}\n" - f"- Raw log mentions: CONTOSO-FS01 also affected 47 min earlier, " - f"3 failed RDP attempts from CONTOSO-WS-047" - ), - options={"response_format": ImpactAnalysis}, - ) + async with impact_agent: + step3_response: AgentResponse = await impact_agent.run( + ( + "Assess the impact and blast radius of this threat.\n\n" + f"Threat assessment:\n{threat.model_dump_json(indent=2)}\n\n" + f"Additional context from original alert:\n" + f"- Affected endpoint: {alert.affected_hostname} ({alert.affected_ip})\n" + f"- Tenant: {alert.tenant_id}\n" + f"- IOCs: {json.dumps(alert.indicators_of_compromise)}\n" + f"- Raw log mentions: CONTOSO-FS01 also affected 47 min earlier, " + f"3 failed RDP attempts from CONTOSO-WS-047" + ), + options={"response_format": ImpactAnalysis}, + ) impact = cast(ImpactAnalysis, step3_response.value) _print_contract("ImpactAnalysis", impact) @@ -507,18 +510,19 @@ async def main() -> None: print(f" Output : IncidentResponse (15 typed fields, action-ready)") print() - step4_response: AgentResponse = await response_agent.run( - ( - "Generate the incident response plan with specific automated actions.\n\n" - f"Impact analysis:\n{impact.model_dump_json(indent=2)}\n\n" - f"Threat context: {threat.attack_vector.value}, " - f"threat_score={threat.threat_score}, " - f"severity={alert.severity.value}\n" - f"Affected user: {alert.affected_user}\n" - f"IOCs to block: {json.dumps(alert.indicators_of_compromise)}" - ), - options={"response_format": IncidentResponse}, - ) + async with response_agent: + step4_response: AgentResponse = await response_agent.run( + ( + "Generate the incident response plan with specific automated actions.\n\n" + f"Impact analysis:\n{impact.model_dump_json(indent=2)}\n\n" + f"Threat context: {threat.attack_vector.value}, " + f"threat_score={threat.threat_score}, " + f"severity={alert.severity.value}\n" + f"Affected user: {alert.affected_user}\n" + f"IOCs to block: {json.dumps(alert.indicators_of_compromise)}" + ), + options={"response_format": IncidentResponse}, + ) response = cast(IncidentResponse, step4_response.value) _print_contract("IncidentResponse", response) From 72ad453f81b457b6ab0c17bc01e87aab93a3d301 Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 17:36:29 -0800 Subject: [PATCH 10/37] Clean up MCP demos: remove comparison commentary, drop typed-contracts - workflow_proxy_agent.py: remove 'traditional vs proxy' framing MCPProxyAgent is the standard approach for remote agent integration - Delete workflow_typed_contracts.py: not relevant to MCP integration - README: 7 scripts, renumber hybrid 6-7, remove typed-contract sections --- agentic_ai/agents/mcp_agent_demo/README.md | 128 +--- .../mcp_agent_demo/workflow_proxy_agent.py | 70 +- .../workflow_typed_contracts.py | 625 ------------------ 3 files changed, 32 insertions(+), 791 deletions(-) delete mode 100644 agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md index 64f1162fb..b0c8512f9 100644 --- a/agentic_ai/agents/mcp_agent_demo/README.md +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -1,39 +1,34 @@ # MCP Agent Demo — Agent Framework -This demo shows eight capabilities of the Microsoft Agent Framework: +This demo shows seven capabilities of the Microsoft Agent Framework: | # | What | Script | |---|------|--------| | 1 | **Expose an agent as an MCP server** (stateless HTTP) | `mcp_server.py` | | 2 | **Consume the agent-powered MCP service** from another agent | `mcp_client_agent.py` | -| 3 | **Proxy agent workflow** — local agent + remote MCP agent (no intermediate LLM) | `workflow_proxy_agent.py` | +| 3 | **Proxy agent workflow** — local agent + remote MCP agent | `workflow_proxy_agent.py` | | 4 | **Stateful agent-as-MCP server** (multi-turn sessions) | `mcp_server_stateful.py` | | 5 | **Stateful multi-turn client agent** conversation via MCP | `mcp_client_stateful.py` | -| 6 | **Typed-contract multi-agent workflow** (structured data exchange) | `workflow_typed_contracts.py` | -| 7 | **Hybrid MCP server** — strict-schema + natural-language tools in one endpoint | `mcp_server_hybrid.py` | -| 8 | **Hybrid client** — demonstrates both tool types in a single incident flow | `mcp_client_hybrid.py` | +| 6 | **Hybrid MCP server** — strict-schema + natural-language tools in one endpoint | `mcp_server_hybrid.py` | +| 7 | **Hybrid client** — demonstrates both tool types in a single incident flow | `mcp_client_hybrid.py` | ## Architecture ### Stateless Mode (Scripts 1–3) -Scripts 1 & 2 expose / consume an agent via MCP. Script 3 adds the -**proxy agent pattern** — a `BaseAgent` with NO LLM that calls the MCP -tool directly via `call_tool()`, eliminating wasteful intermediate reasoning. +Scripts 1 & 2 expose / consume an agent via MCP. Script 3 adds a +SequentialBuilder workflow that chains a local agent with the remote +MCP agent via `MCPProxyAgent`. ``` SequentialBuilder Workflow -┌──────────────────┐ text output ┌──────────────────┐ direct call ┌───────────────────────┐ +┌──────────────────┐ text output ┌──────────────────┐ call_tool() ┌───────────────────────┐ │ Researcher │ ──────────────▶│ MCPProxyAgent │ ─────────────▶│ MCP Server (8002) │ -│ (LLM agent) │ │ (BaseAgent) │ call_tool() │ ExpertAdvisor Agent │ -│ │ │ • NO LLM client │ HTTP │ • analyze_risk │ -│ Drafts initial │ │ • Zero tokens │ │ • get_market_data │ -│ analysis │ │ • Deterministic │ │ • summarize_findings │ +│ (LLM agent) │ │ (BaseAgent) │ HTTP │ ExpertAdvisor Agent │ └──────────────────┘ └──────────────────┘ └───────────────────────┘ - 1 LLM call 0 LLM calls Remote LLM call ``` -**Critical detail:** `run()` must be a regular `def` (not `async def`) — +**Note:** `MCPProxyAgent.run()` must be a regular `def` (not `async def`) — `SequentialBuilder` iterates with `async for`, which needs an async iterable. ### Stateful Mode (Scripts 4–5) @@ -56,7 +51,7 @@ Client Agent (Coordinator) Server (FastMCP v3) and maps each MCP `session_id` to an `AgentSession` that accumulates conversation history across tool calls. -### Hybrid Mode (Scripts 7–8) +### Hybrid Mode (Scripts 6–7) ``` ┌─────────────────────── Hybrid MCP Server (port 8002) ───────────────────────┐ @@ -125,11 +120,10 @@ cd agentic_ai/agents/mcp_agent_demo uv run python workflow_proxy_agent.py ``` -This demonstrates a **SequentialBuilder** workflow using the **proxy agent -pattern** — a `BaseAgent` with NO LLM that calls an MCP tool directly: -1. **Researcher** (LLM agent) → drafts initial analysis -2. **MCPProxyAgent** (no LLM) → forwards output directly to `ask_expert` - via `call_tool()` — zero local LLM overhead, deterministic routing +This runs a **SequentialBuilder** workflow that chains a local Researcher +agent with the remote Expert Advisor via `MCPProxyAgent`: +1. **Researcher** → drafts initial analysis +2. **MCPProxyAgent** → forwards output to `ask_expert` via `call_tool()` --- @@ -162,47 +156,9 @@ This runs a 3-turn conversation where each follow-up builds on the prior: The remote expert references prior turns because the `mcp-session-id` header ties all requests to the same server-side `AgentSession`. -### 6. Run the Typed-Contract Workflow (standalone — no server needed) - -```bash -cd agentic_ai/agents/mcp_agent_demo -uv run python workflow_typed_contracts.py -``` - -This demonstrates **strict typed data exchange** between agents in an -IT security incident response pipeline — the key advantage of -framework-native orchestration over natural-language protocols like A2A. -Built for an MSP / IT management platform where wrong data at any step -means wrong automated response and a customer breach. - -``` - ┌──────────────┐ SecurityAlert ┌──────────────┐ ThreatAssessment - │ Alert │ ────────────────▶ │ Threat │ ────────────────────▶ - │ Triage │ (Pydantic) │ Intel │ (Pydantic) - └──────────────┘ └──────────────┘ - │ - ┌──────────────┐ IncidentResponse ▼ - │ Response │ ◀──────────────── ┌──────────────┐ ImpactAnalysis - │ Orchestrator│ (Pydantic) │ Impact │ ────────────────────▶ - └──────────────┘ │ Analyzer │ (Pydantic) - │ └──────────────┘ - ▼ - IncidentResponse (drives automated remediation — isolation, blocking, SLA) -``` - -Each arrow represents a **Pydantic-validated contract**: -- `SecurityAlert` — 12 fields, enum-constrained severity & alert source -- `ThreatAssessment` — 11 fields, MITRE ATT&CK vectors, threat score (0-100) -- `ImpactAnalysis` — 11 fields, blast-radius scope enum, endpoint counts -- `IncidentResponse` — 15 fields, ordered remediation actions, SLA deadlines - -If any agent produces output that violates the schema, the pipeline fails -fast with a Pydantic `ValidationError` — no silent corruption downstream. -In IT security, "approximately right" gets customers breached. - --- -### 7. Start the Hybrid MCP Server +### 6. Start the Hybrid MCP Server ```bash cd agentic_ai/agents/mcp_agent_demo @@ -225,7 +181,7 @@ exposes **both** strict-schema tools and natural-language tools: Session state is shared: strict tools store their outputs (`last_alert`, `last_threat`, `last_response`) so natural-language tools can reference them. -### 8. Run the Hybrid Client (in a second terminal) +### 7. Run the Hybrid Client (in a second terminal) ```bash cd agentic_ai/agents/mcp_agent_demo @@ -284,14 +240,14 @@ async with MCPStreamableHTTPTool( result = await agent.run("Analyze this business scenario...") ``` -### Workflow with Proxy Agent (Local + Remote) +### Integrating a Remote MCP Agent into a Workflow ```python from agent_framework import BaseAgent, MCPStreamableHTTPTool, Message from agent_framework.orchestrations import SequentialBuilder class MCPProxyAgent(BaseAgent): - """No LLM — directly calls a known MCP tool.""" + """Forwards messages to a remote MCP tool via call_tool().""" def __init__(self, *, mcp_tool, tool_name, param_name="question", **kw): super().__init__(**kw) self._mcp_tool, self._tool_name, self._param_name = mcp_tool, tool_name, param_name @@ -303,13 +259,9 @@ class MCPProxyAgent(BaseAgent): result = await self._mcp_tool.call_tool(self._tool_name, **{self._param_name: text}) ... -# Local agent (LLM) researcher = client.as_agent(name="researcher", instructions="...") - -# Proxy agent (NO LLM — direct MCP call) proxy = MCPProxyAgent(mcp_tool=mcp_tool, tool_name="ask_expert", name="proxy") -# Sequential workflow: researcher → proxy (zero LLM waste) workflow = SequentialBuilder(participants=[researcher, proxy]).build() result = await workflow.run("Analyze the market for electric bikes") ``` @@ -365,47 +317,6 @@ async with MCPStreamableHTTPTool( # The expert remembers all prior turns ✓ ``` -### Typed-Contract Workflow (Structured Data Exchange) - -The typed-contract demo shows how `response_format` with Pydantic models -creates strict data boundaries between agents — no natural language needed -at the inter-agent boundary: - -```python -from pydantic import BaseModel, Field -from agent_framework import Agent, AgentResponse - -# Define the CONTRACT between Agent 1 and Agent 2 -class ThreatAssessment(BaseModel): - threat_score: float = Field(ge=0, le=100) - attack_vector: AttackVector # MITRE ATT&CK enum - mitre_techniques: list[str] # e.g. ["T1566.001", "T1059.001"] - confidence_pct: float = Field(ge=0, le=100) - -# Agent 1 produces typed output -response: AgentResponse = await threat_intel_agent.run( - f"Analyze this alert:\n{alert.model_dump_json()}", - options={"response_format": ThreatAssessment}, # ← enforced schema -) -threat = cast(ThreatAssessment, response.value) # ← validated Python object - -# Agent 2 consumes typed input (not prose!) -impact_response = await impact_agent.run( - f"Assess blast radius:\n{threat.model_dump_json()}", - options={"response_format": ImpactAnalysis}, -) -``` - -**Why this matters vs. A2A / natural-language passing:** - -| Aspect | Natural Language (A2A) | Typed Contracts (this demo) | -|--------|----------------------|-----------------------------| -| Data format | Prose: "threat seems critical" | `threat.threat_score = 85.0` (float, 0-100) | -| Validation | None — hope the LLM got it right | Pydantic validates at runtime | -| Downstream action | LLM must re-interpret prose | Direct attribute → API call | -| Failure mode | Silent corruption → wrong playbook | `ValidationError` — fail fast | -| SLA tracking | "Pretty urgent" → which timer? | `severity=CRITICAL` → 15-min SLA | - ## Dependencies | Package | Purpose | @@ -414,5 +325,4 @@ impact_response = await impact_agent.run( | `agent-framework-orchestrations` | SequentialBuilder for workflows | | `mcp` | MCP SDK (used by stateless server via `mcp.server.fastmcp.FastMCP`) | | `fastmcp` | PrefectHQ FastMCP v3 (used by stateful server — session state support) | -| `pydantic` | Typed contracts / schema validation (transitive via agent-framework-core) | | `python-dotenv` | Load credentials from `mcp/.env` | diff --git a/agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py b/agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py index 1a167c7df..f7ccf71ab 100644 --- a/agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py +++ b/agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py @@ -56,16 +56,12 @@ # ═══════════════════════════════════════════════════════════════════════════ -# MCPProxyAgent — a BaseAgent with NO LLM that directly calls an MCP tool +# MCPProxyAgent — BaseAgent that forwards to a remote MCP tool # ═══════════════════════════════════════════════════════════════════════════ class MCPProxyAgent(BaseAgent): - """Agent with NO LLM that directly calls a known MCP tool via call_tool(). - - Eliminates the LLM reasoning step that would otherwise just pick the - same tool every time — deterministic, zero-cost routing. - """ + """BaseAgent that forwards messages to a remote MCP tool via call_tool().""" def __init__( self, @@ -156,17 +152,9 @@ async def main() -> None: mcp_server_url = "http://localhost:8002/mcp" print("=" * 78) - print("🔀 PROXY AGENT — Direct MCP Tool Invocation Without LLM Reasoning") + print("🔀 Proxy Agent Workflow — Remote MCP Agent Integration") print("=" * 78) print() - print(" This demo compares two approaches in a SequentialBuilder workflow:") - print() - print(" ❌ Traditional: Researcher → Expert(LLM+MCP) → MCP Server") - print(" ↳ Expert's LLM must reason about which tool to call (extra cost)") - print() - print(" ✅ Proxy Agent: Researcher → MCPProxyAgent → MCP Server") - print(" ↳ Proxy calls ask_expert directly — zero LLM overhead") - print() # ── Connect to the MCP server ─────────────────────────────────────── async with MCPStreamableHTTPTool( @@ -198,16 +186,16 @@ async def main() -> None: ), ) - # Agent 2: MCPProxyAgent — NO LLM, direct MCP call + # Agent 2: MCPProxyAgent — forwards to MCP proxy_expert = MCPProxyAgent( mcp_tool=mcp_tool, - tool_name="ask_expert", # The exact MCP tool to invoke - param_name="question", # Parameter name the tool expects + tool_name="ask_expert", + param_name="question", name="proxy_expert", - description="Proxy that directly calls the remote Expert Advisor MCP tool", + description="Proxy that calls the remote Expert Advisor MCP tool", ) - # ── Build workflow: Researcher → Proxy Expert ─────────────────── + # ── Build workflow ────────────────────────────────────────────── workflow = SequentialBuilder( participants=[researcher, proxy_expert], ).build() @@ -217,8 +205,8 @@ async def main() -> None: print("━" * 78) print(f"📋 Topic: {topic}") print("━" * 78) - print(" Step 1: Researcher (LLM) → draft analysis") - print(" Step 2: MCPProxyAgent (NO LLM) → direct call to ask_expert MCP tool") + print(" Step 1: Researcher → draft analysis") + print(" Step 2: MCPProxyAgent → forward to remote expert via MCP") print("━" * 78) print() @@ -236,9 +224,9 @@ async def main() -> None: for msg in outputs[-1]: name = msg.author_name or "unknown" if name == "researcher": - label = "🔬 Researcher (LLM — 1 LLM call)" + label = "🔬 Researcher" elif name == "proxy_expert": - label = "🎯 Proxy Expert (NO local LLM — direct MCP call)" + label = "🎯 Proxy Expert (via MCP)" else: label = f"👤 {name}" print(f"\n{'─' * 78}") @@ -248,39 +236,7 @@ async def main() -> None: print() print("=" * 78) - print("📊 COMPARISON: Traditional vs. Proxy Agent") - print("=" * 78) - print(""" - ┌─────────────────────────────┬─────────────────────────────────────────┐ - │ Traditional │ Proxy Agent (this demo) │ - ├─────────────────────────────┼─────────────────────────────────────────┤ - │ 2 local LLM calls: │ 1 local LLM call: │ - │ • Researcher (LLM) │ • Researcher (LLM) │ - │ • Expert (LLM → pick tool) │ • ProxyAgent (direct call_tool) │ - ├─────────────────────────────┼─────────────────────────────────────────┤ - │ Expert LLM must reason │ No reasoning — tool_name is hardcoded │ - │ about which tool to call │ at configuration time │ - ├─────────────────────────────┼─────────────────────────────────────────┤ - │ Risk: LLM picks wrong tool │ Zero risk — deterministic routing │ - │ or hallucinates parameters │ with exact parameter forwarding │ - ├─────────────────────────────┼─────────────────────────────────────────┤ - │ Extra latency: ~1-3 sec │ Eliminated — direct HTTP call to MCP │ - │ for LLM to reason │ │ - ├─────────────────────────────┼─────────────────────────────────────────┤ - │ Extra cost: LLM tokens for │ Zero extra cost — no tokens consumed │ - │ tool selection reasoning │ for routing decision │ - ├─────────────────────────────┼─────────────────────────────────────────┤ - │ Good for: dynamic tool │ Good for: known integrations, │ - │ selection, exploration │ pipelines, cross-team orchestration │ - └─────────────────────────────┴─────────────────────────────────────────┘ - - The Proxy Agent pattern is ideal for intra-company agent orchestration - where you KNOW which remote service to call. The remote MCP server's - agent still does its own LLM reasoning internally — the proxy just - eliminates the redundant local LLM call that would only pick the tool. - """) - print("=" * 78) - print("✅ Workflow complete — proxy agent called MCP directly, zero local LLM waste.") + print("✅ Workflow complete.") print("=" * 78) diff --git a/agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py b/agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py deleted file mode 100644 index 5a19fc0ad..000000000 --- a/agentic_ai/agents/mcp_agent_demo/workflow_typed_contracts.py +++ /dev/null @@ -1,625 +0,0 @@ -""" -Part 4 — Typed-Contract Multi-Agent Workflow (Structured Data Exchange) - -Demonstrates WHY strict typed data exchange between agents matters in -business-critical multi-agent orchestrations. This security incident -response pipeline — built for an IT management / MSP platform — has -Pydantic-enforced INPUT and OUTPUT contracts at every agent boundary. -If any agent's output violates the schema the pipeline fails fast — -no silent corruption, no LLM "interpretation" of malformed data. - -Pipeline (IT Security Incident Response): - - ┌──────────────┐ SecurityAlert ┌──────────────┐ ThreatAssessment - │ Alert │ ────────────────▶ │ Threat │ ────────────────────▶ - │ Triage │ (Pydantic) │ Intel │ (Pydantic) - └──────────────┘ └──────────────┘ - │ - ┌──────────────┐ IncidentResponse ▼ - │ Response │ ◀──────────────── ┌──────────────┐ ImpactAnalysis - │ Orchestrator│ (Pydantic) │ Impact │ ────────────────────▶ - └──────────────┘ │ Analyzer │ (Pydantic) - │ └──────────────┘ - ▼ - IncidentResponse (Pydantic — drives automated remediation) - -Why typed contracts matter in IT security: - - Wrong severity enum → wrong SLA timer → breached customer SLA - - Misclassified attack vector → wrong containment playbook executed - - Ambiguous "some endpoints affected" → automated isolation skips hosts - - "Risk seems high" vs risk_score=87 → completely different escalation path - -Usage: - cd agentic_ai/agents/mcp_agent_demo - uv run python workflow_typed_contracts.py -""" - -import asyncio -import json -import os -import sys -from datetime import datetime, timezone -from enum import Enum -from typing import cast - -from dotenv import load_dotenv -from pydantic import BaseModel, Field - -# ── Load credentials ──────────────────────────────────────────────────────── -env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") -load_dotenv(env_path) - - -# ═══════════════════════════════════════════════════════════════════════════ -# CONTRACTS — Pydantic models that form the typed data-exchange boundary -# between agents. Each model is both a SCHEMA (for the LLM) and a -# VALIDATOR (for the runtime). -# -# In an MSP / IT management platform, these contracts drive automation: -# - Automated ticket creation (needs exact severity + category) -# - SLA timers (wrong severity = wrong SLA = contractual breach) -# - Playbook selection (wrong attack_vector = wrong containment) -# - Customer notifications (requires structured, auditable data) -# ═══════════════════════════════════════════════════════════════════════════ - - -class AlertSource(str, Enum): - """Where the raw alert originated.""" - EDR = "edr" # Endpoint Detection & Response - SIEM = "siem" # Security Information & Event Management - NETWORK_IDS = "network_ids" # Network Intrusion Detection System - EMAIL_GATEWAY = "email_gateway" - VULNERABILITY_SCANNER = "vulnerability_scanner" - USER_REPORT = "user_report" - - -class Severity(str, Enum): - """NIST-aligned severity levels — drives SLA timers.""" - CRITICAL = "critical" # SLA: 15 min response, 1 hr containment - HIGH = "high" # SLA: 30 min response, 4 hr containment - MEDIUM = "medium" # SLA: 2 hr response, 24 hr containment - LOW = "low" # SLA: 8 hr response, 72 hr containment - INFORMATIONAL = "informational" - - -class AttackVector(str, Enum): - """MITRE ATT&CK-aligned attack vector classification.""" - RANSOMWARE = "ransomware" - PHISHING = "phishing" - LATERAL_MOVEMENT = "lateral_movement" - CREDENTIAL_THEFT = "credential_theft" - MALWARE_EXECUTION = "malware_execution" - DATA_EXFILTRATION = "data_exfiltration" - PRIVILEGE_ESCALATION = "privilege_escalation" - COMMAND_AND_CONTROL = "command_and_control" - SUPPLY_CHAIN = "supply_chain" - UNKNOWN = "unknown" - - -class SecurityAlert(BaseModel): - """Contract: Alert Triage Agent → Threat Intel Agent. - - Structured extraction from raw SIEM/EDR noise into actionable alert. - Every field drives downstream automation — no ambiguity allowed. - """ - alert_id: str = Field(description="Unique alert identifier (e.g. ALT-2026-00847)") - timestamp: str = Field(description="ISO 8601 timestamp of first detection") - source: AlertSource = Field(description="System that generated the alert") - severity: Severity = Field(description="Triage-assessed severity level") - title: str = Field(description="One-line alert title") - description: str = Field(description="Concise description of observed activity") - affected_hostname: str = Field(description="Primary affected endpoint hostname") - affected_ip: str = Field(description="IP address of affected endpoint") - affected_user: str = Field(description="Username associated with the activity") - tenant_id: str = Field(description="MSP customer/tenant identifier") - indicators_of_compromise: list[str] = Field( - description="List of IOCs: file hashes, IPs, domains, registry keys" - ) - raw_log_snippet: str = Field(description="Relevant log excerpt (first 500 chars)") - - -class ThreatCategory(str, Enum): - """Threat categorization — determines which playbook runs.""" - APT = "apt" # Advanced Persistent Threat - COMMODITY_MALWARE = "commodity_malware" - INSIDER_THREAT = "insider_threat" - OPPORTUNISTIC_ATTACK = "opportunistic_attack" - TARGETED_ATTACK = "targeted_attack" - FALSE_POSITIVE = "false_positive" - - -class ThreatAssessment(BaseModel): - """Contract: Threat Intel Agent → Impact Analyzer. - - Enriched intelligence assessment with confidence scores. - threat_score drives escalation; attack_vector selects containment playbook. - """ - alert_id: str = Field(description="Alert ID (pass-through for traceability)") - threat_category: ThreatCategory = Field(description="Classified threat category") - attack_vector: AttackVector = Field(description="MITRE ATT&CK vector classification") - threat_score: float = Field( - ge=0, le=100, description="Threat confidence score 0-100 (higher = more dangerous)" - ) - known_threat_actor: str | None = Field( - description="Known threat actor/group name, or null if unknown" - ) - mitre_techniques: list[str] = Field( - description="MITRE ATT&CK technique IDs (e.g. T1566.001, T1059.001)" - ) - ioc_matches: list[str] = Field( - description="IOCs that matched known threat intelligence feeds" - ) - confidence_pct: float = Field( - ge=0, le=100, description="Analyst confidence in classification (0-100%)" - ) - is_known_campaign: bool = Field( - description="True if IOCs match a known active campaign" - ) - recommended_severity_override: Severity | None = Field( - description="Override triage severity if intel warrants it, null to keep original" - ) - intel_summary: str = Field(description="Brief threat intelligence narrative") - - -class ImpactScope(str, Enum): - """Blast radius classification — drives isolation scope.""" - SINGLE_ENDPOINT = "single_endpoint" - MULTIPLE_ENDPOINTS = "multiple_endpoints" - SUBNET = "subnet" - SITE = "site" - TENANT_WIDE = "tenant_wide" - CROSS_TENANT = "cross_tenant" # MSP nightmare scenario - - -class ImpactAnalysis(BaseModel): - """Contract: Impact Analyzer → Response Orchestrator. - - Precise blast-radius assessment across the managed endpoint fleet. - Numbers here drive automated isolation — wrong count = missed endpoints. - """ - alert_id: str = Field(description="Alert ID (pass-through for traceability)") - impact_scope: ImpactScope = Field(description="Blast radius classification") - affected_endpoint_count: int = Field( - ge=0, description="Number of confirmed affected endpoints" - ) - at_risk_endpoint_count: int = Field( - ge=0, description="Number of endpoints at risk but not yet confirmed compromised" - ) - affected_hostnames: list[str] = Field(description="List of confirmed affected hostnames") - compromised_accounts: list[str] = Field( - description="User accounts confirmed or suspected compromised" - ) - data_at_risk: bool = Field( - description="True if sensitive/regulated data may be exposed" - ) - business_services_affected: list[str] = Field( - description="Business services/applications impacted (e.g. 'Exchange', 'ERP', 'File Server')" - ) - lateral_movement_detected: bool = Field( - description="True if evidence of lateral movement across hosts" - ) - estimated_dwell_time_hours: float = Field( - ge=0, description="Estimated hours the threat has been present" - ) - impact_summary: str = Field(description="Brief impact assessment narrative") - - -class RemediationAction(str, Enum): - """Specific automated actions the response system can execute.""" - ISOLATE_ENDPOINT = "isolate_endpoint" - DISABLE_ACCOUNT = "disable_account" - BLOCK_IP = "block_ip" - BLOCK_HASH = "block_hash" - FORCE_PASSWORD_RESET = "force_password_reset" - REVOKE_SESSIONS = "revoke_sessions" - QUARANTINE_EMAIL = "quarantine_email" - DEPLOY_PATCH = "deploy_patch" - RESTORE_FROM_BACKUP = "restore_from_backup" - ESCALATE_TO_HUMAN = "escalate_to_human" - - -class ResponsePriority(str, Enum): - """Incident response priority — maps to SLA commitments.""" - P1 = "P1" # Immediate automated action + SOC notification - P2 = "P2" # Automated action within 15 min + analyst review - P3 = "P3" # Queued for analyst review, auto-action optional - P4 = "P4" # Logged, no immediate action required - - -class IncidentResponse(BaseModel): - """Contract: Response Orchestrator → Automation Engine / Ticket System. - - This is the OUTPUT that drives real-world automated remediation. - Every field maps directly to an API call or ticket field. - Wrong data here = wrong containment = breach. - """ - incident_id: str = Field(description="Generated incident ID (e.g. INC-2026-00412)") - alert_id: str = Field(description="Originating alert ID (traceability)") - priority: ResponsePriority = Field(description="Response priority (P1-P4)") - final_severity: Severity = Field(description="Final assessed severity after all analysis") - immediate_actions: list[RemediationAction] = Field( - description="Ordered list of automated remediation actions to execute NOW" - ) - endpoints_to_isolate: list[str] = Field( - description="Specific hostnames to network-isolate" - ) - accounts_to_disable: list[str] = Field( - description="Specific accounts to disable/lock" - ) - ips_to_block: list[str] = Field(description="IPs to add to firewall blocklist") - hashes_to_block: list[str] = Field(description="File hashes to block across fleet") - notification_list: list[str] = Field( - description="Roles/teams to notify (e.g. 'soc_lead', 'tenant_admin', 'ciso')" - ) - customer_communication_required: bool = Field( - description="True if MSP must notify the customer per SLA" - ) - compliance_flags: list[str] = Field( - description="Regulatory notifications needed (e.g. 'GDPR_72hr', 'HIPAA_breach', 'SOC2_incident')" - ) - sla_response_deadline: str = Field( - description="ISO 8601 deadline for initial response per SLA" - ) - sla_containment_deadline: str = Field( - description="ISO 8601 deadline for containment per SLA" - ) - decision_rationale: str = Field(description="Brief explanation of response decisions") - - -# ═══════════════════════════════════════════════════════════════════════════ -# PIPELINE — sequential agent chain with typed contracts at each boundary -# ═══════════════════════════════════════════════════════════════════════════ - - -async def main() -> None: - from agent_framework import Agent, AgentResponse - from agent_framework.azure import AzureOpenAIChatClient - - endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") - api_key = os.environ.get("AZURE_OPENAI_API_KEY") - deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") - api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") - - if not endpoint or not api_key: - print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") - sys.exit(1) - - client = AzureOpenAIChatClient( - api_key=api_key, - endpoint=endpoint, - deployment_name=deployment, - api_version=api_version, - ) - - now = datetime.now(timezone.utc) - - # ── Agent 1: Alert Triage ─────────────────────────────────────────── - triage_agent = Agent( - client=client, - name="AlertTriageAgent", - instructions=( - "You are a Tier-1 SOC analyst performing initial alert triage for a " - "managed security services provider (MSP). You receive raw SIEM/EDR " - "alerts and extract structured, actionable security alert data.\n\n" - "Rules:\n" - "- Assign severity based on: critical=active ransomware/data exfil, " - "high=credential theft/lateral movement, medium=suspicious behavior, " - "low=policy violation, informational=benign anomaly\n" - "- Extract ALL IOCs: file hashes (SHA-256), IP addresses, domains, " - "registry keys, email addresses\n" - "- alert_id format: ALT-YYYY-NNNNN\n" - "- Include the raw log snippet (first 500 chars)\n" - "- Be precise with hostnames, IPs, and usernames — these drive automation\n" - f"- Current UTC time: {now.isoformat()}" - ), - ) - - # ── Agent 2: Threat Intelligence ──────────────────────────────────── - threat_intel_agent = Agent( - client=client, - name="ThreatIntelAgent", - instructions=( - "You are a threat intelligence analyst. You receive a structured " - "SecurityAlert and produce a ThreatAssessment by enriching it with " - "threat intelligence.\n\n" - "Rules:\n" - "- Classify using MITRE ATT&CK framework — list specific technique IDs\n" - "- threat_score formula: base 50, +20 if matches known campaign, " - "+15 if credential theft involved, +10 per IOC matching known feeds, " - "-20 if likely false positive\n" - "- confidence_pct: 90%+ only if multiple IOC matches, 60-89% for " - "behavioral match, <60% for heuristic-only\n" - "- Override severity ONLY if intel clearly contradicts triage assessment\n" - "- Mark is_known_campaign=true only if IOCs match documented campaigns\n" - "- For unknown actors, set known_threat_actor to null" - ), - ) - - # ── Agent 3: Impact Analysis ──────────────────────────────────────── - impact_agent = Agent( - client=client, - name="ImpactAnalyzer", - instructions=( - "You are an impact assessment specialist for a managed IT platform. " - "You receive a ThreatAssessment and determine the blast radius across " - "the managed endpoint fleet.\n\n" - "Rules:\n" - "- Scope: single_endpoint (1 host), multiple_endpoints (2-10), " - "subnet (same /24), site (same location), tenant_wide (all customer endpoints), " - "cross_tenant (MSP-level compromise affecting multiple customers)\n" - "- If lateral_movement is the attack vector, assume at_risk_endpoint_count " - "is at least 3x the confirmed affected count\n" - "- Check if affected services include any regulated data " - "(healthcare→HIPAA, financial→SOX, EU data→GDPR)\n" - "- estimated_dwell_time: check timestamps, assume minimum 1 hour if " - "first detection was from automated tools, 24+ hours if from user report\n" - "- List specific business services that rely on affected endpoints" - ), - ) - - # ── Agent 4: Response Orchestration ───────────────────────────────── - response_agent = Agent( - client=client, - name="ResponseOrchestrator", - instructions=( - "You are the automated incident response engine for an MSP platform. " - "You receive an ImpactAnalysis and produce the final IncidentResponse " - "that drives automated remediation and ticket creation.\n\n" - "SLA matrix (from detection to response / containment):\n" - "- CRITICAL: P1, respond 15 min, contain 1 hr\n" - "- HIGH: P2, respond 30 min, contain 4 hrs\n" - "- MEDIUM: P3, respond 2 hrs, contain 24 hrs\n" - "- LOW: P4, respond 8 hrs, contain 72 hrs\n\n" - "Response rules:\n" - "- Ransomware/data_exfil → ALWAYS isolate endpoints + disable accounts\n" - "- Credential theft → force password reset + revoke sessions\n" - "- Lateral movement → isolate ALL affected + at-risk endpoints\n" - "- Phishing → quarantine email + block sender IP/domain\n" - "- If data_at_risk=true AND regulated data → add compliance flags\n" - "- Customer communication required for P1 and P2\n" - "- Always notify soc_lead; add tenant_admin for P1-P2, ciso for P1\n" - "- incident_id format: INC-YYYY-NNNNN\n" - f"- Current UTC time: {now.isoformat()}\n" - "- Calculate SLA deadlines from current time based on severity" - ), - ) - - # ── Raw security alert (simulating SIEM/EDR output) ───────────────── - - raw_alert = """ - === EDR ALERT — Managed Endpoint Security === - Timestamp: 2026-02-19T14:23:17Z - Source: Endpoint Detection & Response (EDR Agent v4.2) - Tenant: Contoso Financial Services (tenant: contoso-fin-2024) - - ALERT: Suspicious PowerShell execution detected on CONTOSO-DC01 - - Endpoint: CONTOSO-DC01 (10.42.1.5) — Domain Controller - User Context: CONTOSO\\svc_backup (service account) - - Observed Activity: - - PowerShell process spawned by svc_backup at 14:23:17Z - - Encoded command detected: Base64-encoded Invoke-Mimikatz variant - - LSASS memory access attempted (credential dumping pattern) - - Outbound connection to 185.220.101.42:443 (known C2 infrastructure) - - Kerberos ticket request anomaly: TGT requested for domain admin group - - Shadow copy deletion command queued (vssadmin delete shadows) - - File Hash (SHA-256): a1b2c3d4e5f67890abcdef1234567890abcdef1234567890abcdef1234567890 - Secondary Hash: f0e1d2c3b4a596870fedcba9876543210fedcba9876543210fedcba987654321 - DNS Query: update-service.kfrp[.]xyz (suspicious DGA-like domain) - - Additional Telemetry: - - 3 failed RDP attempts from CONTOSO-WS-047 to CONTOSO-DC01 prior to event - - svc_backup account password last changed 847 days ago - - Similar PowerShell pattern seen on CONTOSO-FS01 (file server) 47 min earlier - - No MFA enforcement on service accounts per tenant policy - - Raw Log: EventID=4688|ProcessName=powershell.exe|CommandLine=-enc - SQBtAHAAbwByAHQALQBNAG8AZAB1AGwAZQAgAC4AXABJAHaA...|ParentProcess=cmd.exe - |User=CONTOSO\\svc_backup|LogonType=3|SourceIP=10.42.3.47 - """ - - print("=" * 78) - print("🛡️ IT SECURITY INCIDENT RESPONSE — Typed-Contract Multi-Agent Pipeline") - print("=" * 78) - print() - print(" Scenario: MSP platform processes a critical endpoint alert through a") - print(" 4-agent pipeline where EVERY hand-off is a Pydantic-validated contract.") - print(" Wrong data at any step → wrong automated response → customer breach.") - print() - print(" Why typed contracts, not natural language (A2A)?") - print(" • severity=CRITICAL → 15-min SLA timer starts (not 'seems pretty bad')") - print(" • attack_vector=CREDENTIAL_THEFT → Mimikatz playbook (not 'suspicious PS')") - print(" • endpoints_to_isolate=['DC01','FS01'] → exact API calls (not 'some hosts')") - print() - - # ──────────────────────────────────────────────────────────────────── - # STEP 1 → Alert Triage: raw EDR alert → SecurityAlert - # ──────────────────────────────────────────────────────────────────── - print("━" * 78) - print("STEP 1 │ AlertTriageAgent: Raw EDR Alert → SecurityAlert (typed)") - print("━" * 78) - print(f" Input : Raw EDR alert text ({len(raw_alert.strip())} chars)") - print(f" Output : SecurityAlert (12 typed fields, enum-constrained)") - print() - - async with triage_agent: - step1_response: AgentResponse = await triage_agent.run( - f"Triage this security alert and extract structured data:\n\n{raw_alert}", - options={"response_format": SecurityAlert}, - ) - alert = cast(SecurityAlert, step1_response.value) - _print_contract("SecurityAlert", alert) - - # ──────────────────────────────────────────────────────────────────── - # STEP 2 → Threat Intel: SecurityAlert → ThreatAssessment - # ──────────────────────────────────────────────────────────────────── - print("━" * 78) - print("STEP 2 │ ThreatIntelAgent: SecurityAlert → ThreatAssessment (typed)") - print("━" * 78) - print(f" Input : SecurityAlert (validated Pydantic model)") - print(f" Output : ThreatAssessment (11 typed fields, range + enum constrained)") - print() - - async with threat_intel_agent: - step2_response: AgentResponse = await threat_intel_agent.run( - ( - "Analyze this security alert with threat intelligence and classify the threat.\n\n" - f"Alert data:\n{alert.model_dump_json(indent=2)}" - ), - options={"response_format": ThreatAssessment}, - ) - threat = cast(ThreatAssessment, step2_response.value) - _print_contract("ThreatAssessment", threat) - - # ──────────────────────────────────────────────────────────────────── - # STEP 3 → Impact Analysis: ThreatAssessment → ImpactAnalysis - # ──────────────────────────────────────────────────────────────────── - print("━" * 78) - print("STEP 3 │ ImpactAnalyzer: ThreatAssessment → ImpactAnalysis (typed)") - print("━" * 78) - print(f" Input : ThreatAssessment (validated Pydantic model)") - print(f" Output : ImpactAnalysis (11 typed fields, scope + count constrained)") - print() - - async with impact_agent: - step3_response: AgentResponse = await impact_agent.run( - ( - "Assess the impact and blast radius of this threat.\n\n" - f"Threat assessment:\n{threat.model_dump_json(indent=2)}\n\n" - f"Additional context from original alert:\n" - f"- Affected endpoint: {alert.affected_hostname} ({alert.affected_ip})\n" - f"- Tenant: {alert.tenant_id}\n" - f"- IOCs: {json.dumps(alert.indicators_of_compromise)}\n" - f"- Raw log mentions: CONTOSO-FS01 also affected 47 min earlier, " - f"3 failed RDP attempts from CONTOSO-WS-047" - ), - options={"response_format": ImpactAnalysis}, - ) - impact = cast(ImpactAnalysis, step3_response.value) - _print_contract("ImpactAnalysis", impact) - - # ──────────────────────────────────────────────────────────────────── - # STEP 4 → Response: ImpactAnalysis → IncidentResponse - # ──────────────────────────────────────────────────────────────────── - print("━" * 78) - print("STEP 4 │ ResponseOrchestrator: ImpactAnalysis → IncidentResponse (typed)") - print("━" * 78) - print(f" Input : ImpactAnalysis (validated Pydantic model)") - print(f" Output : IncidentResponse (15 typed fields, action-ready)") - print() - - async with response_agent: - step4_response: AgentResponse = await response_agent.run( - ( - "Generate the incident response plan with specific automated actions.\n\n" - f"Impact analysis:\n{impact.model_dump_json(indent=2)}\n\n" - f"Threat context: {threat.attack_vector.value}, " - f"threat_score={threat.threat_score}, " - f"severity={alert.severity.value}\n" - f"Affected user: {alert.affected_user}\n" - f"IOCs to block: {json.dumps(alert.indicators_of_compromise)}" - ), - options={"response_format": IncidentResponse}, - ) - response = cast(IncidentResponse, step4_response.value) - _print_contract("IncidentResponse", response) - - # ──────────────────────────────────────────────────────────────────── - # SUMMARY — the full typed data chain - # ──────────────────────────────────────────────────────────────────── - print() - print("=" * 78) - print("📊 PIPELINE SUMMARY — Typed Contract Chain") - print("=" * 78) - print() - print(f" 📡 Raw EDR alert → free text ({len(raw_alert.strip())} chars)") - print(f" 🚨 SecurityAlert → {alert.severity.value.upper()} | " - f"{alert.affected_hostname} | {len(alert.indicators_of_compromise)} IOCs") - print(f" 🔍 ThreatAssessment → {threat.attack_vector.value} | " - f"score={threat.threat_score:.0f}/100 | " - f"confidence={threat.confidence_pct:.0f}%") - print(f" 💥 ImpactAnalysis → {impact.impact_scope.value} | " - f"{impact.affected_endpoint_count} affected | " - f"{impact.at_risk_endpoint_count} at risk") - print(f" 🛡️ IncidentResponse → {response.priority.value} | " - f"{len(response.immediate_actions)} actions | " - f"notify: {', '.join(response.notification_list)}") - print() - - # Show the automated actions that would execute - print(" ⚡ AUTOMATED ACTIONS (would execute immediately):") - for i, action in enumerate(response.immediate_actions, 1): - print(f" {i}. {action.value}") - if response.endpoints_to_isolate: - print(f" 🔌 Isolate: {', '.join(response.endpoints_to_isolate)}") - if response.accounts_to_disable: - print(f" 🔒 Disable: {', '.join(response.accounts_to_disable)}") - if response.ips_to_block: - print(f" 🚫 Block IPs: {', '.join(response.ips_to_block)}") - if response.compliance_flags: - print(f" 📋 Compliance: {', '.join(response.compliance_flags)}") - print(f" ⏱️ SLA response by: {response.sla_response_deadline}") - print(f" ⏱️ SLA contain by: {response.sla_containment_deadline}") - - print() - print("─" * 78) - print("🔑 WHY THIS MATTERS — natural language vs. typed contracts in IT security:") - print("─" * 78) - print(""" - ┌─────────────────────────────────┬────────────────────────────────────────┐ - │ Natural Language (A2A) │ Typed Contracts (this demo) │ - ├─────────────────────────────────┼────────────────────────────────────────┤ - │ "The threat seems pretty │ threat.threat_score = 85.0 │ - │ serious, probably critical" │ alert.severity = Severity.CRITICAL │ - │ → Which SLA timer starts? │ → 15-min SLA timer, no ambiguity │ - ├─────────────────────────────────┼────────────────────────────────────────┤ - │ "Looks like credential theft │ threat.attack_vector = │ - │ with some lateral movement" │ AttackVector.CREDENTIAL_THEFT │ - │ → Which playbook runs? │ → Mimikatz containment playbook │ - ├─────────────────────────────────┼────────────────────────────────────────┤ - │ "Several servers are affected, │ impact.affected_hostnames = │ - │ should probably isolate them" │ ["CONTOSO-DC01", "CONTOSO-FS01"] │ - │ → Which endpoints get API │ → Exact isolation API calls fired │ - │ isolation calls? │ │ - ├─────────────────────────────────┼────────────────────────────────────────┤ - │ "There might be compliance │ response.compliance_flags = │ - │ implications" │ ["SOX_incident", "SOC2_incident"] │ - │ → Which regulators get │ → Exact notification workflows │ - │ notified? Within what SLA? │ triggered with deadlines │ - └─────────────────────────────────┴────────────────────────────────────────┘ - - In IT security, "approximately right" gets customers breached. - Typed contracts ensure machines act on EXACT data, not interpretations. - """) - print("=" * 78) - print("✅ Pipeline complete — 4 agents, 4 typed contracts, zero ambiguity.") - print("=" * 78) - - -def _print_contract(name: str, model: BaseModel) -> None: - """Pretty-print a Pydantic model as the validated contract output.""" - data = model.model_dump(mode="json") - print(f" ✅ {name} — validated successfully ({len(data)} fields)") - print(f" {'─' * 60}") - for key, value in data.items(): - if isinstance(value, list): - val_str = json.dumps(value) - if len(val_str) > 100: - print(f" {key}: {val_str[:100]}...") - else: - print(f" {key}: {val_str}") - elif isinstance(value, float): - print(f" {key}: {value}") - else: - val_str = str(value) - if len(val_str) > 100: - print(f" {key}: {val_str[:100]}...") - else: - print(f" {key}: {value}") - print() - - -if __name__ == "__main__": - asyncio.run(main()) From 7487b66b9c2d8837a0656f51dd39882be7367c09 Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 18:05:46 -0800 Subject: [PATCH 11/37] Add LangGraph + MAF GroupChat cross-framework demo (Scripts 8-9) - mcp_server_langgraph.py: LangGraph ReAct agent exposed as MCP server on port 8003 with architecture tools (pattern eval, migration estimate, tech stack recommendation) - workflow_group_chat.py: MAF GroupChatBuilder orchestration with LLM planner routing between local BusinessStrategist (MAF) and remote TechnicalArchitect (LangGraph via MCP) - pyproject.toml: add langgraph, langchain-openai, langchain-core deps - README.md: update to 9 scripts, add cross-framework architecture diagram, Quick Start sections, and dependency table entries --- agentic_ai/agents/mcp_agent_demo/README.md | 82 ++- .../mcp_agent_demo/mcp_server_langgraph.py | 294 ++++++++++ .../agents/mcp_agent_demo/pyproject.toml | 3 + agentic_ai/agents/mcp_agent_demo/uv.lock | 546 ++++++++++++++++++ .../mcp_agent_demo/workflow_group_chat.py | 306 ++++++++++ 5 files changed, 1229 insertions(+), 2 deletions(-) create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py create mode 100644 agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md index b0c8512f9..b0056d49d 100644 --- a/agentic_ai/agents/mcp_agent_demo/README.md +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -1,6 +1,6 @@ # MCP Agent Demo — Agent Framework -This demo shows seven capabilities of the Microsoft Agent Framework: +This demo shows nine capabilities of the Microsoft Agent Framework: | # | What | Script | |---|------|--------| @@ -11,6 +11,8 @@ This demo shows seven capabilities of the Microsoft Agent Framework: | 5 | **Stateful multi-turn client agent** conversation via MCP | `mcp_client_stateful.py` | | 6 | **Hybrid MCP server** — strict-schema + natural-language tools in one endpoint | `mcp_server_hybrid.py` | | 7 | **Hybrid client** — demonstrates both tool types in a single incident flow | `mcp_client_hybrid.py` | +| 8 | **LangGraph agent as MCP server** — cross-framework interop | `mcp_server_langgraph.py` | +| 9 | **Group Chat orchestration** — local + LangGraph agent + LLM planner | `workflow_group_chat.py` | ## Architecture @@ -85,6 +87,40 @@ history across tool calls. Both share the same session state, so `explain_for_customer` can reference the `IncidentResponse` that `create_response` just produced. +### Cross-Framework Group Chat (Scripts 8–9) + +Scripts 8 & 9 demonstrate **cross-framework interoperability**: a LangGraph +agent is exposed via MCP and participates in a MAF GroupChat alongside a +native MAF agent, orchestrated by an LLM-based planner. + +``` +GroupChatBuilder +┌───────────────────────────────────────────────────────────────────────────────┐ +│ 🎯 Planner (Orchestrator) │ +│ LLM agent that decides who speaks next │ +│ returns: {next_speaker, terminate, reason} │ +└──────────────────┬──────────────────────────────┬────────────────────────────┘ + │ │ + ▼ ▼ +┌──────────────────────────────┐ ┌────────────────────────────────────────────┐ +│ 👔 BusinessStrategist │ │ 🏗️ TechnicalArchitect (MCPProxyAgent) │ +│ Local MAF Agent │ │ ┌──────────────────────────────────────┐ │ +│ (Azure OpenAI) │ │ │ call_tool("ask_architect", ...) │ │ +│ │ │ │ ↓ MCP HTTP (port 8003) │ │ +│ • Business impact & ROI │ │ │ ┌──────────────────────────────────┐ │ │ +│ • Risk mitigation │ │ │ │ LangGraph ReAct Agent │ │ │ +│ • Go-to-market strategy │ │ │ │ • evaluate_architecture_pattern │ │ │ +│ • Customer experience │ │ │ │ • estimate_migration_effort │ │ │ +│ │ │ │ │ • recommend_tech_stack │ │ │ +└──────────────────────────────┘ │ │ └──────────────────────────────────┘ │ │ + │ └──────────────────────────────────────┘ │ + └────────────────────────────────────────────┘ +``` + +**Key insight:** MCP is the universal connector. MAF's GroupChatBuilder +doesn't know (or care) that one participant is LangGraph behind the scenes. +This pattern works with any framework that can expose tools via MCP. + ## Prerequisites - Azure OpenAI credentials in `mcp/.env` (already configured) @@ -202,6 +238,45 @@ Both share the same MCP session. --- +### 8. Start the LangGraph MCP Server + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv run python mcp_server_langgraph.py +``` + +This starts a **LangGraph-based** Technical Architect agent as an MCP server +on `http://localhost:8003/mcp`. The agent uses a ReAct loop with +architecture-related tools: + +| Tool | Purpose | +|------|---------| +| `evaluate_architecture_pattern` | Evaluate microservices, monolith, serverless, event-driven patterns | +| `estimate_migration_effort` | Estimate timeline, cost, and team size for a migration | +| `recommend_tech_stack` | Recommend technologies for a given domain and scale | + +**Cross-framework interop:** The agent is built entirely with LangGraph +but served via MCP — any MCP client can consume it regardless of framework. + +### 9. Run the Group Chat Orchestration (in a second terminal) + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv run python workflow_group_chat.py +``` + +This runs a multi-agent **GroupChat** discussion using MAF's +`GroupChatBuilder` with an LLM-based planner. Three roles participate: + +1. **🎯 Planner** (orchestrator) — decides who speaks next +2. **👔 BusinessStrategist** (local MAF agent) — business impact, ROI, risk +3. **🏗️ TechnicalArchitect** (LangGraph via MCP) — architecture, tech stack + +Each turn shows which agent was selected and their response. The planner +alternates between participants to build a comprehensive strategy. + +--- + ## How It Works ### Exposing an Agent as MCP Server @@ -322,7 +397,10 @@ async with MCPStreamableHTTPTool( | Package | Purpose | |---------|---------| | `agent-framework-core` | Agent, MCPStreamableHTTPTool, tool decorator | -| `agent-framework-orchestrations` | SequentialBuilder for workflows | +| `agent-framework-orchestrations` | SequentialBuilder, GroupChatBuilder for workflows | | `mcp` | MCP SDK (used by stateless server via `mcp.server.fastmcp.FastMCP`) | | `fastmcp` | PrefectHQ FastMCP v3 (used by stateful server — session state support) | +| `langgraph` | LangGraph — stateful agent graphs (used by Script 8) | +| `langchain-openai` | Azure OpenAI integration for LangGraph agents | +| `langchain-core` | LangChain core abstractions (messages, tools) | | `python-dotenv` | Load credentials from `mcp/.env` | diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py b/agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py new file mode 100644 index 000000000..516cfa8af --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py @@ -0,0 +1,294 @@ +""" +Part 8 — LangGraph Agent as MCP Server (Cross-Framework Interop) + +A LangGraph-based Technical Architect agent exposed as an MCP endpoint. +This demonstrates cross-framework interoperability: the agent is built +entirely with LangGraph (ReAct loop + tools) but served over MCP so +ANY framework — MAF, AutoGen, CrewAI — can consume it as a tool. + +The LangGraph agent has architecture-related tools and uses a ReAct +loop to reason about which tools to call and how to synthesize results. + +Prerequisites: + uv sync (installs langgraph, langchain-openai, langchain-core) + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_server_langgraph.py +""" + +import asyncio +import os +import sys +from typing import Annotated + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from langchain_core.messages import HumanMessage, SystemMessage +from langchain_core.tools import tool as lc_tool +from langchain_openai import AzureChatOpenAI +from langgraph.graph import END, START, MessagesState, StateGraph +from langgraph.prebuilt import ToolNode +from mcp.server.fastmcp import FastMCP + + +# ═══════════════════════════════════════════════════════════════════════════ +# LangGraph Tools — architecture domain +# ═══════════════════════════════════════════════════════════════════════════ + + +@lc_tool +def evaluate_architecture_pattern(pattern: str) -> str: + """Evaluate an architecture pattern and return pros, cons, and guidance. + Supported patterns: microservices, monolith, serverless, event-driven.""" + patterns = { + "microservices": ( + "Microservices Pattern Evaluation:\n" + "✓ Independent deployment & scaling per service\n" + "✓ Technology diversity — polyglot-friendly\n" + "✓ Fault isolation — one service failure doesn't cascade\n" + "✗ Distributed system complexity (network latency, partial failures)\n" + "✗ Data consistency challenges (eventual consistency, saga pattern)\n" + "✗ Operational overhead — requires mature DevOps practices\n" + "• Best for: Large teams, complex domains, high scalability needs\n" + "• Maturity required: HIGH — need CI/CD, observability, service mesh" + ), + "monolith": ( + "Monolith Pattern Evaluation:\n" + "✓ Simple to develop, test, and deploy initially\n" + "✓ Single codebase — easy to understand end-to-end\n" + "✓ No inter-service communication overhead\n" + "✗ Scaling requires scaling the entire application\n" + "✗ Technology lock-in — one stack for everything\n" + "✗ Deployment risk — small change requires full redeploy\n" + "• Best for: Small teams, MVPs, low-complexity domains\n" + "• Consider: Modular monolith as a stepping stone" + ), + "serverless": ( + "Serverless Pattern Evaluation:\n" + "✓ Zero infrastructure management — cloud handles scaling\n" + "✓ Pay-per-execution — cost-efficient for variable workloads\n" + "✓ Rapid development — focus on business logic only\n" + "✗ Cold start latency — first invocation can be slow\n" + "✗ Vendor lock-in — tied to cloud provider's runtime\n" + "✗ Debugging complexity — distributed tracing is essential\n" + "• Best for: Event-driven workloads, APIs, batch processing\n" + "• Watch out: Long-running tasks may exceed time limits" + ), + "event-driven": ( + "Event-Driven Pattern Evaluation:\n" + "✓ Loose coupling — producers and consumers are independent\n" + "✓ Natural fit for async workflows and real-time processing\n" + "✓ Excellent scalability — consumers scale independently\n" + "✗ Event ordering and exactly-once delivery are hard\n" + "✗ Debugging event chains requires correlation IDs\n" + "✗ Schema evolution needs careful versioning\n" + "• Best for: Real-time analytics, IoT, workflow orchestration\n" + "• Key tech: Kafka, Event Hubs, EventGrid, Pub/Sub" + ), + } + key = pattern.strip().lower().replace("-", "").replace("_", "").replace(" ", "") + for k, v in patterns.items(): + if k in key or key in k: + return v + return ( + f"Pattern '{pattern}' not in evaluation database.\n" + f"Available patterns: {', '.join(patterns.keys())}.\n" + "Provide a general assessment based on the pattern name." + ) + + +@lc_tool +def estimate_migration_effort( + source: str, target: str, components: int +) -> str: + """Estimate migration effort from source to target architecture. + source/target: e.g. 'monolith', 'microservices', 'serverless'. + components: number of major components/modules to migrate.""" + # Simulated effort estimation + complexity_map = { + ("monolith", "microservices"): ("HIGH", 18, 45), + ("monolith", "serverless"): ("MEDIUM-HIGH", 12, 35), + ("monolith", "event-driven"): ("MEDIUM", 10, 30), + ("microservices", "serverless"): ("LOW-MEDIUM", 6, 15), + } + key = (source.strip().lower(), target.strip().lower()) + complexity, base_weeks, base_cost_k = complexity_map.get( + key, ("MEDIUM", 8, 25) + ) + weeks = base_weeks + (components * 2) + cost_k = base_cost_k + (components * 8) + return ( + f"Migration Effort Estimate: {source} → {target}\n" + f"─────────────────────────────────────────\n" + f"• Components to migrate: {components}\n" + f"• Complexity: {complexity}\n" + f"• Estimated timeline: {weeks} weeks\n" + f"• Estimated cost: ${cost_k}K–${int(cost_k * 1.4)}K\n" + f"• Recommended team size: {max(3, components // 2)} engineers\n" + f"• Phases: Discovery (2w) → Pilot (4w) → Migration ({weeks - 8}w) → Validation (2w)\n" + f"• Risk factors: Data migration, API compatibility, testing coverage" + ) + + +@lc_tool +def recommend_tech_stack(domain: str, scale: str) -> str: + """Recommend a technology stack for a given domain and scale. + domain: e.g. 'e-commerce', 'fintech', 'healthcare', 'saas'. + scale: 'small', 'medium', 'large', 'enterprise'.""" + stacks = { + "e-commerce": { + "frontend": "Next.js + React + Tailwind CSS", + "backend": "Node.js / Python FastAPI (microservices)", + "database": "PostgreSQL (catalog) + Redis (sessions/cache) + Elasticsearch (search)", + "cloud": "Azure AKS or AWS EKS (Kubernetes)", + "ai_ml": "Azure OpenAI (personalization) + Azure ML (recommendations)", + "messaging": "Azure Service Bus / Kafka (order events)", + "observability": "OpenTelemetry + Grafana + Azure Monitor", + }, + "fintech": { + "frontend": "React + TypeScript + Material UI", + "backend": "Java Spring Boot / .NET (regulatory compliance)", + "database": "PostgreSQL (ACID) + TimescaleDB (time-series)", + "cloud": "Azure (compliance certifications) + Azure Confidential Computing", + "ai_ml": "Azure OpenAI (fraud detection) + custom ML models", + "messaging": "Kafka (event sourcing, audit trail)", + "observability": "Datadog + Azure Monitor + custom audit logging", + }, + "healthcare": { + "frontend": "React + HIPAA-compliant hosting", + "backend": "Python FastAPI / .NET (HL7 FHIR support)", + "database": "Azure Health Data Services (FHIR) + PostgreSQL", + "cloud": "Azure (HIPAA BAA) + Azure Confidential Ledger", + "ai_ml": "Azure OpenAI (clinical notes) + Azure Health Bot", + "messaging": "Azure Service Bus (secure messaging)", + "observability": "Azure Monitor + HIPAA-compliant logging", + }, + } + stack = stacks.get(domain.strip().lower(), stacks["e-commerce"]) + scale_notes = { + "small": "Start with managed services, minimize ops overhead", + "medium": "Introduce Kubernetes, add CI/CD automation", + "large": "Multi-region, auto-scaling, dedicated SRE team", + "enterprise": "Global deployment, multi-cloud strategy, SOC2/ISO compliance", + } + note = scale_notes.get(scale.strip().lower(), scale_notes["medium"]) + lines = [ + f"Tech Stack Recommendation: {domain} ({scale} scale)", + "─" * 50, + ] + for layer, tech in stack.items(): + lines.append(f" {layer.replace('_', '/').title():20s} → {tech}") + lines.append(f"\n Scale guidance: {note}") + return "\n".join(lines) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Build the LangGraph agent (ReAct loop) +# ═══════════════════════════════════════════════════════════════════════════ + +SYSTEM_PROMPT = ( + "You are a Technical Architect with deep expertise in cloud-native systems, " + "distributed architectures, and technology strategy. When asked a question:\n" + "1. Use your tools to gather concrete data (patterns, estimates, tech stacks)\n" + "2. Synthesize the tool outputs into a cohesive, actionable recommendation\n" + "3. Be specific — cite numbers, timelines, and trade-offs\n" + "4. If the conversation has prior context, build on it rather than starting fresh\n" + "Keep responses concise and structured." +) + +tools = [evaluate_architecture_pattern, estimate_migration_effort, recommend_tech_stack] + + +def _build_model() -> AzureChatOpenAI: + return AzureChatOpenAI( + azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], + api_key=os.environ["AZURE_OPENAI_API_KEY"], + azure_deployment=os.environ.get( + "AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1" + ), + api_version=os.environ.get( + "AZURE_OPENAI_API_VERSION", "2025-03-01-preview" + ), + ) + + +def _build_graph(): + """Build a LangGraph ReAct agent with architecture tools.""" + llm = _build_model() + llm_with_tools = llm.bind_tools(tools) + + def agent_node(state: MessagesState): + messages = [SystemMessage(content=SYSTEM_PROMPT)] + state["messages"] + response = llm_with_tools.invoke(messages) + return {"messages": [response]} + + def should_continue(state: MessagesState): + last = state["messages"][-1] + if hasattr(last, "tool_calls") and last.tool_calls: + return "tools" + return END + + graph = StateGraph(MessagesState) + graph.add_node("agent", agent_node) + graph.add_node("tools", ToolNode(tools)) + graph.add_edge(START, "agent") + graph.add_conditional_edges("agent", should_continue) + graph.add_edge("tools", "agent") + return graph.compile() + + +# Compile the graph at module level +langgraph_agent = _build_graph() + + +# ═══════════════════════════════════════════════════════════════════════════ +# Expose via MCP (port 8003 — separate from the MAF server on 8002) +# ═══════════════════════════════════════════════════════════════════════════ + +mcp_server = FastMCP( + "TechnicalArchitect", + stateless_http=True, + json_response=True, + host="0.0.0.0", + port=8003, +) + + +@mcp_server.tool() +async def ask_architect( + question: Annotated[str, "Architecture question or discussion context"], +) -> str: + """Ask the Technical Architect for architecture advice, design patterns, + migration strategies, or technology stack recommendations.""" + result = await langgraph_agent.ainvoke( + {"messages": [HumanMessage(content=question)]} + ) + # Extract the last AI message (skip tool-call messages) + for msg in reversed(result["messages"]): + content = getattr(msg, "content", None) + if content and not getattr(msg, "tool_calls", None): + return content if isinstance(content, str) else str(content) + return "No response generated." + + +# ═══════════════════════════════════════════════════════════════════════════ + +if __name__ == "__main__": + print("=" * 70) + print("🏗️ LangGraph Technical Architect — MCP Server (port 8003)") + print("=" * 70) + print(" Endpoint: http://localhost:8003/mcp") + print(" Tool: ask_architect") + print(" Framework: LangGraph (cross-framework interop via MCP)") + print() + print(" Architecture tools available to the agent:") + print(" • evaluate_architecture_pattern") + print(" • estimate_migration_effort") + print(" • recommend_tech_stack") + print("=" * 70) + mcp_server.run(transport="streamable-http") diff --git a/agentic_ai/agents/mcp_agent_demo/pyproject.toml b/agentic_ai/agents/mcp_agent_demo/pyproject.toml index 892b906f0..92ffae34f 100644 --- a/agentic_ai/agents/mcp_agent_demo/pyproject.toml +++ b/agentic_ai/agents/mcp_agent_demo/pyproject.toml @@ -9,6 +9,9 @@ dependencies = [ "agent-framework-orchestrations>=0.1.0", "azure-identity>=1.19.0", "fastmcp>=3.0.0", + "langchain-core>=0.3.0", + "langchain-openai>=0.3.0", + "langgraph>=0.3.0", "mcp>=1.13.0", "python-dotenv>=1.0.0", "uvicorn>=0.30.0", diff --git a/agentic_ai/agents/mcp_agent_demo/uv.lock b/agentic_ai/agents/mcp_agent_demo/uv.lock index c280bf7cc..f779dbd60 100644 --- a/agentic_ai/agents/mcp_agent_demo/uv.lock +++ b/agentic_ai/agents/mcp_agent_demo/uv.lock @@ -633,6 +633,27 @@ wheels = [ { url = 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b/agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py new file mode 100644 index 000000000..1f638144b --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py @@ -0,0 +1,306 @@ +""" +Part 9 — Group Chat: Local Agent + LangGraph Agent + LLM Planner + +Demonstrates MAF's GroupChatBuilder orchestrating a multi-agent +discussion between: + + 👔 BusinessStrategist — local MAF agent (Azure OpenAI) + 🏗️ TechnicalArchitect — LangGraph agent served via MCP (port 8003) + 🎯 Planner — LLM orchestrator that decides who speaks next + +The Planner examines the conversation after each turn and routes to +the participant whose expertise is most relevant. Each turn is printed +so you can follow the flow of the discussion. + +This is a cross-framework orchestration: + • The BusinessStrategist is a native MAF Agent + • The TechnicalArchitect is a LangGraph ReAct agent exposed via MCP + • MAF's GroupChatBuilder treats both identically — it doesn't know + (or care) that one participant is running LangGraph behind the scenes + +Prerequisites: + mcp_server_langgraph.py must be running on http://localhost:8003/mcp + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python workflow_group_chat.py +""" + +import asyncio +import os +import sys +import uuid +from typing import Any, cast + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import ( + AgentResponse, + AgentResponseUpdate, + BaseAgent, + Content, + MCPStreamableHTTPTool, + Message, +) +from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.orchestrations import GroupChatBuilder + + +# ═══════════════════════════════════════════════════════════════════════════ +# MCPProxyAgent — BaseAgent that bridges to a remote MCP tool +# ═══════════════════════════════════════════════════════════════════════════ + + +class MCPProxyAgent(BaseAgent): + """BaseAgent that forwards conversation context to a remote MCP tool. + + Unlike a normal Agent (which needs an LLM client), this proxy calls + a remote MCP tool directly via call_tool(). The orchestrator treats + it as a regular participant. + """ + + def __init__( + self, + *, + mcp_tool: MCPStreamableHTTPTool, + tool_name: str, + name: str = "mcp_proxy", + description: str | None = None, + param_name: str = "question", + **kwargs: Any, + ) -> None: + super().__init__(name=name, description=description, **kwargs) + self._mcp_tool = mcp_tool + self._tool_name = tool_name + self._param_name = param_name + + # Must be a regular def — GroupChatBuilder iterates with + # ``async for update in agent.run(stream=True)`` + def run(self, messages: Any = None, *, stream: bool = False, **kwargs: Any) -> Any: + if stream: + return self._run_stream(messages) + return self._run_impl(messages) + + async def _run_impl(self, messages: Any) -> AgentResponse: + result_text = await self._call(messages) + return AgentResponse( + messages=[Message("assistant", [result_text], author_name=self.name)], + response_id=f"proxy-{uuid.uuid4().hex[:8]}", + agent_id=self.id, + ) + + async def _run_stream(self, messages: Any): + result_text = await self._call(messages) + yield AgentResponseUpdate( + contents=[Content.from_text(result_text)], + role="assistant", + author_name=self.name, + agent_id=self.id, + response_id=f"proxy-{uuid.uuid4().hex[:8]}", + ) + + async def _call(self, messages: Any) -> str: + """Build conversation context and forward to the MCP tool.""" + input_text = _conversation_context(messages) + result = await self._mcp_tool.call_tool( + self._tool_name, **{self._param_name: input_text} + ) + if isinstance(result, (list, tuple)): + return "\n".join( + c.text for c in result if hasattr(c, "text") and c.text + ) or str(result) + return result.text if hasattr(result, "text") and result.text else str(result) + + +def _conversation_context(messages: Any) -> str: + """Build a conversation context string for the MCP tool. + + In a group chat the orchestrator passes the FULL conversation as a + list[Message]. We format it so the remote agent has full context + and can build on prior turns. + """ + if isinstance(messages, str): + return messages + if isinstance(messages, Message): + return messages.text or "" + if isinstance(messages, (list, tuple)): + parts: list[str] = [] + for m in messages: + if isinstance(m, Message): + name = m.author_name or m.role or "unknown" + text = m.text or "" + if text.strip(): + parts.append(f"[{name}]: {text}") + elif isinstance(m, str) and m.strip(): + parts.append(m) + return "\n\n".join(parts) if parts else str(messages) + return str(messages) if messages else "" + + +# ═══════════════════════════════════════════════════════════════════════════ +# MAIN — Group Chat with LLM Planner +# ═══════════════════════════════════════════════════════════════════════════ + + +async def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + mcp_server_url = "http://localhost:8003/mcp" + + print("=" * 78) + print("🗣️ Group Chat — Local MAF Agent + LangGraph Agent + LLM Planner") + print("=" * 78) + print() + + # ── Connect to the LangGraph MCP server ────────────────────────────── + async with MCPStreamableHTTPTool( + name="technical_architect", + description="Remote Technical Architect (LangGraph) via MCP", + url=mcp_server_url, + ) as mcp_tool: + print(f"🔗 Connected to LangGraph MCP server at {mcp_server_url}") + print(f" Available tools: {[t.name for t in mcp_tool.functions]}") + print() + + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + # ── Participant 1: Local Business Strategist (MAF Agent) ───────── + strategist = client.as_agent( + name="BusinessStrategist", + instructions=( + "You are a Business Strategist specializing in digital " + "transformation and cloud migration. Focus on:\n" + "• Business impact and ROI analysis\n" + "• Customer experience implications\n" + "• Risk mitigation and change management\n" + "• Competitive advantage and market positioning\n" + "• Timeline and budget considerations\n\n" + "Build on what others have said in the conversation. " + "Be concise but insightful — 2-3 paragraphs max." + ), + ) + + # ── Participant 2: Remote Technical Architect (LangGraph via MCP) ─ + architect = MCPProxyAgent( + mcp_tool=mcp_tool, + tool_name="ask_architect", + param_name="question", + name="TechnicalArchitect", + description=( + "Technical Architect providing architecture design, " + "technology stack recommendations, cloud infrastructure " + "planning, and migration strategies. Built with LangGraph, " + "served via MCP." + ), + ) + + # ── Orchestrator: LLM-based Planner ────────────────────────────── + planner = client.as_agent( + name="Planner", + instructions=( + "You are a discussion facilitator for a strategy meeting " + "about cloud migration. Two experts are available:\n\n" + " • BusinessStrategist — business impact, ROI, risk, " + "go-to-market\n" + " • TechnicalArchitect — architecture, tech stack, " + "migration patterns, infrastructure\n\n" + "After each turn, decide who should speak next based on " + "what the conversation needs. Alternate between them to " + "build a comprehensive plan. If both have contributed " + "enough, terminate the discussion." + ), + ) + + # ── Build the GroupChat workflow ────────────────────────────────── + workflow = GroupChatBuilder( + participants=[strategist, architect], + orchestrator_agent=planner, + max_rounds=4, + intermediate_outputs=True, + ).build() + + topic = ( + "Our company is a mid-size e-commerce retailer with a legacy " + "on-premise monolithic Java application serving 2M monthly users. " + "We want to migrate to a cloud-native architecture to improve " + "scalability, reduce operational costs, and enable AI-powered " + "personalization. We have a $500K budget and 12-month timeline. " + "Discuss the strategy, architecture, and implementation approach." + ) + + print("━" * 78) + print("📋 TOPIC") + print("━" * 78) + print(f" {topic}") + print() + print("━" * 78) + print("👥 PARTICIPANTS") + print("━" * 78) + print(" 👔 BusinessStrategist — local MAF agent (Azure OpenAI)") + print(" 🏗️ TechnicalArchitect — LangGraph agent via MCP (port 8003)") + print(" 🎯 Planner — LLM orchestrator (decides who speaks)") + print("━" * 78) + print() + + # ── Run the group chat ─────────────────────────────────────────── + round_num = 0 + async for event in workflow.run(topic, stream=True): + if event.type == "output": + data = event.data + if not isinstance(data, list): + continue + for msg in cast(list[Message], data): + name = msg.author_name or "" + text = msg.text or "" + if not text.strip(): + continue + + # Skip the initial user message echo + if not name or msg.role == "user": + continue + + # Pick the right icon + if name == "BusinessStrategist": + icon = "👔" + framework = "MAF Agent" + elif name == "TechnicalArchitect": + icon = "🏗️" + framework = "LangGraph via MCP" + elif name == "Planner": + icon = "🎯" + framework = "Orchestrator" + else: + icon = "💬" + framework = name + + round_num += 1 + print(f"{'═' * 78}") + print(f" Round {round_num} — {icon} {name} [{framework}]") + print(f"{'═' * 78}") + print() + print(text) + print() + + print("━" * 78) + print(f"✅ Group Chat complete — {round_num} rounds.") + print("━" * 78) + + +if __name__ == "__main__": + asyncio.run(main()) From e93bc7c0c85f4a1a367db91ffd28eb0489e66f7d Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 19:09:55 -0800 Subject: [PATCH 12/37] MCP agent demo: rewrite README as MCP-vs-A2A thesis, delete proxy agent, finalize group chat with inline Planner --- agentic_ai/agents/mcp_agent_demo/README.md | 557 +++++++----------- .../mcp_agent_demo/mcp_server_langgraph.py | 148 +++-- .../mcp_agent_demo/workflow_group_chat.py | 342 +++++++---- .../mcp_agent_demo/workflow_proxy_agent.py | 244 -------- 4 files changed, 568 insertions(+), 723 deletions(-) delete mode 100644 agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md index b0056d49d..321c80561 100644 --- a/agentic_ai/agents/mcp_agent_demo/README.md +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -1,406 +1,307 @@ -# MCP Agent Demo — Agent Framework - -This demo shows nine capabilities of the Microsoft Agent Framework: - -| # | What | Script | -|---|------|--------| -| 1 | **Expose an agent as an MCP server** (stateless HTTP) | `mcp_server.py` | -| 2 | **Consume the agent-powered MCP service** from another agent | `mcp_client_agent.py` | -| 3 | **Proxy agent workflow** — local agent + remote MCP agent | `workflow_proxy_agent.py` | -| 4 | **Stateful agent-as-MCP server** (multi-turn sessions) | `mcp_server_stateful.py` | -| 5 | **Stateful multi-turn client agent** conversation via MCP | `mcp_client_stateful.py` | -| 6 | **Hybrid MCP server** — strict-schema + natural-language tools in one endpoint | `mcp_server_hybrid.py` | -| 7 | **Hybrid client** — demonstrates both tool types in a single incident flow | `mcp_client_hybrid.py` | -| 8 | **LangGraph agent as MCP server** — cross-framework interop | `mcp_server_langgraph.py` | -| 9 | **Group Chat orchestration** — local + LangGraph agent + LLM planner | `workflow_group_chat.py` | +# MCP as the Universal Agent Interop Layer -## Architecture +## The Thesis -### Stateless Mode (Scripts 1–3) +> Every major AI framework speaks MCP today. Instead of introducing +> another protocol for agent-to-agent communication, have teams expose +> their agentic capabilities as MCP tool servers. Behind each tool? +> Whatever they want — multi-agent workflows, RAG pipelines, LLM +> reasoning chains, or agents built with entirely different frameworks. +> The caller doesn't know or care. -Scripts 1 & 2 expose / consume an agent via MCP. Script 3 adds a -SequentialBuilder workflow that chains a local agent with the remote -MCP agent via `MCPProxyAgent`. +This demo series progressively proves that MCP can serve as the **universal +interop layer** between AI agents — even across frameworks, even when the +agents aren't local, even when conversations span multiple turns. -``` -SequentialBuilder Workflow -┌──────────────────┐ text output ┌──────────────────┐ call_tool() ┌───────────────────────┐ -│ Researcher │ ──────────────▶│ MCPProxyAgent │ ─────────────▶│ MCP Server (8002) │ -│ (LLM agent) │ │ (BaseAgent) │ HTTP │ ExpertAdvisor Agent │ -└──────────────────┘ └──────────────────┘ └───────────────────────┘ -``` +## Why MCP, Not A2A? -**Note:** `MCPProxyAgent.run()` must be a regular `def` (not `async def`) — -`SequentialBuilder` iterates with `async for`, which needs an async iterable. +I keep hearing from customers: *"We need A2A for our agents to talk across +teams and frameworks."* -### Stateful Mode (Scripts 4–5) +But do you? -``` -Client Agent (Coordinator) Server (FastMCP v3) -┌───────────────────────────┐ ┌──────────────────────────────────┐ -│ MCPStreamableHTTPTool │ ── session ──→ │ mcp-session-id → AgentSession │ -│ (auto-sends session ID │ preserved │ ↓ │ -│ via mcp-session-id hdr) │ │ Agent.run(msg, session=session) │ -│ │ │ → InMemoryHistoryProvider │ -│ Turn 1: "What risks?" │ ─────────────→ │ → history auto-managed │ -│ Turn 2: "Market data?" │ ─────────────→ │ → builds on prior turns │ -│ Turn 3: "Summarize all" │ ─────────────→ │ → references full context │ -└───────────────────────────┘ └──────────────────────────────────┘ -``` +The latest MCP spec now covers virtually everything A2A does: -**Key difference:** In stateful mode, the server uses PrefectHQ's -[FastMCP v3](https://github.com/PrefectHQ/fastmcp) (default `stateless_http=False`) -and maps each MCP `session_id` to an `AgentSession` that accumulates conversation -history across tool calls. +| Capability | MCP | A2A | +|---|---|---| +| Stateful sessions | ✅ `Mcp-Session-Id` | ✅ | +| Streaming | ✅ SSE | ✅ SSE | +| Long-running tasks (polling, cancellation, TTL) | ✅ *(this was A2A's last edge, now closed)* | ✅ | +| Mid-task user input (elicitation) | ✅ | ❌ | +| OAuth 2.1 + OIDC auth | ✅ | ✅ | +| Structured input/output schemas | ✅ | ✅ | +| Ecosystem adoption | 🟢 Every major LLM platform | 🟡 Growing | -### Hybrid Mode (Scripts 6–7) +Here's the thing people miss: when an agent calls another agent via A2A, +**the LLM still sees it as a tool call**. A2A just wraps it in "agent +identity" semantics. MCP keeps it as what it actually is — a tool with +typed I/O. Less abstraction, less complexity, same result. -``` -┌─────────────────────── Hybrid MCP Server (port 8002) ───────────────────────┐ -│ │ -│ STRICT-SCHEMA TOOLS (machines consume) NL TOOLS (humans consume) │ -│ ┌──────────────────────────────────┐ ┌────────────────────────────┐ │ -│ │ triage_alert │ │ ask_security_advisor │ │ -│ │ raw text → SecurityAlert │ │ free-form Q&A, policy │ │ -│ │ (Pydantic, 12 fields) │ │ advice, best practices │ │ -│ ├──────────────────────────────────┤ ├────────────────────────────┤ │ -│ │ assess_threat │ │ explain_for_customer │ │ -│ │ SecurityAlert → ThreatAssess. │ │ translate technical │ │ -│ │ (MITRE ATT&CK, score 0-100) │ │ incident → plain English │ │ -│ ├──────────────────────────────────┤ │ for executives, techs, │ │ -│ │ create_response │ │ or compliance officers │ │ -│ │ ThreatAssess. → IncidentResp. │ └────────────────────────────┘ │ -│ │ (remediation actions, SLA) │ │ -│ └──────────────────────────────────┘ ┌────────────────────────────┐ │ -│ │ get_session_info │ │ -│ Session state: last_alert, last_threat, │ reset_session │ │ -│ last_response shared across all tools └────────────────────────────┘ │ -└──────────────────────────────────────────────────────────────────────────────┘ -``` +### Where MCP wins -**Key insight:** Real MSP platforms need **both** tool types coexisting: -- Strict-schema tools drive automation (isolation, blocking, SLA timers) — - wrong data means wrong remediation and a customer breach. -- Natural-language tools help humans understand incidents and communicate - with customers — no schema needed, prose is the point. +- **Massive ecosystem** — every major LLM platform speaks MCP today +- **Typed contracts between teams** — no "interpret my natural language" + ambiguity +- **Simpler mental model** — teams expose tools, done -Both share the same session state, so `explain_for_customer` can reference -the `IncidentResponse` that `create_response` just produced. +### Where A2A still makes sense -### Cross-Framework Group Chat (Scripts 8–9) +- Cross-organizational federation with unknown third parties +- When you truly can't define schemas upfront -Scripts 8 & 9 demonstrate **cross-framework interoperability**: a LangGraph -agent is exposed via MCP and participates in a MAF GroupChat alongside a -native MAF agent, orchestrated by an LLM-based planner. +For most enterprise scenarios I've seen? **MCP is the answer.** +Save A2A for when you actually need it. -``` -GroupChatBuilder -┌───────────────────────────────────────────────────────────────────────────────┐ -│ 🎯 Planner (Orchestrator) │ -│ LLM agent that decides who speaks next │ -│ returns: {next_speaker, terminate, reason} │ -└──────────────────┬──────────────────────────────┬────────────────────────────┘ - │ │ - ▼ ▼ -┌──────────────────────────────┐ ┌────────────────────────────────────────────┐ -│ 👔 BusinessStrategist │ │ 🏗️ TechnicalArchitect (MCPProxyAgent) │ -│ Local MAF Agent │ │ ┌──────────────────────────────────────┐ │ -│ (Azure OpenAI) │ │ │ call_tool("ask_architect", ...) │ │ -│ │ │ │ ↓ MCP HTTP (port 8003) │ │ -│ • Business impact & ROI │ │ │ ┌──────────────────────────────────┐ │ │ -│ • Risk mitigation │ │ │ │ LangGraph ReAct Agent │ │ │ -│ • Go-to-market strategy │ │ │ │ • evaluate_architecture_pattern │ │ │ -│ • Customer experience │ │ │ │ • estimate_migration_effort │ │ │ -│ │ │ │ │ • recommend_tech_stack │ │ │ -└──────────────────────────────┘ │ │ └──────────────────────────────────┘ │ │ - │ └──────────────────────────────────────┘ │ - └────────────────────────────────────────────┘ -``` +## What This Demo Proves -**Key insight:** MCP is the universal connector. MAF's GroupChatBuilder -doesn't know (or care) that one participant is LangGraph behind the scenes. -This pattern works with any framework that can expose tools via MCP. +This demo builds the case progressively — each pair of scripts adds one +capability, culminating in a cross-framework multi-agent orchestration +where the MCP boundary is invisible. -## Prerequisites +### Layer 1: Agent-as-a-Service (Scripts 1–2) -- Azure OpenAI credentials in `mcp/.env` (already configured) -- Python 3.12+, `uv` +> **Thesis:** Any agent can be exposed as an MCP tool server. Any other +> agent can consume it. The caller doesn't know what's behind the tool. -## Quick Start +| Script | Role | What it does | +|---|---|---| +| `mcp_server.py` | Server | Wraps a MAF Agent with domain tools (risk analysis, market data, summarization) as an MCP Streamable-HTTP endpoint on port 8002 | +| `mcp_client_agent.py` | Client | A "Coordinator" agent connects to the MCP server and delegates business analysis — it sees the remote agent as just another tool | -### 1. Start the MCP Agent Server +**Key takeaway:** The client agent has no idea what framework, model, or +infrastructure powers the remote tool. It just calls `ask_expert()` and +gets a typed response. This is the foundation of the entire pattern. -```bash -cd agentic_ai/agents/mcp_agent_demo -uv sync -uv run python mcp_server.py -``` +### Layer 2: Stateful Sessions (Scripts 3–4) -This starts an MCP server on `http://localhost:8002/mcp` that exposes an -"Expert Advisor" agent as a tool via the MCP Streamable HTTP transport. +> **Thesis:** MCP sessions (`Mcp-Session-Id`) enable multi-turn +> conversations with remote agents. The server remembers context across +> calls — no client-side history management needed. -### 2. Run the Client Agent (in a second terminal) +| Script | Role | What it does | +|---|---|---| +| `mcp_server_stateful.py` | Server | Each MCP session gets its own `AgentSession` with `InMemoryHistoryProvider` — the agent remembers the full conversation history | +| `mcp_client_stateful.py` | Client | Runs a 3-turn conversation where each follow-up builds on the prior: risks → market data → executive summary | -```bash -cd agentic_ai/agents/mcp_agent_demo -uv run python mcp_client_agent.py -``` +**Key takeaway:** Session statefulness is a protocol-level feature. +`MCPStreamableHTTPTool` sends the `mcp-session-id` header automatically. +The client doesn't manage history — the server does. This is exactly +what you need for remote agent conversations. -This creates a local agent that connects to the MCP server and delegates -questions to the remote Expert Advisor agent. +### Layer 3: Strict + Natural Language Tools (Scripts 5–6) -### 3. Run the Proxy Agent Workflow (in a second terminal) +> **Thesis:** Real enterprise platforms need both machine-consumable +> (strict-schema) and human-consumable (natural-language) tools. MCP +> supports both in the same endpoint, sharing session state. -```bash -cd agentic_ai/agents/mcp_agent_demo -uv run python workflow_proxy_agent.py -``` +| Script | Role | What it does | +|---|---|---| +| `mcp_server_hybrid.py` | Server | Exposes strict-schema tools (`triage_alert` → Pydantic `SecurityAlert`, `assess_threat` → `ThreatAssessment`, `create_response` → `IncidentResponse`) alongside NL tools (`ask_security_advisor`, `explain_for_customer`) — all sharing session state | +| `mcp_client_hybrid.py` | Client | Runs a full SOC incident flow: strict pipeline (triage → assess → respond) then NL advisor & customer notification | -This runs a **SequentialBuilder** workflow that chains a local Researcher -agent with the remote Expert Advisor via `MCPProxyAgent`: -1. **Researcher** → drafts initial analysis -2. **MCPProxyAgent** → forwards output to `ask_expert` via `call_tool()` +**Key takeaway:** Strict-schema tools drive automation (wrong data = +wrong remediation = customer breach). NL tools help humans understand +and communicate. Both coexist in one MCP service, sharing the same +session state. This is how real MSP platforms work. ---- +### Layer 4: Cross-Framework Interop (Scripts 7–8) -### 4. Start the Stateful MCP Agent Server +> **Thesis:** MCP is framework-agnostic. A LangGraph agent behind MCP +> is indistinguishable from a MAF agent behind MCP. The orchestrator +> doesn't know or care — it just calls tools. -```bash -cd agentic_ai/agents/mcp_agent_demo -uv run python mcp_server_stateful.py -``` +| Script | Role | What it does | +|---|---|---| +| `mcp_server_langgraph.py` | Server | A **LangGraph** ReAct agent (Technical Architect) with architecture tools, exposed via MCP on port 8003. Uses `MemorySaver` for stateful conversations — MCP `session_id` maps to LangGraph `thread_id` | +| `workflow_group_chat.py` | Orchestrator | MAF `GroupChatBuilder` orchestrates a multi-agent discussion with **4 agents**: 👔 BusinessStrategist (local MAF), 🏗️ TechnicalArchitect (LangGraph via MCP), 📋 Planner (local MAF), 🎯 Facilitator (LLM orchestrator). Runs a multi-turn conversation — the LangGraph agent remembers prior turns via MemorySaver | -This starts a **stateful** MCP server on `http://localhost:8002/mcp` using -[FastMCP v3](https://github.com/PrefectHQ/fastmcp). Each client session gets -its own `AgentSession`, so the expert agent remembers the full conversation -history across tool calls. +**Key takeaway:** This is the capstone. Three different execution +models (local MAF agent, remote LangGraph agent via MCP, LLM orchestrator) +participate in the same conversation. The `MCPProxyAgent` sends only +the latest message — the LangGraph server keeps full history via +`MemorySaver`. MAF's `GroupChatBuilder` treats all participants +identically. **MCP made the framework boundary invisible.** -Tools exposed: `chat_with_expert`, `get_session_info`, `reset_conversation` +## Architecture -### 5. Run the Stateful Multi-Turn Client (in a second terminal) +### The Pattern: Teams Expose Capabilities as MCP Tool Servers -```bash -cd agentic_ai/agents/mcp_agent_demo -uv run python mcp_client_stateful.py ``` - -This runs a 3-turn conversation where each follow-up builds on the prior: -1. "What are the main risks of expanding into technology?" -2. "Based on that analysis, what does market data look like?" -3. "Summarize everything we discussed into an executive recommendation." - -The remote expert references prior turns because the `mcp-session-id` header -ties all requests to the same server-side `AgentSession`. - ---- - -### 6. Start the Hybrid MCP Server - -```bash -cd agentic_ai/agents/mcp_agent_demo -uv run python mcp_server_hybrid.py +┌─────────────────────────────────────────────────────────────────────────┐ +│ ORCHESTRATING AGENT │ +│ (MAF, LangChain, AutoGen, ...) │ +│ │ +│ "I need business analysis" "I need architecture design" │ +│ ↓ call_tool() ↓ call_tool() │ +└─────────┬───────────────────────────────┬───────────────────────────────┘ + │ MCP Protocol │ + │ (Streamable HTTP) │ + ▼ ▼ +┌──────────────────────┐ ┌──────────────────────────────────┐ +│ MCP Server A │ │ MCP Server B │ +│ (Team Alpha) │ │ (Team Beta) │ +│ │ │ │ +│ Behind the tool: │ │ Behind the tool: │ +│ • MAF Agent │ │ • LangGraph ReAct Agent │ +│ • Azure OpenAI │ │ • MemorySaver checkpointer │ +│ • Domain tools │ │ • Architecture tools │ +│ │ │ │ +│ The caller has │ │ The caller has │ +│ no idea. │ │ no idea. │ +└──────────────────────┘ └──────────────────────────────────┘ ``` -This starts a **hybrid** MCP server on `http://localhost:8002/mcp` that -exposes **both** strict-schema tools and natural-language tools: - -| Tool | Type | Input → Output | -|------|------|----------------| -| `triage_alert` | Strict schema | raw text → `SecurityAlert` (Pydantic) | -| `assess_threat` | Strict schema | `SecurityAlert` → `ThreatAssessment` (Pydantic) | -| `create_response` | Strict schema | `ThreatAssessment` → `IncidentResponse` (Pydantic) | -| `ask_security_advisor` | Natural language | free-form question → prose answer | -| `explain_for_customer` | Natural language | incident context → plain-English summary | -| `get_session_info` | Utility | — → session metadata | -| `reset_session` | Utility | — → clears session state | +### Cross-Framework Group Chat (Scripts 7–8) -Session state is shared: strict tools store their outputs (`last_alert`, -`last_threat`, `last_response`) so natural-language tools can reference them. +``` + GroupChatBuilder +┌─────────────────────────────────────────────────────────────────────────────┐ +│ │ +│ 🎯 Facilitator │ +│ LLM orchestrator — decides who speaks next │ +│ Terminates after Planner delivers the plan │ +│ │ +│ ┌──────────────────┬──────────────────┬──────────────────┐ │ +│ │ │ │ │ │ +│ ▼ ▼ ▼ │ │ +│ ┌──────────────┐ ┌───────────────┐ ┌──────────────┐ │ │ +│ │ 👔 Business │ │ 🏗️ Technical │ │ 📋 Planner │ │ │ +│ │ Strategist │ │ Architect │ │ │ │ │ +│ │ │ │ │ │ Synthesizes │ │ │ +│ │ Local MAF │ │ MCPProxyAgent │ │ discussion │ │ │ +│ │ Agent │ │ │ │ │ into plan │ │ │ +│ │ │ │ │ MCP │ │ │ │ │ +│ │ Business │ │ ▼ │ │ Local MAF │ │ │ +│ │ impact, │ │ ┌──────────┐ │ │ Agent │ │ │ +│ │ ROI, risk │ │ │LangGraph │ │ │ │ │ │ +│ │ │ │ │ReAct │ │ │ │ │ │ +│ │ │ │ │Agent │ │ │ │ │ │ +│ │ │ │ │(port │ │ │ │ │ │ +│ │ │ │ │ 8003) │ │ │ │ │ │ +│ │ │ │ └──────────┘ │ │ │ │ │ +│ └──────────────┘ └───────────────┘ └──────────────┘ │ │ +│ │ +│ Flow: User question → BusinessStrategist → TechnicalArchitect │ +│ → (iterate if needed) → Planner delivers plan → Terminate │ +└─────────────────────────────────────────────────────────────────────────────┘ +``` -### 7. Run the Hybrid Client (in a second terminal) +### Stateful Session Flow -```bash -cd agentic_ai/agents/mcp_agent_demo -uv run python mcp_client_hybrid.py +``` + Client MCP Server (Stateful) + │ │ + │── call_tool("ask", Q1) ─────────▶│ session_id=abc123 + │◀── Response R1 ─────────────────│ store in AgentSession[abc123] + │ │ + │── call_tool("ask", Q2) ─────────▶│ same session_id=abc123 + │◀── Response R2 (references R1) ─│ history: [Q1, R1, Q2] + │ │ + │── call_tool("ask", Q3) ─────────▶│ same session_id=abc123 + │◀── Response R3 (full context) ──│ history: [Q1, R1, Q2, R2, Q3] + │ │ ``` -This runs a 5-step incident flow that uses **both** tool types: +**No client-side history management.** The `mcp-session-id` header ties +all requests to the same server-side session. The client sends one +message at a time — the server remembers everything. -1. **`triage_alert`** (strict) — parse raw SIEM alert → `SecurityAlert` -2. **`assess_threat`** (strict) — analyze alert → `ThreatAssessment` with MITRE ATT&CK mapping -3. **`create_response`** (strict) — build response plan → `IncidentResponse` with remediation actions -4. **`ask_security_advisor`** (NL) — "What compliance obligations apply?" -5. **`explain_for_customer`** (NL) — draft executive-friendly incident notification +## The Key Insight -Strict tools validate with Pydantic (fail fast on bad data). -Natural-language tools produce human-readable prose. -Both share the same MCP session. +When Team Alpha exposes their agent as an MCP tool server, and Team Beta +calls it from their LangGraph pipeline, and Team Gamma calls it from +their AutoGen workflow — **nobody changed any code**. The MCP protocol +handles: ---- +- **Discovery** — what tools are available, what schemas they expect +- **Invocation** — typed input → typed output, no ambiguity +- **State** — session IDs for multi-turn conversations +- **Streaming** — SSE for real-time updates -### 8. Start the LangGraph MCP Server +This is what A2A promises. MCP already delivers it. -```bash -cd agentic_ai/agents/mcp_agent_demo -uv run python mcp_server_langgraph.py -``` +## Quick Start -This starts a **LangGraph-based** Technical Architect agent as an MCP server -on `http://localhost:8003/mcp`. The agent uses a ReAct loop with -architecture-related tools: +### Prerequisites -| Tool | Purpose | -|------|---------| -| `evaluate_architecture_pattern` | Evaluate microservices, monolith, serverless, event-driven patterns | -| `estimate_migration_effort` | Estimate timeline, cost, and team size for a migration | -| `recommend_tech_stack` | Recommend technologies for a given domain and scale | +- Python 3.12+, `uv` +- Azure OpenAI credentials in `mcp/.env` -**Cross-framework interop:** The agent is built entirely with LangGraph -but served via MCP — any MCP client can consume it regardless of framework. +### Running the demos -### 9. Run the Group Chat Orchestration (in a second terminal) +Each layer builds on the previous. Start with Layer 1 and work up. ```bash cd agentic_ai/agents/mcp_agent_demo -uv run python workflow_group_chat.py +uv sync ``` -This runs a multi-agent **GroupChat** discussion using MAF's -`GroupChatBuilder` with an LLM-based planner. Three roles participate: +#### Layer 1: Basic Agent-as-MCP-Service -1. **🎯 Planner** (orchestrator) — decides who speaks next -2. **👔 BusinessStrategist** (local MAF agent) — business impact, ROI, risk -3. **🏗️ TechnicalArchitect** (LangGraph via MCP) — architecture, tech stack - -Each turn shows which agent was selected and their response. The planner -alternates between participants to build a comprehensive strategy. - ---- - -## How It Works - -### Exposing an Agent as MCP Server - -```python -from agent_framework import Agent, tool -from agent_framework.azure import AzureOpenAIChatClient - -# Create an agent with tools -agent = Agent(client=AzureOpenAIChatClient(...), tools=[...]) - -# Convert to MCP server (low-level Server from mcp SDK) -server = agent.as_mcp_server(server_name="ExpertAdvisor") - -# Serve over HTTP using FastMCP's streamable HTTP transport -from mcp.server.fastmcp import FastMCP -mcp_app = FastMCP("ExpertAdvisor", stateless_http=True, json_response=True) -# ... register agent tool ... -mcp_app.run(transport="streamable-http", port=8002) -``` +```bash +# Terminal 1 — start the server +uv run python mcp_server.py # port 8002 -### Consuming MCP Service from Another Agent - -```python -from agent_framework import Agent, MCPStreamableHTTPTool -from agent_framework.azure import AzureOpenAIChatClient - -async with MCPStreamableHTTPTool( - name="expert_advisor", - url="http://localhost:8002/mcp", -) as mcp_tool: - async with Agent( - client=AzureOpenAIChatClient(...), - tools=mcp_tool, - ) as agent: - result = await agent.run("Analyze this business scenario...") +# Terminal 2 — run the client +uv run python mcp_client_agent.py ``` -### Integrating a Remote MCP Agent into a Workflow - -```python -from agent_framework import BaseAgent, MCPStreamableHTTPTool, Message -from agent_framework.orchestrations import SequentialBuilder - -class MCPProxyAgent(BaseAgent): - """Forwards messages to a remote MCP tool via call_tool().""" - def __init__(self, *, mcp_tool, tool_name, param_name="question", **kw): - super().__init__(**kw) - self._mcp_tool, self._tool_name, self._param_name = mcp_tool, tool_name, param_name - - def run(self, messages=None, *, stream=False, **kw): # must be regular def - return self._stream(messages) if stream else self._impl(messages) - - async def _impl(self, messages): - result = await self._mcp_tool.call_tool(self._tool_name, **{self._param_name: text}) - ... +#### Layer 2: Stateful Multi-Turn Sessions -researcher = client.as_agent(name="researcher", instructions="...") -proxy = MCPProxyAgent(mcp_tool=mcp_tool, tool_name="ask_expert", name="proxy") +```bash +# Terminal 1 — start the stateful server (stop Layer 1 first — same port) +uv run python mcp_server_stateful.py # port 8002 -workflow = SequentialBuilder(participants=[researcher, proxy]).build() -result = await workflow.run("Analyze the market for electric bikes") +# Terminal 2 — run the stateful client +uv run python mcp_client_stateful.py ``` -### Stateful Agent-as-MCP Server (Multi-Turn) - -The stateful server uses [FastMCP v3](https://github.com/PrefectHQ/fastmcp) -instead of `mcp.server.fastmcp.FastMCP` from the base MCP SDK. Two things -make multi-turn work: - -1. **Server side:** Each MCP `session_id` maps to an `AgentSession`. - Passing the same session to `agent.run()` lets the framework's - `InMemoryHistoryProvider` accumulate messages automatically. - -2. **Client side:** `MCPStreamableHTTPTool` sends the `mcp-session-id` - header on every request after initialization — no extra code needed. +#### Layer 3: Hybrid Strict + NL Tools -```python -# Server — stateful MCP with session-scoped agent history -from fastmcp import FastMCP # PrefectHQ fastmcp v3 -from fastmcp.server.context import Context -from agent_framework import Agent -from agent_framework._sessions import AgentSession +```bash +# Terminal 1 — start the hybrid server (stop Layers 1–2 first — same port) +uv run python mcp_server_hybrid.py # port 8002 -agent_sessions: dict[str, AgentSession] = {} -mcp_server = FastMCP("StatefulExpert") +# Terminal 2 — run the hybrid client +uv run python mcp_client_hybrid.py +``` -@mcp_server.tool -async def chat_with_expert(message: str, ctx: Context) -> str: - session_id = ctx.session_id - if session_id not in agent_sessions: - agent_sessions[session_id] = AgentSession() - session = agent_sessions[session_id] +#### Layer 4: Cross-Framework Group Chat - async with agent: - response = await agent.run(message, session=session) - return response.text +```bash +# Terminal 1 — start the LangGraph MCP server +uv run python mcp_server_langgraph.py # port 8003 -mcp_server.run(transport="streamable-http", host="0.0.0.0", port=8002) +# Terminal 2 — run the group chat orchestration +uv run python workflow_group_chat.py ``` -```python -# Client — multi-turn conversation (session auto-managed) -from agent_framework import Agent, MCPStreamableHTTPTool - -async with MCPStreamableHTTPTool( - name="expert", url="http://localhost:8002/mcp" -) as mcp_tool: - async with Agent(client=client, tools=mcp_tool) as coordinator: - r1 = await coordinator.run("What are the risks?") # Turn 1 - r2 = await coordinator.run("Show me market data.") # Turn 2 - r3 = await coordinator.run("Summarize everything.") # Turn 3 - # The expert remembers all prior turns ✓ -``` +> **Note:** Layers 1–3 share port 8002 — only run one at a time. +> Layer 4 uses port 8003 and can run alongside any of the others. -## Dependencies +## Technologies Used | Package | Purpose | |---------|---------| -| `agent-framework-core` | Agent, MCPStreamableHTTPTool, tool decorator | -| `agent-framework-orchestrations` | SequentialBuilder, GroupChatBuilder for workflows | -| `mcp` | MCP SDK (used by stateless server via `mcp.server.fastmcp.FastMCP`) | -| `fastmcp` | PrefectHQ FastMCP v3 (used by stateful server — session state support) | -| `langgraph` | LangGraph — stateful agent graphs (used by Script 8) | +| `agent-framework-core` | Microsoft Agent Framework — agents, tools, MCP client | +| `agent-framework-orchestrations` | GroupChatBuilder for multi-agent workflows | +| `fastmcp` | PrefectHQ FastMCP v3 — stateful MCP server with session support | +| `langgraph` | LangGraph — stateful agent graphs with MemorySaver | | `langchain-openai` | Azure OpenAI integration for LangGraph agents | -| `langchain-core` | LangChain core abstractions (messages, tools) | | `python-dotenv` | Load credentials from `mcp/.env` | + +## File Inventory + +``` +mcp_agent_demo/ +├── mcp_server.py # Layer 1: Basic MAF agent as MCP server +├── mcp_client_agent.py # Layer 1: Client that consumes the MCP service +├── mcp_server_stateful.py # Layer 2: Stateful MCP server (session memory) +├── mcp_client_stateful.py # Layer 2: Multi-turn conversation client +├── mcp_server_hybrid.py # Layer 3: Strict-schema + NL tools in one endpoint +├── mcp_client_hybrid.py # Layer 3: SOC incident flow using both tool types +├── mcp_server_langgraph.py # Layer 4: LangGraph agent as MCP server (port 8003) +├── workflow_group_chat.py # Layer 4: GroupChat — MAF + LangGraph via MCP +├── pyproject.toml # Dependencies +└── README.md # This file +``` diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py b/agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py index 516cfa8af..f4c8ed0d1 100644 --- a/agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py @@ -1,23 +1,40 @@ """ -Part 8 — LangGraph Agent as MCP Server (Cross-Framework Interop) - -A LangGraph-based Technical Architect agent exposed as an MCP endpoint. -This demonstrates cross-framework interoperability: the agent is built -entirely with LangGraph (ReAct loop + tools) but served over MCP so -ANY framework — MAF, AutoGen, CrewAI — can consume it as a tool. - -The LangGraph agent has architecture-related tools and uses a ReAct -loop to reason about which tools to call and how to synthesize results. +Part 8 — Stateful LangGraph Agent as MCP Server (Cross-Framework Interop) + +A LangGraph-based Technical Architect agent exposed as a **stateful** +MCP endpoint using PrefectHQ FastMCP v3. Each MCP session gets its own +LangGraph conversation thread (via MemorySaver checkpointer), so the +agent remembers prior turns without the client resending history. + +Cross-framework interoperability: the agent is built entirely with +LangGraph (ReAct loop + tools) but any MCP client can consume it — +MAF, AutoGen, CrewAI — regardless of framework. + +Architecture: + ┌──────────────────────────────────────────────────────┐ + │ FastMCP (stateful, streamable-http, port 8003) │ + │ │ + │ MCP session_id → LangGraph thread_id │ + │ ↓ │ + │ LangGraph ReAct Agent + MemorySaver (conversation) │ + │ • evaluate_architecture_pattern │ + │ • estimate_migration_effort │ + │ • recommend_tech_stack │ + │ │ + │ Tools: │ + │ ask_architect — multi-turn architecture Q&A │ + │ get_session_info — session metadata │ + │ reset_conversation — clear history │ + └──────────────────────────────────────────────────────┘ Prerequisites: - uv sync (installs langgraph, langchain-openai, langchain-core) + uv sync (installs langgraph, langchain-openai, langchain-core, fastmcp) Usage: cd agentic_ai/agents/mcp_agent_demo uv run python mcp_server_langgraph.py """ -import asyncio import os import sys from typing import Annotated @@ -28,12 +45,14 @@ env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") load_dotenv(env_path) +from fastmcp import FastMCP +from fastmcp.server.context import Context from langchain_core.messages import HumanMessage, SystemMessage from langchain_core.tools import tool as lc_tool from langchain_openai import AzureChatOpenAI +from langgraph.checkpoint.memory import MemorySaver from langgraph.graph import END, START, MessagesState, StateGraph from langgraph.prebuilt import ToolNode -from mcp.server.fastmcp import FastMCP # ═══════════════════════════════════════════════════════════════════════════ @@ -103,13 +122,10 @@ def evaluate_architecture_pattern(pattern: str) -> str: @lc_tool -def estimate_migration_effort( - source: str, target: str, components: int -) -> str: +def estimate_migration_effort(source: str, target: str, components: int) -> str: """Estimate migration effort from source to target architecture. source/target: e.g. 'monolith', 'microservices', 'serverless'. components: number of major components/modules to migrate.""" - # Simulated effort estimation complexity_map = { ("monolith", "microservices"): ("HIGH", 18, 45), ("monolith", "serverless"): ("MEDIUM-HIGH", 12, 35), @@ -117,9 +133,7 @@ def estimate_migration_effort( ("microservices", "serverless"): ("LOW-MEDIUM", 6, 15), } key = (source.strip().lower(), target.strip().lower()) - complexity, base_weeks, base_cost_k = complexity_map.get( - key, ("MEDIUM", 8, 25) - ) + complexity, base_weeks, base_cost_k = complexity_map.get(key, ("MEDIUM", 8, 25)) weeks = base_weeks + (components * 2) cost_k = base_cost_k + (components * 8) return ( @@ -188,7 +202,7 @@ def recommend_tech_stack(domain: str, scale: str) -> str: # ═══════════════════════════════════════════════════════════════════════════ -# Build the LangGraph agent (ReAct loop) +# Build the LangGraph agent (ReAct loop with MemorySaver) # ═══════════════════════════════════════════════════════════════════════════ SYSTEM_PROMPT = ( @@ -197,28 +211,29 @@ def recommend_tech_stack(domain: str, scale: str) -> str: "1. Use your tools to gather concrete data (patterns, estimates, tech stacks)\n" "2. Synthesize the tool outputs into a cohesive, actionable recommendation\n" "3. Be specific — cite numbers, timelines, and trade-offs\n" - "4. If the conversation has prior context, build on it rather than starting fresh\n" + "4. You are in a multi-turn conversation. Build on prior context rather than " + "starting fresh — reference earlier discussion points.\n" "Keep responses concise and structured." ) tools = [evaluate_architecture_pattern, estimate_migration_effort, recommend_tech_stack] +# MemorySaver keeps conversation history per thread_id in memory. +# Each MCP session maps to a unique LangGraph thread. +checkpointer = MemorySaver() + def _build_model() -> AzureChatOpenAI: return AzureChatOpenAI( azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], api_key=os.environ["AZURE_OPENAI_API_KEY"], - azure_deployment=os.environ.get( - "AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1" - ), - api_version=os.environ.get( - "AZURE_OPENAI_API_VERSION", "2025-03-01-preview" - ), + azure_deployment=os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1"), + api_version=os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview"), ) def _build_graph(): - """Build a LangGraph ReAct agent with architecture tools.""" + """Build a LangGraph ReAct agent with architecture tools and checkpointer.""" llm = _build_model() llm_with_tools = llm.bind_tools(tools) @@ -239,34 +254,50 @@ def should_continue(state: MessagesState): graph.add_edge(START, "agent") graph.add_conditional_edges("agent", should_continue) graph.add_edge("tools", "agent") - return graph.compile() + # Compile WITH checkpointer — state persisted per thread_id + return graph.compile(checkpointer=checkpointer) # Compile the graph at module level langgraph_agent = _build_graph() +# Track turn count per session +session_turns: dict[str, int] = {} + # ═══════════════════════════════════════════════════════════════════════════ -# Expose via MCP (port 8003 — separate from the MAF server on 8002) +# Expose via stateful MCP (port 8003, PrefectHQ FastMCP) # ═══════════════════════════════════════════════════════════════════════════ -mcp_server = FastMCP( - "TechnicalArchitect", - stateless_http=True, - json_response=True, - host="0.0.0.0", - port=8003, -) +mcp_server = FastMCP("TechnicalArchitect") -@mcp_server.tool() +@mcp_server.tool async def ask_architect( - question: Annotated[str, "Architecture question or discussion context"], + question: Annotated[str, "Architecture question or follow-up message"], + ctx: Context, ) -> str: - """Ask the Technical Architect for architecture advice, design patterns, - migration strategies, or technology stack recommendations.""" + """Ask the Technical Architect for architecture advice. The conversation + is maintained across calls within the same MCP session — just send the + new message, no need to resend history.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — processing message") + + if session_id not in session_turns: + session_turns[session_id] = 0 + await ctx.info("New conversation thread started") + + session_turns[session_id] += 1 + turn = session_turns[session_id] + await ctx.info(f"Processing turn {turn}") + + # Use the MCP session_id as the LangGraph thread_id. + # The MemorySaver checkpointer accumulates messages per thread, + # so the agent sees full conversation history automatically. + config = {"configurable": {"thread_id": session_id}} result = await langgraph_agent.ainvoke( - {"messages": [HumanMessage(content=question)]} + {"messages": [HumanMessage(content=question)]}, + config=config, ) # Extract the last AI message (skip tool-call messages) for msg in reversed(result["messages"]): @@ -276,19 +307,46 @@ async def ask_architect( return "No response generated." +@mcp_server.tool +async def get_session_info(ctx: Context) -> dict: + """Get metadata about the current conversation session.""" + session_id = ctx.session_id + turn_count = session_turns.get(session_id, 0) + return { + "session_id": session_id, + "turn_count": turn_count, + "has_history": turn_count > 0, + } + + +@mcp_server.tool +async def reset_conversation(ctx: Context) -> str: + """Clear the conversation history for the current session.""" + session_id = ctx.session_id + if session_id in session_turns: + del session_turns[session_id] + return f"Conversation cleared for session {session_id[:8]}…" + return "No conversation history to clear." + + # ═══════════════════════════════════════════════════════════════════════════ if __name__ == "__main__": print("=" * 70) - print("🏗️ LangGraph Technical Architect — MCP Server (port 8003)") + print("🏗️ LangGraph Technical Architect — Stateful MCP Server (port 8003)") print("=" * 70) print(" Endpoint: http://localhost:8003/mcp") - print(" Tool: ask_architect") + print(" Transport: Streamable HTTP (stateful sessions)") + print(" Tools: ask_architect, get_session_info, reset_conversation") print(" Framework: LangGraph (cross-framework interop via MCP)") print() print(" Architecture tools available to the agent:") print(" • evaluate_architecture_pattern") print(" • estimate_migration_effort") print(" • recommend_tech_stack") + print() + print(" Session memory: MCP session_id → LangGraph thread_id") + print(" → conversation history maintained server-side") + print(" → clients send only the new message each turn") print("=" * 70) - mcp_server.run(transport="streamable-http") + mcp_server.run(transport="streamable-http", host="0.0.0.0", port=8003) diff --git a/agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py b/agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py index 1f638144b..34d0e3a11 100644 --- a/agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py +++ b/agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py @@ -1,22 +1,30 @@ """ -Part 9 — Group Chat: Local Agent + LangGraph Agent + LLM Planner +Part 9 — Conversational Group Chat: User ↔ Local Agent + LangGraph Agent Demonstrates MAF's GroupChatBuilder orchestrating a multi-agent discussion between: 👔 BusinessStrategist — local MAF agent (Azure OpenAI) 🏗️ TechnicalArchitect — LangGraph agent served via MCP (port 8003) - 🎯 Planner — LLM orchestrator that decides who speaks next - -The Planner examines the conversation after each turn and routes to -the participant whose expertise is most relevant. Each turn is printed -so you can follow the flow of the discussion. - -This is a cross-framework orchestration: - • The BusinessStrategist is a native MAF Agent - • The TechnicalArchitect is a LangGraph ReAct agent exposed via MCP - • MAF's GroupChatBuilder treats both identically — it doesn't know - (or care) that one participant is running LangGraph behind the scenes + 📋 Planner — local MAF agent that synthesizes the plan + 🎯 Facilitator — LLM orchestrator that decides who speaks next + +The Facilitator routes the conversation: experts discuss first, then +the Planner delivers a consolidated plan inline — no separate +synthesis step needed. This is more efficient than post-hoc synthesis. + +Simulates a multi-turn conversation with predefined questions: + 1. User poses an initial topic → experts discuss → Planner delivers plan + 2. User asks a follow-up → experts discuss deeper → Planner updates plan + 3. The TechnicalArchitect (LangGraph) remembers prior turns + via MemorySaver — the proxy sends only the NEW message + +This showcases stateful cross-framework orchestration: + • MCP session provides a persistent connection + • LangGraph MemorySaver keeps conversation history server-side + • MCPProxyAgent sends only the latest message (not full history) + • GroupChatBuilder treats both local and remote agents identically + • Planner delivers the plan as a participant (no extra LLM call) Prerequisites: mcp_server_langgraph.py must be running on http://localhost:8003/mcp @@ -56,11 +64,12 @@ class MCPProxyAgent(BaseAgent): - """BaseAgent that forwards conversation context to a remote MCP tool. + """BaseAgent that forwards the latest message to a remote MCP tool. - Unlike a normal Agent (which needs an LLM client), this proxy calls - a remote MCP tool directly via call_tool(). The orchestrator treats - it as a regular participant. + Because the MCP server is **stateful** (maintains conversation history + per session via MemorySaver), we only need to send the LATEST message + — not the full conversation history. The server accumulates context + automatically. """ def __init__( @@ -104,10 +113,16 @@ async def _run_stream(self, messages: Any): ) async def _call(self, messages: Any) -> str: - """Build conversation context and forward to the MCP tool.""" - input_text = _conversation_context(messages) + """Extract only the LATEST message and forward to the stateful MCP tool. + + The server-side LangGraph agent (with MemorySaver) already has the + full conversation history for this session, so we only send the + new content. This is the key difference from the old implementation + which manually forwarded the entire conversation each turn. + """ + latest_text = _extract_latest_message(messages) result = await self._mcp_tool.call_tool( - self._tool_name, **{self._param_name: input_text} + self._tool_name, **{self._param_name: latest_text} ) if isinstance(result, (list, tuple)): return "\n".join( @@ -116,35 +131,131 @@ async def _call(self, messages: Any) -> str: return result.text if hasattr(result, "text") and result.text else str(result) -def _conversation_context(messages: Any) -> str: - """Build a conversation context string for the MCP tool. +def _extract_latest_message(messages: Any) -> str: + """Extract just the last meaningful message from the conversation. - In a group chat the orchestrator passes the FULL conversation as a - list[Message]. We format it so the remote agent has full context - and can build on prior turns. + The orchestrator passes the full conversation as list[Message]. + We only need the LAST message since the MCP server is stateful + and already has prior turns in its MemorySaver checkpointer. """ if isinstance(messages, str): return messages if isinstance(messages, Message): return messages.text or "" if isinstance(messages, (list, tuple)): - parts: list[str] = [] - for m in messages: + # Walk backwards to find the last message with content + for m in reversed(messages): if isinstance(m, Message): - name = m.author_name or m.role or "unknown" text = m.text or "" if text.strip(): - parts.append(f"[{name}]: {text}") + name = m.author_name or m.role or "someone" + return f"[{name}]: {text}" elif isinstance(m, str) and m.strip(): - parts.append(m) - return "\n\n".join(parts) if parts else str(messages) + return m return str(messages) if messages else "" # ═══════════════════════════════════════════════════════════════════════════ -# MAIN — Group Chat with LLM Planner +# MAIN — Conversational Group Chat with follow-ups +# ═══════════════════════════════════════════════════════════════════════════ + + +async def _run_group_chat( + topic: str, + strategist: Any, + architect: MCPProxyAgent, + planner: Any, + facilitator: Any, + turn_label: str = "Initial Discussion", +) -> int: + """Run one round of group chat. + + Returns: + round_count — number of visible participant messages. + """ + workflow = GroupChatBuilder( + participants=[strategist, architect, planner], + orchestrator_agent=facilitator, + max_rounds=6, + intermediate_outputs=True, + ).build() + + print() + print(f"━{'━' * 76}━") + print(f" 📋 {turn_label}") + print(f"━{'━' * 76}━") + print(f" {topic[:120]}{'…' if len(topic) > 120 else ''}") + print() + + round_num = 0 + async for event in workflow.run(topic, stream=True): + if event.type == "output": + data = event.data + if not isinstance(data, list): + continue + for msg in cast(list[Message], data): + name = msg.author_name or "" + text = msg.text or "" + if not text.strip(): + continue + + # Skip the initial user message echo + if not name or msg.role == "user": + continue + + if name == "BusinessStrategist": + icon, framework = "👔", "MAF Agent" + elif name == "TechnicalArchitect": + icon, framework = "🏗️", "LangGraph via MCP" + elif name == "Planner": + icon, framework = "📋", "MAF Agent" + else: + icon, framework = "💬", name + + round_num += 1 + print(f"{'═' * 78}") + print(f" Round {round_num} — {icon} {name} [{framework}]") + print(f"{'═' * 78}") + print() + print(text) + print() + + return round_num + + + + + +# ═══════════════════════════════════════════════════════════════════════════ +# Predefined conversation — simulates a multi-turn user interaction # ═══════════════════════════════════════════════════════════════════════════ +CONVERSATION = [ + { + "role": "user", + "label": "Initial Topic", + "message": ( + "Our company is a mid-size e-commerce retailer with a legacy " + "on-premise monolithic Java application serving 2M monthly users. " + "We want to migrate to a cloud-native architecture to improve " + "scalability, reduce operational costs, and enable AI-powered " + "personalization. We have a $500K budget and 12-month timeline. " + "Discuss the strategy, architecture, and implementation approach." + ), + }, + { + "role": "user", + "label": "Follow-up: Risk & Phasing", + "message": ( + "Thanks for the plan. I have concerns about risk. " + "What are the biggest risks with this migration, and how " + "should we phase the rollout to minimize disruption to our " + "existing customers? Can we keep the monolith running in " + "parallel during the transition?" + ), + }, +] + async def main() -> None: endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") @@ -159,11 +270,15 @@ async def main() -> None: mcp_server_url = "http://localhost:8003/mcp" print("=" * 78) - print("🗣️ Group Chat — Local MAF Agent + LangGraph Agent + LLM Planner") + print("🗣️ Conversational Group Chat — User ↔ MAF + LangGraph Agents") print("=" * 78) print() + print(" Simulating a multi-turn conversation with predefined questions.") + print(" The TechnicalArchitect (LangGraph) remembers prior turns via") + print(" MemorySaver — the proxy sends only the NEW message each turn.") + print() - # ── Connect to the LangGraph MCP server ────────────────────────────── + # ── Connect to the stateful LangGraph MCP server ───────────────────── async with MCPStreamableHTTPTool( name="technical_architect", description="Remote Technical Architect (LangGraph) via MCP", @@ -171,6 +286,7 @@ async def main() -> None: ) as mcp_tool: print(f"🔗 Connected to LangGraph MCP server at {mcp_server_url}") print(f" Available tools: {[t.name for t in mcp_tool.functions]}") + print(f" Session: stateful (server maintains conversation history)") print() client = AzureOpenAIChatClient( @@ -210,96 +326,110 @@ async def main() -> None: ), ) - # ── Orchestrator: LLM-based Planner ────────────────────────────── + # ── Participant 3: Planner (synthesizes discussion into plan) ──── planner = client.as_agent( name="Planner", instructions=( - "You are a discussion facilitator for a strategy meeting " - "about cloud migration. Two experts are available:\n\n" + "You are a senior project planner participating in a " + "strategy meeting alongside a BusinessStrategist and " + "TechnicalArchitect.\n\n" + "When the facilitator calls on you, synthesize the team's " + "discussion into a structured, actionable plan:\n" + " • Numbered phases with clear deliverables\n" + " • Timeline and budget allocation\n" + " • Key milestones and success metrics\n" + " • Top risks with mitigations\n" + " • Concrete next steps\n\n" + "Incorporate BOTH business and technical recommendations " + "from your teammates. Write it as a professional plan " + "the client can act on. Be comprehensive but concise." + ), + ) + + # ── Orchestrator: Lightweight Facilitator ───────────────────────── + facilitator = client.as_agent( + name="Facilitator", + instructions=( + "You are a discussion facilitator for a strategy meeting. " + "Three experts are available:\n\n" " • BusinessStrategist — business impact, ROI, risk, " "go-to-market\n" " • TechnicalArchitect — architecture, tech stack, " - "migration patterns, infrastructure\n\n" - "After each turn, decide who should speak next based on " - "what the conversation needs. Alternate between them to " - "build a comprehensive plan. If both have contributed " - "enough, terminate the discussion." + "migration patterns, infrastructure\n" + " • Planner — synthesizes discussion into an actionable " + "plan\n\n" + "Your workflow:\n" + " 1. Start with BusinessStrategist for business perspective\n" + " 2. Then TechnicalArchitect for technical depth\n" + " 3. Alternate if needed for a richer discussion\n" + " 4. When both experts have contributed enough, call on " + "Planner to deliver the consolidated plan\n" + " 5. After Planner delivers the plan, TERMINATE\n\n" + "Do NOT let Planner speak until the experts have " + "had a meaningful exchange." ), ) - # ── Build the GroupChat workflow ────────────────────────────────── - workflow = GroupChatBuilder( - participants=[strategist, architect], - orchestrator_agent=planner, - max_rounds=4, - intermediate_outputs=True, - ).build() - - topic = ( - "Our company is a mid-size e-commerce retailer with a legacy " - "on-premise monolithic Java application serving 2M monthly users. " - "We want to migrate to a cloud-native architecture to improve " - "scalability, reduce operational costs, and enable AI-powered " - "personalization. We have a $500K budget and 12-month timeline. " - "Discuss the strategy, architecture, and implementation approach." - ) - - print("━" * 78) - print("📋 TOPIC") - print("━" * 78) - print(f" {topic}") - print() + # ── Print participant info ──────────────────────────────────────── print("━" * 78) print("👥 PARTICIPANTS") print("━" * 78) print(" 👔 BusinessStrategist — local MAF agent (Azure OpenAI)") print(" 🏗️ TechnicalArchitect — LangGraph agent via MCP (port 8003)") - print(" 🎯 Planner — LLM orchestrator (decides who speaks)") + print(" 📋 Planner — local MAF agent (synthesizes plan)") + print(" 🎯 Facilitator — LLM orchestrator (decides who speaks)") print("━" * 78) print() - # ── Run the group chat ─────────────────────────────────────────── - round_num = 0 - async for event in workflow.run(topic, stream=True): - if event.type == "output": - data = event.data - if not isinstance(data, list): - continue - for msg in cast(list[Message], data): - name = msg.author_name or "" - text = msg.text or "" - if not text.strip(): - continue - - # Skip the initial user message echo - if not name or msg.role == "user": - continue - - # Pick the right icon - if name == "BusinessStrategist": - icon = "👔" - framework = "MAF Agent" - elif name == "TechnicalArchitect": - icon = "🏗️" - framework = "LangGraph via MCP" - elif name == "Planner": - icon = "🎯" - framework = "Orchestrator" - else: - icon = "💬" - framework = name - - round_num += 1 - print(f"{'═' * 78}") - print(f" Round {round_num} — {icon} {name} [{framework}]") - print(f"{'═' * 78}") - print() - print(text) - print() - - print("━" * 78) - print(f"✅ Group Chat complete — {round_num} rounds.") - print("━" * 78) + # ── Run the predefined conversation ─────────────────────────────── + total_rounds = 0 + + for turn_num, turn in enumerate(CONVERSATION, 1): + is_last_turn = turn_num == len(CONVERSATION) + + # Print what the "user" is saying + print() + print(f"{'▓' * 78}") + print(f" 👤 USER (Turn {turn_num})") + print(f"{'▓' * 78}") + print(f" {turn['message']}") + print() + + rounds = await _run_group_chat( + turn["message"], strategist, architect, planner, + facilitator, + turn_label=f"Discussion #{turn_num}: {turn['label']}", + ) + total_rounds += rounds + + print("━" * 78) + print(f"✅ Discussion #{turn_num} complete — {rounds} rounds.") + print("━" * 78) + + if not is_last_turn: + print() + print("━" * 78) + print(" 💡 The TechnicalArchitect remembers the prior discussion") + print(" (stateful MCP session with LangGraph MemorySaver)") + print(" Only the NEW question is sent — server keeps history.") + print() + print(" 📝 The user reviews the plan and asks a follow-up...") + print("━" * 78) + + # ── Summary ────────────────────────────────────────────────────── + print() + print("=" * 78) + print(f"📊 Session Summary: {len(CONVERSATION)} discussions, {total_rounds} total rounds") + print("=" * 78) + print(" Key points demonstrated:") + print(" • Multi-turn conversation with predefined questions") + print(" • Planner delivers plan inline — no separate synthesis step") + print(" • Facilitator orchestrates: experts discuss → Planner delivers") + print(" • LangGraph agent remembers prior context (MemorySaver)") + print(" • MCP session provides persistent stateful connection") + print(" • Proxy sends only latest message (server keeps history)") + print(" • Cross-framework: MAF orchestrator + LangGraph agent via MCP") + print("=" * 78) if __name__ == "__main__": diff --git a/agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py b/agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py deleted file mode 100644 index f7ccf71ab..000000000 --- a/agentic_ai/agents/mcp_agent_demo/workflow_proxy_agent.py +++ /dev/null @@ -1,244 +0,0 @@ -""" -Part 3 — Proxy Agent Workflow: Direct MCP Tool Invocation Without LLM - -Demonstrates a "proxy agent" pattern where a SequentialBuilder workflow -calls a remote MCP agent DIRECTLY — no intermediate LLM reasoning step. - -Traditional approach: - Researcher (LLM) → Expert Agent (LLM reasons → picks tool) → MCP - ↑ unnecessary LLM call — extra cost & latency - -Proxy agent approach: - Researcher (LLM) → MCPProxyAgent (direct call_tool()) → MCP - ↑ zero LLM overhead — deterministic routing - -MCPProxyAgent subclasses BaseAgent (not Agent) — no LLM client. -Its run() directly calls a known MCP tool by name. The orchestrator -treats it as a normal participant, but internally it's just a -transparent bridge to the MCP endpoint. - -When to use: - - You KNOW which MCP tool to call (no routing decision needed) - - You want deterministic routing with zero hallucination risk - - You're optimizing for latency and cost in high-throughput pipelines - - Cross-team integration where the remote service is a black box - -Prerequisites: - mcp_server.py must be running on http://localhost:8002/mcp - -Usage: - cd agentic_ai/agents/mcp_agent_demo - uv run python workflow_proxy_agent.py -""" - -import asyncio -import os -import sys -import uuid -from typing import Any, cast - -from dotenv import load_dotenv - -# Load credentials from the shared mcp/.env -env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") -load_dotenv(env_path) - -from agent_framework import ( - AgentResponse, - AgentResponseUpdate, - BaseAgent, - Content, - MCPStreamableHTTPTool, - Message, -) -from agent_framework.azure import AzureOpenAIChatClient -from agent_framework.orchestrations import SequentialBuilder - - -# ═══════════════════════════════════════════════════════════════════════════ -# MCPProxyAgent — BaseAgent that forwards to a remote MCP tool -# ═══════════════════════════════════════════════════════════════════════════ - - -class MCPProxyAgent(BaseAgent): - """BaseAgent that forwards messages to a remote MCP tool via call_tool().""" - - def __init__( - self, - *, - mcp_tool: MCPStreamableHTTPTool, - tool_name: str, - name: str = "mcp_proxy", - description: str | None = None, - param_name: str = "question", - **kwargs: Any, - ) -> None: - super().__init__(name=name, description=description, **kwargs) - self._mcp_tool = mcp_tool - self._tool_name = tool_name - self._param_name = param_name - - def run(self, messages: Any = None, *, stream: bool = False, **kwargs: Any) -> Any: - """Forward input to the MCP tool — no LLM involved. - - Must be a regular def (not async def) because SequentialBuilder - does ``async for update in agent.run(stream=True)``. - """ - if stream: - return self._run_stream(messages) - return self._run_impl(messages) - - async def _run_impl(self, messages: Any) -> AgentResponse: - result_text = await self._call(messages) - return AgentResponse( - messages=[Message("assistant", [result_text], author_name=self.name)], - response_id=f"proxy-{uuid.uuid4().hex[:8]}", - agent_id=self.id, - ) - - async def _run_stream(self, messages: Any): - result_text = await self._call(messages) - yield AgentResponseUpdate( - contents=[Content.from_text(result_text)], - role="assistant", - author_name=self.name, - agent_id=self.id, - response_id=f"proxy-{uuid.uuid4().hex[:8]}", - ) - - async def _call(self, messages: Any) -> str: - """Extract input text, call the MCP tool, return result as string.""" - input_text = _last_text(messages) - result = await self._mcp_tool.call_tool( - self._tool_name, **{self._param_name: input_text} - ) - # call_tool returns Content objects — extract their .text - if isinstance(result, (list, tuple)): - return "\n".join( - c.text for c in result if hasattr(c, "text") and c.text - ) or str(result) - return result.text if hasattr(result, "text") and result.text else str(result) - - -def _last_text(messages: Any) -> str: - """Get the last agent's text output from the orchestrator's message list.""" - if isinstance(messages, str): - return messages - if isinstance(messages, Message): - return messages.text or "" - if isinstance(messages, (list, tuple)): - for m in reversed(messages): - text = m.text if isinstance(m, Message) else str(m) - if text and text.strip(): - return text - return str(messages) if messages else "" - - -# ═══════════════════════════════════════════════════════════════════════════ -# MAIN — demonstrate proxy agent in a SequentialBuilder workflow -# ═══════════════════════════════════════════════════════════════════════════ - - -async def main() -> None: - endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") - api_key = os.environ.get("AZURE_OPENAI_API_KEY") - deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") - api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") - - if not endpoint or not api_key: - print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") - sys.exit(1) - - mcp_server_url = "http://localhost:8002/mcp" - - print("=" * 78) - print("🔀 Proxy Agent Workflow — Remote MCP Agent Integration") - print("=" * 78) - print() - - # ── Connect to the MCP server ─────────────────────────────────────── - async with MCPStreamableHTTPTool( - name="expert_advisor", - description="Remote Expert Advisor via MCP", - url=mcp_server_url, - ) as mcp_tool: - print(f"🔗 Connected to MCP server at {mcp_server_url}") - print(f" Available tools: {[t.name for t in mcp_tool.functions]}") - print() - - # ── Set up agents ─────────────────────────────────────────────── - - client = AzureOpenAIChatClient( - api_key=api_key, - endpoint=endpoint, - deployment_name=deployment, - api_version=api_version, - ) - - # Agent 1: Local Researcher (uses LLM) - researcher = client.as_agent( - name="researcher", - instructions=( - "You are a market researcher. Given a business topic, provide a " - "concise initial analysis covering opportunity, competition, and " - "key trends. Keep it to 3-4 paragraphs. Your output will be " - "reviewed by an expert advisor next." - ), - ) - - # Agent 2: MCPProxyAgent — forwards to MCP - proxy_expert = MCPProxyAgent( - mcp_tool=mcp_tool, - tool_name="ask_expert", - param_name="question", - name="proxy_expert", - description="Proxy that calls the remote Expert Advisor MCP tool", - ) - - # ── Build workflow ────────────────────────────────────────────── - workflow = SequentialBuilder( - participants=[researcher, proxy_expert], - ).build() - - topic = "Expanding a mid-size SaaS company into the healthcare AI market" - - print("━" * 78) - print(f"📋 Topic: {topic}") - print("━" * 78) - print(" Step 1: Researcher → draft analysis") - print(" Step 2: MCPProxyAgent → forward to remote expert via MCP") - print("━" * 78) - print() - - # ── Run the workflow ──────────────────────────────────────────── - outputs: list[list[Message]] = [] - async for event in workflow.run(topic, stream=True): - if event.type == "output": - outputs.append(cast(list[Message], event.data)) - - # ── Display results ───────────────────────────────────────────── - if outputs: - print("=" * 78) - print("📊 WORKFLOW RESULTS") - print("=" * 78) - for msg in outputs[-1]: - name = msg.author_name or "unknown" - if name == "researcher": - label = "🔬 Researcher" - elif name == "proxy_expert": - label = "🎯 Proxy Expert (via MCP)" - else: - label = f"👤 {name}" - print(f"\n{'─' * 78}") - print(f"{label}:") - print(f"{'─' * 78}") - print(msg.text) - - print() - print("=" * 78) - print("✅ Workflow complete.") - print("=" * 78) - - -if __name__ == "__main__": - asyncio.run(main()) From c4af73330293a094727eda131839253245682e41 Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 19:16:51 -0800 Subject: [PATCH 13/37] README: Mermaid diagrams, professional tone, remove LinkedIn teasing language --- agentic_ai/agents/mcp_agent_demo/README.md | 447 ++++++++++++--------- 1 file changed, 250 insertions(+), 197 deletions(-) diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md index 321c80561..4f00a4bd8 100644 --- a/agentic_ai/agents/mcp_agent_demo/README.md +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -1,307 +1,360 @@ # MCP as the Universal Agent Interop Layer -## The Thesis +> Have teams expose their agentic capabilities as MCP tool servers. +> Behind each tool? Whatever they want — multi-agent workflows, RAG +> pipelines, LLM reasoning chains, or agents built with entirely +> different frameworks. The caller doesn't know or care. -> Every major AI framework speaks MCP today. Instead of introducing -> another protocol for agent-to-agent communication, have teams expose -> their agentic capabilities as MCP tool servers. Behind each tool? -> Whatever they want — multi-agent workflows, RAG pipelines, LLM -> reasoning chains, or agents built with entirely different frameworks. -> The caller doesn't know or care. +This demo progressively proves that **MCP can serve as the universal +interop layer** between AI agents — across frameworks, across machines, +across multi-turn conversations. -This demo series progressively proves that MCP can serve as the **universal -interop layer** between AI agents — even across frameworks, even when the -agents aren't local, even when conversations span multiple turns. +## Why MCP for Agent-to-Agent Communication? -## Why MCP, Not A2A? - -I keep hearing from customers: *"We need A2A for our agents to talk across -teams and frameworks."* - -But do you? - -The latest MCP spec now covers virtually everything A2A does: +The MCP specification now covers virtually every capability needed for +agent-to-agent communication: | Capability | MCP | A2A | |---|---|---| | Stateful sessions | ✅ `Mcp-Session-Id` | ✅ | | Streaming | ✅ SSE | ✅ SSE | -| Long-running tasks (polling, cancellation, TTL) | ✅ *(this was A2A's last edge, now closed)* | ✅ | +| Long-running tasks (polling, cancellation, TTL) | ✅ | ✅ | | Mid-task user input (elicitation) | ✅ | ❌ | | OAuth 2.1 + OIDC auth | ✅ | ✅ | | Structured input/output schemas | ✅ | ✅ | | Ecosystem adoption | 🟢 Every major LLM platform | 🟡 Growing | -Here's the thing people miss: when an agent calls another agent via A2A, -**the LLM still sees it as a tool call**. A2A just wraps it in "agent -identity" semantics. MCP keeps it as what it actually is — a tool with -typed I/O. Less abstraction, less complexity, same result. - -### Where MCP wins - -- **Massive ecosystem** — every major LLM platform speaks MCP today -- **Typed contracts between teams** — no "interpret my natural language" - ambiguity -- **Simpler mental model** — teams expose tools, done +When an agent calls another agent via A2A, the LLM still sees it as a +tool call. A2A wraps it in "agent identity" semantics. MCP keeps it as +what it actually is — **a tool with typed I/O**. Less abstraction, less +complexity, same result. -### Where A2A still makes sense +**Where MCP wins:** +- Massive ecosystem — every major LLM platform speaks MCP +- Typed contracts between teams — no "interpret my natural language" ambiguity +- Simpler mental model — teams expose tools, done +**Where A2A still makes sense:** - Cross-organizational federation with unknown third parties - When you truly can't define schemas upfront -For most enterprise scenarios I've seen? **MCP is the answer.** -Save A2A for when you actually need it. - ## What This Demo Proves -This demo builds the case progressively — each pair of scripts adds one -capability, culminating in a cross-framework multi-agent orchestration -where the MCP boundary is invisible. +Each layer adds one capability, building to a cross-framework +multi-agent orchestration where the MCP boundary is invisible. + +```mermaid +graph LR + L1["Layer 1\nAgent-as-a-Service"] + L2["Layer 2\nStateful Sessions"] + L3["Layer 3\nStrict + NL Tools"] + L4["Layer 4\nCross-Framework\nOrchestration"] + + L1 --> L2 --> L3 --> L4 + + style L1 fill:#e3f2fd,stroke:#1565c0 + style L2 fill:#e8f5e9,stroke:#2e7d32 + style L3 fill:#fff3e0,stroke:#e65100 + style L4 fill:#fce4ec,stroke:#c62828 +``` + +--- ### Layer 1: Agent-as-a-Service (Scripts 1–2) -> **Thesis:** Any agent can be exposed as an MCP tool server. Any other -> agent can consume it. The caller doesn't know what's behind the tool. +> Any agent can be exposed as an MCP tool server. Any other agent can +> consume it. The caller doesn't know what's behind the tool. + +```mermaid +sequenceDiagram + participant Client as 🤖 Coordinator Agent
(MAF) + participant MCP as MCP Protocol
(Streamable HTTP) + participant Server as 🧠 Expert Advisor Agent
(MAF, port 8002) -| Script | Role | What it does | + Client->>MCP: call_tool("ask_expert", question) + MCP->>Server: HTTP POST /mcp + Server-->>MCP: Typed response + MCP-->>Client: Result text + + Note over Client,Server: Client has no idea what framework,
model, or infrastructure powers the tool +``` + +| Script | Role | Description | |---|---|---| -| `mcp_server.py` | Server | Wraps a MAF Agent with domain tools (risk analysis, market data, summarization) as an MCP Streamable-HTTP endpoint on port 8002 | -| `mcp_client_agent.py` | Client | A "Coordinator" agent connects to the MCP server and delegates business analysis — it sees the remote agent as just another tool | +| `mcp_server.py` | Server | MAF Agent with domain tools exposed as MCP endpoint on port 8002 | +| `mcp_client_agent.py` | Client | Coordinator agent that delegates to the remote agent via MCP | -**Key takeaway:** The client agent has no idea what framework, model, or -infrastructure powers the remote tool. It just calls `ask_expert()` and -gets a typed response. This is the foundation of the entire pattern. +--- ### Layer 2: Stateful Sessions (Scripts 3–4) -> **Thesis:** MCP sessions (`Mcp-Session-Id`) enable multi-turn -> conversations with remote agents. The server remembers context across -> calls — no client-side history management needed. +> MCP sessions (`Mcp-Session-Id`) enable multi-turn conversations with +> remote agents. The server remembers context — no client-side history needed. -| Script | Role | What it does | -|---|---|---| -| `mcp_server_stateful.py` | Server | Each MCP session gets its own `AgentSession` with `InMemoryHistoryProvider` — the agent remembers the full conversation history | -| `mcp_client_stateful.py` | Client | Runs a 3-turn conversation where each follow-up builds on the prior: risks → market data → executive summary | +```mermaid +sequenceDiagram + participant Client as 🤖 Client Agent + participant Server as 🧠 Stateful MCP Server
(port 8002) -**Key takeaway:** Session statefulness is a protocol-level feature. -`MCPStreamableHTTPTool` sends the `mcp-session-id` header automatically. -The client doesn't manage history — the server does. This is exactly -what you need for remote agent conversations. + Client->>Server: call_tool("chat", Q1)
Mcp-Session-Id: abc123 + Server-->>Client: R1 + Note right of Server: AgentSession[abc123]
history: [Q1, R1] -### Layer 3: Strict + Natural Language Tools (Scripts 5–6) + Client->>Server: call_tool("chat", Q2)
Mcp-Session-Id: abc123 + Server-->>Client: R2 (references R1) + Note right of Server: history: [Q1, R1, Q2, R2] -> **Thesis:** Real enterprise platforms need both machine-consumable -> (strict-schema) and human-consumable (natural-language) tools. MCP -> supports both in the same endpoint, sharing session state. + Client->>Server: call_tool("chat", Q3)
Mcp-Session-Id: abc123 + Server-->>Client: R3 (full context) + Note right of Server: history: [Q1..R2, Q3, R3] +``` -| Script | Role | What it does | +| Script | Role | Description | |---|---|---| -| `mcp_server_hybrid.py` | Server | Exposes strict-schema tools (`triage_alert` → Pydantic `SecurityAlert`, `assess_threat` → `ThreatAssessment`, `create_response` → `IncidentResponse`) alongside NL tools (`ask_security_advisor`, `explain_for_customer`) — all sharing session state | -| `mcp_client_hybrid.py` | Client | Runs a full SOC incident flow: strict pipeline (triage → assess → respond) then NL advisor & customer notification | +| `mcp_server_stateful.py` | Server | Each MCP session → own `AgentSession` with accumulated history | +| `mcp_client_stateful.py` | Client | 3-turn conversation: risks → market data → executive summary | -**Key takeaway:** Strict-schema tools drive automation (wrong data = -wrong remediation = customer breach). NL tools help humans understand -and communicate. Both coexist in one MCP service, sharing the same -session state. This is how real MSP platforms work. +--- -### Layer 4: Cross-Framework Interop (Scripts 7–8) +### Layer 3: Strict + Natural Language Tools (Scripts 5–6) -> **Thesis:** MCP is framework-agnostic. A LangGraph agent behind MCP -> is indistinguishable from a MAF agent behind MCP. The orchestrator -> doesn't know or care — it just calls tools. +> Real enterprise platforms need both machine-consumable (strict-schema) +> and human-consumable (natural-language) tools in the same endpoint. + +```mermaid +graph TB + subgraph MCP["Hybrid MCP Server (port 8002)"] + direction TB + subgraph Strict["🔧 Strict-Schema Tools"] + T1["triage_alert
raw text → SecurityAlert"] + T2["assess_threat
SecurityAlert → ThreatAssessment"] + T3["create_response
ThreatAssessment → IncidentResponse"] + end + subgraph NL["💬 Natural Language Tools"] + T4["ask_security_advisor
free-form Q&A"] + T5["explain_for_customer
incident → plain English"] + end + SS[("Shared Session State
last_alert, last_threat,
last_response")] + end + + T1 --> T2 --> T3 + T3 -.-> SS + SS -.-> T4 + SS -.-> T5 + + style Strict fill:#e8f5e9,stroke:#2e7d32 + style NL fill:#e3f2fd,stroke:#1565c0 + style SS fill:#fff9c4,stroke:#f9a825 +``` -| Script | Role | What it does | +| Script | Role | Description | |---|---|---| -| `mcp_server_langgraph.py` | Server | A **LangGraph** ReAct agent (Technical Architect) with architecture tools, exposed via MCP on port 8003. Uses `MemorySaver` for stateful conversations — MCP `session_id` maps to LangGraph `thread_id` | -| `workflow_group_chat.py` | Orchestrator | MAF `GroupChatBuilder` orchestrates a multi-agent discussion with **4 agents**: 👔 BusinessStrategist (local MAF), 🏗️ TechnicalArchitect (LangGraph via MCP), 📋 Planner (local MAF), 🎯 Facilitator (LLM orchestrator). Runs a multi-turn conversation — the LangGraph agent remembers prior turns via MemorySaver | +| `mcp_server_hybrid.py` | Server | Pydantic-validated strict tools + NL tools, shared session state | +| `mcp_client_hybrid.py` | Client | Full SOC incident flow using both tool types in sequence | + +--- + +### Layer 4: Cross-Framework Orchestration (Scripts 7–8) + +> MCP is framework-agnostic. A LangGraph agent behind MCP is +> indistinguishable from a MAF agent. The orchestrator doesn't +> know or care. + +```mermaid +graph TB + User["👤 User"] + User --> GC + + subgraph GC["GroupChatBuilder (MAF Orchestration)"] + Fac["🎯 Facilitator
LLM Orchestrator
decides who speaks"] + + Fac --> BS + Fac --> TA + Fac --> PL + + BS["👔 BusinessStrategist
Local MAF Agent
business impact, ROI, risk"] + + subgraph MCP_Bridge["MCP Boundary (port 8003)"] + TA["🏗️ TechnicalArchitect
MCPProxyAgent"] + LG["LangGraph ReAct Agent
+ MemorySaver"] + TA -->|"call_tool(ask_architect)"| LG + end + + PL["📋 Planner
Local MAF Agent
synthesizes plan"] + end + + style GC fill:#fafafa,stroke:#333 + style MCP_Bridge fill:#fff3e0,stroke:#e65100 + style BS fill:#e3f2fd,stroke:#1565c0 + style PL fill:#e8f5e9,stroke:#2e7d32 + style TA fill:#fff3e0,stroke:#e65100 + style LG fill:#fce4ec,stroke:#c62828 + style Fac fill:#f3e5f5,stroke:#6a1b9a +``` -**Key takeaway:** This is the capstone. Three different execution -models (local MAF agent, remote LangGraph agent via MCP, LLM orchestrator) -participate in the same conversation. The `MCPProxyAgent` sends only -the latest message — the LangGraph server keeps full history via -`MemorySaver`. MAF's `GroupChatBuilder` treats all participants -identically. **MCP made the framework boundary invisible.** +**Conversation flow:** + +```mermaid +sequenceDiagram + participant U as 👤 User + participant F as 🎯 Facilitator + participant BS as 👔 BusinessStrategist
(MAF) + participant TA as 🏗️ TechnicalArchitect
(LangGraph via MCP) + participant PL as 📋 Planner
(MAF) + + U->>F: "Migrate our e-commerce to cloud" + F->>BS: Your turn + BS-->>F: Business perspective & ROI + F->>TA: Your turn + Note right of TA: Proxy sends only latest msg
LangGraph MemorySaver keeps history + TA-->>F: Architecture & tech stack + F->>PL: Synthesize the plan + PL-->>F: Consolidated action plan + F-->>U: ✅ Discussion complete + + U->>F: "What about risks and phasing?" + F->>BS: Your turn + BS-->>F: Risk analysis + F->>TA: Your turn + Note right of TA: Remembers prior discussion
via MemorySaver + TA-->>F: Migration phasing strategy + F->>PL: Update the plan + PL-->>F: Revised plan with risk mitigations + F-->>U: ✅ Discussion complete +``` -## Architecture +| Script | Role | Description | +|---|---|---| +| `mcp_server_langgraph.py` | Server | LangGraph ReAct agent as MCP server (port 8003), `MemorySaver` for statefulness | +| `workflow_group_chat.py` | Orchestrator | MAF GroupChat: 3 participants + LLM facilitator, multi-turn with predefined questions | -### The Pattern: Teams Expose Capabilities as MCP Tool Servers +**This is the capstone.** Three execution models (local MAF, remote +LangGraph via MCP, LLM orchestrator) participate in the same conversation. +MAF's `GroupChatBuilder` treats all participants identically. **MCP made +the framework boundary invisible.** -``` -┌─────────────────────────────────────────────────────────────────────────┐ -│ ORCHESTRATING AGENT │ -│ (MAF, LangChain, AutoGen, ...) │ -│ │ -│ "I need business analysis" "I need architecture design" │ -│ ↓ call_tool() ↓ call_tool() │ -└─────────┬───────────────────────────────┬───────────────────────────────┘ - │ MCP Protocol │ - │ (Streamable HTTP) │ - ▼ ▼ -┌──────────────────────┐ ┌──────────────────────────────────┐ -│ MCP Server A │ │ MCP Server B │ -│ (Team Alpha) │ │ (Team Beta) │ -│ │ │ │ -│ Behind the tool: │ │ Behind the tool: │ -│ • MAF Agent │ │ • LangGraph ReAct Agent │ -│ • Azure OpenAI │ │ • MemorySaver checkpointer │ -│ • Domain tools │ │ • Architecture tools │ -│ │ │ │ -│ The caller has │ │ The caller has │ -│ no idea. │ │ no idea. │ -└──────────────────────┘ └──────────────────────────────────┘ -``` +--- -### Cross-Framework Group Chat (Scripts 7–8) +## The Core Pattern -``` - GroupChatBuilder -┌─────────────────────────────────────────────────────────────────────────────┐ -│ │ -│ 🎯 Facilitator │ -│ LLM orchestrator — decides who speaks next │ -│ Terminates after Planner delivers the plan │ -│ │ -│ ┌──────────────────┬──────────────────┬──────────────────┐ │ -│ │ │ │ │ │ -│ ▼ ▼ ▼ │ │ -│ ┌──────────────┐ ┌───────────────┐ ┌──────────────┐ │ │ -│ │ 👔 Business │ │ 🏗️ Technical │ │ 📋 Planner │ │ │ -│ │ Strategist │ │ Architect │ │ │ │ │ -│ │ │ │ │ │ Synthesizes │ │ │ -│ │ Local MAF │ │ MCPProxyAgent │ │ discussion │ │ │ -│ │ Agent │ │ │ │ │ into plan │ │ │ -│ │ │ │ │ MCP │ │ │ │ │ -│ │ Business │ │ ▼ │ │ Local MAF │ │ │ -│ │ impact, │ │ ┌──────────┐ │ │ Agent │ │ │ -│ │ ROI, risk │ │ │LangGraph │ │ │ │ │ │ -│ │ │ │ │ReAct │ │ │ │ │ │ -│ │ │ │ │Agent │ │ │ │ │ │ -│ │ │ │ │(port │ │ │ │ │ │ -│ │ │ │ │ 8003) │ │ │ │ │ │ -│ │ │ │ └──────────┘ │ │ │ │ │ -│ └──────────────┘ └───────────────┘ └──────────────┘ │ │ -│ │ -│ Flow: User question → BusinessStrategist → TechnicalArchitect │ -│ → (iterate if needed) → Planner delivers plan → Terminate │ -└─────────────────────────────────────────────────────────────────────────────┘ -``` +```mermaid +graph TB + subgraph Orchestrator["Orchestrating Agent (any framework)"] + O["MAF / LangChain / AutoGen / CrewAI"] + end -### Stateful Session Flow + O -->|"call_tool()"| MCP1 + O -->|"call_tool()"| MCP2 + O -->|"call_tool()"| MCP3 -``` - Client MCP Server (Stateful) - │ │ - │── call_tool("ask", Q1) ─────────▶│ session_id=abc123 - │◀── Response R1 ─────────────────│ store in AgentSession[abc123] - │ │ - │── call_tool("ask", Q2) ─────────▶│ same session_id=abc123 - │◀── Response R2 (references R1) ─│ history: [Q1, R1, Q2] - │ │ - │── call_tool("ask", Q3) ─────────▶│ same session_id=abc123 - │◀── Response R3 (full context) ──│ history: [Q1, R1, Q2, R2, Q3] - │ │ -``` + subgraph MCP1["MCP Server — Team Alpha"] + A1["MAF Agent\n+ Azure OpenAI\n+ Domain Tools"] + end -**No client-side history management.** The `mcp-session-id` header ties -all requests to the same server-side session. The client sends one -message at a time — the server remembers everything. + subgraph MCP2["MCP Server — Team Beta"] + A2["LangGraph ReAct Agent\n+ MemorySaver\n+ Architecture Tools"] + end -## The Key Insight + subgraph MCP3["MCP Server — Team Gamma"] + A3["Any Framework\n+ Any Model\n+ Any Tools"] + end -When Team Alpha exposes their agent as an MCP tool server, and Team Beta -calls it from their LangGraph pipeline, and Team Gamma calls it from -their AutoGen workflow — **nobody changed any code**. The MCP protocol -handles: + Note1["The caller has no idea\nwhat's behind the tool"] -- **Discovery** — what tools are available, what schemas they expect -- **Invocation** — typed input → typed output, no ambiguity -- **State** — session IDs for multi-turn conversations -- **Streaming** — SSE for real-time updates + style Orchestrator fill:#f3e5f5,stroke:#6a1b9a + style MCP1 fill:#e3f2fd,stroke:#1565c0 + style MCP2 fill:#fff3e0,stroke:#e65100 + style MCP3 fill:#e8f5e9,stroke:#2e7d32 +``` -This is what A2A promises. MCP already delivers it. +When Team Alpha exposes their agent as an MCP tool server, and Team Beta +calls it from LangGraph, and Team Gamma calls it from AutoGen — **nobody +changed any code**. The MCP protocol handles discovery, invocation, +state, and streaming. This is what A2A promises. MCP already delivers it. ## Quick Start ### Prerequisites -- Python 3.12+, `uv` +- Python 3.12+, [uv](https://docs.astral.sh/uv/) - Azure OpenAI credentials in `mcp/.env` ### Running the demos -Each layer builds on the previous. Start with Layer 1 and work up. - ```bash cd agentic_ai/agents/mcp_agent_demo uv sync ``` -#### Layer 1: Basic Agent-as-MCP-Service +#### Layer 1 — Basic Agent-as-MCP-Service ```bash -# Terminal 1 — start the server +# Terminal 1 uv run python mcp_server.py # port 8002 -# Terminal 2 — run the client +# Terminal 2 uv run python mcp_client_agent.py ``` -#### Layer 2: Stateful Multi-Turn Sessions +#### Layer 2 — Stateful Multi-Turn Sessions ```bash -# Terminal 1 — start the stateful server (stop Layer 1 first — same port) +# Terminal 1 (stop Layer 1 first — same port) uv run python mcp_server_stateful.py # port 8002 -# Terminal 2 — run the stateful client +# Terminal 2 uv run python mcp_client_stateful.py ``` -#### Layer 3: Hybrid Strict + NL Tools +#### Layer 3 — Hybrid Strict + NL Tools ```bash -# Terminal 1 — start the hybrid server (stop Layers 1–2 first — same port) +# Terminal 1 (stop Layer 2 first — same port) uv run python mcp_server_hybrid.py # port 8002 -# Terminal 2 — run the hybrid client +# Terminal 2 uv run python mcp_client_hybrid.py ``` -#### Layer 4: Cross-Framework Group Chat +#### Layer 4 — Cross-Framework Group Chat ```bash -# Terminal 1 — start the LangGraph MCP server +# Terminal 1 uv run python mcp_server_langgraph.py # port 8003 -# Terminal 2 — run the group chat orchestration +# Terminal 2 uv run python workflow_group_chat.py ``` -> **Note:** Layers 1–3 share port 8002 — only run one at a time. -> Layer 4 uses port 8003 and can run alongside any of the others. +> **Note:** Layers 1–3 share port 8002 — run one at a time. Layer 4 uses +> port 8003 and can run alongside any of the others. -## Technologies Used +## Technologies | Package | Purpose | |---------|---------| -| `agent-framework-core` | Microsoft Agent Framework — agents, tools, MCP client | -| `agent-framework-orchestrations` | GroupChatBuilder for multi-agent workflows | -| `fastmcp` | PrefectHQ FastMCP v3 — stateful MCP server with session support | -| `langgraph` | LangGraph — stateful agent graphs with MemorySaver | -| `langchain-openai` | Azure OpenAI integration for LangGraph agents | -| `python-dotenv` | Load credentials from `mcp/.env` | +| [agent-framework-core](https://github.com/microsoft/agent-framework) | Microsoft Agent Framework — agents, tools, MCP client | +| [agent-framework-orchestrations](https://github.com/microsoft/agent-framework) | GroupChatBuilder for multi-agent workflows | +| [fastmcp](https://github.com/jlowin/fastmcp) | PrefectHQ FastMCP v3 — stateful MCP server with session support | +| [langgraph](https://github.com/langchain-ai/langgraph) | Stateful agent graphs with MemorySaver | +| [langchain-openai](https://github.com/langchain-ai/langchain) | Azure OpenAI integration for LangGraph | ## File Inventory ``` mcp_agent_demo/ -├── mcp_server.py # Layer 1: Basic MAF agent as MCP server -├── mcp_client_agent.py # Layer 1: Client that consumes the MCP service +├── mcp_server.py # Layer 1: MAF agent as MCP server +├── mcp_client_agent.py # Layer 1: Client consuming the MCP service ├── mcp_server_stateful.py # Layer 2: Stateful MCP server (session memory) ├── mcp_client_stateful.py # Layer 2: Multi-turn conversation client -├── mcp_server_hybrid.py # Layer 3: Strict-schema + NL tools in one endpoint -├── mcp_client_hybrid.py # Layer 3: SOC incident flow using both tool types -├── mcp_server_langgraph.py # Layer 4: LangGraph agent as MCP server (port 8003) +├── mcp_server_hybrid.py # Layer 3: Strict-schema + NL tools +├── mcp_client_hybrid.py # Layer 3: SOC incident flow +├── mcp_server_langgraph.py # Layer 4: LangGraph agent as MCP server ├── workflow_group_chat.py # Layer 4: GroupChat — MAF + LangGraph via MCP -├── pyproject.toml # Dependencies -└── README.md # This file +├── pyproject.toml +└── README.md ``` + +## License + +MIT From 8dfcd4c98c8a00e4678bce7266366e3cfe8825bd Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 19:26:34 -0800 Subject: [PATCH 14/37] README: reframe around multi-framework interop problem, two design patterns (Agent-as-Tool + Agent Adapter), add conceptual architecture --- agentic_ai/agents/mcp_agent_demo/README.md | 167 ++++++++++++++++----- 1 file changed, 128 insertions(+), 39 deletions(-) diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md index 4f00a4bd8..260c8c17d 100644 --- a/agentic_ai/agents/mcp_agent_demo/README.md +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -1,18 +1,33 @@ # MCP as the Universal Agent Interop Layer -> Have teams expose their agentic capabilities as MCP tool servers. -> Behind each tool? Whatever they want — multi-agent workflows, RAG -> pipelines, LLM reasoning chains, or agents built with entirely -> different frameworks. The caller doesn't know or care. - -This demo progressively proves that **MCP can serve as the universal -interop layer** between AI agents — across frameworks, across machines, -across multi-turn conversations. - -## Why MCP for Agent-to-Agent Communication? - -The MCP specification now covers virtually every capability needed for -agent-to-agent communication: +## The Problem: Multi-Framework Reality + +In any large organization, different teams build AI agents with different +frameworks. One team uses **LangGraph**, another uses **Microsoft Agent +Framework (MAF)**, a third runs **AutoGen** or **CrewAI**. Each +framework has its own agent abstractions, orchestration patterns, and +communication protocols. + +When you need these agents to work together — a multi-agent workflow +where a business analyst agent hands off to a technical architect agent +built by a different team — the framework boundary becomes a wall. + +**A2A (Agent-to-Agent protocol)** is the solution people typically reach +for. It defines agent cards, task lifecycles, and cross-agent messaging. +But in practice: + +- **A2A adoption is still early.** Few production systems use it today. +- **Framework support is uneven.** Not all agentic frameworks have mature + A2A implementations. +- **It adds conceptual overhead** — agent cards, task objects, streaming + negotiation — for what often boils down to "call this agent and get a + response." + +Meanwhile, **MCP (Model Context Protocol)** is everywhere. Virtually +every major agentic framework — LangChain, LangGraph, MAF, AutoGen, +CrewAI, OpenAI Agents SDK — already speaks MCP. Every LLM platform +supports it. And the MCP specification now covers almost every capability +that A2A provides: | Capability | MCP | A2A | |---|---|---| @@ -24,19 +39,41 @@ agent-to-agent communication: | Structured input/output schemas | ✅ | ✅ | | Ecosystem adoption | 🟢 Every major LLM platform | 🟡 Growing | -When an agent calls another agent via A2A, the LLM still sees it as a -tool call. A2A wraps it in "agent identity" semantics. MCP keeps it as -what it actually is — **a tool with typed I/O**. Less abstraction, less -complexity, same result. +So instead of waiting for A2A to mature, you can use what you already +have: **expose agents as MCP tool servers and consume them from any +framework, right now.** + +## Two Design Patterns + +This demo introduces two complementary patterns for cross-framework +agent interop over MCP: + +### Pattern 1: Agent-as-Tool + +Expose an external agent as an **MCP tool endpoint**. The consuming +agent calls it like any other tool — `call_tool("ask_expert", question)` +— and gets a typed response back. The caller has no idea what framework, +model, or infrastructure powers the tool. + +This is the simplest pattern. It works for stateless queries, and with +MCP's session support (`Mcp-Session-Id`), it extends to stateful +multi-turn conversations as well. -**Where MCP wins:** -- Massive ecosystem — every major LLM platform speaks MCP -- Typed contracts between teams — no "interpret my natural language" ambiguity -- Simpler mental model — teams expose tools, done +### Pattern 2: Agent Adapter -**Where A2A still makes sense:** -- Cross-organizational federation with unknown third parties -- When you truly can't define schemas upfront +Go further: wrap the MCP tool in an **adapter** that presents the remote +agent as a **native participant** in your framework's orchestration. The +adapter handles protocol translation, message extraction, and session +mapping so the orchestrator treats the remote agent identically to its +local agents. + +In this demo, `MCPProxyAgent` adapts a LangGraph agent (running behind +MCP on a different port) into a native MAF agent. MAF's `GroupChatBuilder` +orchestrates it alongside local MAF agents — it cannot tell which +participant is local and which is remote, or which framework powers it. + +This is the key insight: **you don't need A2A to do native multi-agent +orchestration across frameworks. MCP + an adapter gets you there.** ## What This Demo Proves @@ -60,7 +97,7 @@ graph LR --- -### Layer 1: Agent-as-a-Service (Scripts 1–2) +### Layer 1: Agent-as-a-Service (Scripts 1–2) — *Pattern 1: Agent-as-Tool* > Any agent can be exposed as an MCP tool server. Any other agent can > consume it. The caller doesn't know what's behind the tool. @@ -154,11 +191,11 @@ graph TB --- -### Layer 4: Cross-Framework Orchestration (Scripts 7–8) +### Layer 4: Cross-Framework Orchestration (Scripts 7–8) — *Pattern 2: Agent Adapter* > MCP is framework-agnostic. A LangGraph agent behind MCP is -> indistinguishable from a MAF agent. The orchestrator doesn't -> know or care. +> indistinguishable from a MAF agent. The `MCPProxyAgent` adapter +> makes the framework boundary invisible to the orchestrator. ```mermaid graph TB @@ -230,8 +267,8 @@ sequenceDiagram **This is the capstone.** Three execution models (local MAF, remote LangGraph via MCP, LLM orchestrator) participate in the same conversation. -MAF's `GroupChatBuilder` treats all participants identically. **MCP made -the framework boundary invisible.** +MAF's `GroupChatBuilder` treats all participants identically. The Agent +Adapter pattern made the framework boundary invisible. --- @@ -243,9 +280,9 @@ graph TB O["MAF / LangChain / AutoGen / CrewAI"] end - O -->|"call_tool()"| MCP1 - O -->|"call_tool()"| MCP2 - O -->|"call_tool()"| MCP3 + O -->|"call_tool()\nPattern 1: Agent-as-Tool"| MCP1 + O -->|"call_tool()\nPattern 1: Agent-as-Tool"| MCP2 + O -->|"Agent Adapter\nPattern 2: Native Participant"| MCP3 subgraph MCP1["MCP Server — Team Alpha"] A1["MAF Agent\n+ Azure OpenAI\n+ Domain Tools"] @@ -259,18 +296,70 @@ graph TB A3["Any Framework\n+ Any Model\n+ Any Tools"] end - Note1["The caller has no idea\nwhat's behind the tool"] - style Orchestrator fill:#f3e5f5,stroke:#6a1b9a style MCP1 fill:#e3f2fd,stroke:#1565c0 style MCP2 fill:#fff3e0,stroke:#e65100 style MCP3 fill:#e8f5e9,stroke:#2e7d32 ``` -When Team Alpha exposes their agent as an MCP tool server, and Team Beta -calls it from LangGraph, and Team Gamma calls it from AutoGen — **nobody -changed any code**. The MCP protocol handles discovery, invocation, -state, and streaming. This is what A2A promises. MCP already delivers it. +--- + +## Conceptual Architecture: MCP as the Agent Interop Bus + +In production, teams across the organization publish their agents as MCP +servers. Consuming teams choose how to integrate: + +- **As a tool** — call the remote agent like any function (`Pattern 1`) +- **As a native agent** — wrap it in an adapter so it participates in + local multi-agent orchestration as a first-class citizen (`Pattern 2`) + +```mermaid +graph TB + subgraph Registry["Agent / MCP Discovery Layer"] + D["MCP Server Registry\n(catalog of available agent-tools)"] + end + + subgraph Publishers["Agent Publishers"] + P1["Team A\nMAF Agent → MCP Server"] + P2["Team B\nLangGraph Agent → MCP Server"] + P3["Team C\nCrewAI Agent → MCP Server"] + P4["Team D\nCustom Agent → MCP Server"] + end + + P1 -->|publish| D + P2 -->|publish| D + P3 -->|publish| D + P4 -->|publish| D + + subgraph Consumers["Agent Consumers"] + direction TB + subgraph C1["Consumer — Pattern 1: Agent-as-Tool"] + CA1["Any Agent (any framework)"] + CA1 -->|"call_tool()"| TOOL["Remote Agent\nused as a tool"] + end + subgraph C2["Consumer — Pattern 2: Agent Adapter"] + CA2["Multi-Agent Orchestration"] + CA2 --> AD["Agent Adapter"] + AD -->|"wraps MCP tool as\nnative agent"| NATIVE["Remote Agent\nparticipates as native\nagent in orchestration"] + end + end + + D -->|discover| CA1 + D -->|discover| CA2 + + style Registry fill:#fff9c4,stroke:#f9a825 + style Publishers fill:#e8f5e9,stroke:#2e7d32 + style C1 fill:#e3f2fd,stroke:#1565c0 + style C2 fill:#fce4ec,stroke:#c62828 + style AD fill:#fff3e0,stroke:#e65100 +``` + +**The key idea:** every team publishes agents through MCP servers. Every +consuming team — regardless of their framework — can discover and use +those agents either as tools (simple, stateless or stateful calls) or as +native participants in their own multi-agent orchestrations (via the +Agent Adapter pattern). No A2A required. No framework lock-in. The +protocol everyone already supports becomes the universal interop layer. ## Quick Start From b72c14b56a8306cdb2fb87cdb22bebf9709481f2 Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 19:30:15 -0800 Subject: [PATCH 15/37] README: fix Mermaid diagrams - use br tags instead of \n for line breaks --- agentic_ai/agents/mcp_agent_demo/README.md | 34 +++++++++++----------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md index 260c8c17d..4d810ff32 100644 --- a/agentic_ai/agents/mcp_agent_demo/README.md +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -82,10 +82,10 @@ multi-agent orchestration where the MCP boundary is invisible. ```mermaid graph LR - L1["Layer 1\nAgent-as-a-Service"] - L2["Layer 2\nStateful Sessions"] - L3["Layer 3\nStrict + NL Tools"] - L4["Layer 4\nCross-Framework\nOrchestration"] + L1["Layer 1
Agent-as-a-Service"] + L2["Layer 2
Stateful Sessions"] + L3["Layer 3
Strict + NL Tools"] + L4["Layer 4
Cross-Framework
Orchestration"] L1 --> L2 --> L3 --> L4 @@ -280,20 +280,20 @@ graph TB O["MAF / LangChain / AutoGen / CrewAI"] end - O -->|"call_tool()\nPattern 1: Agent-as-Tool"| MCP1 - O -->|"call_tool()\nPattern 1: Agent-as-Tool"| MCP2 - O -->|"Agent Adapter\nPattern 2: Native Participant"| MCP3 + O -->|"call_tool()
Pattern 1: Agent-as-Tool"| MCP1 + O -->|"call_tool()
Pattern 1: Agent-as-Tool"| MCP2 + O -->|"Agent Adapter
Pattern 2: Native Participant"| MCP3 subgraph MCP1["MCP Server — Team Alpha"] - A1["MAF Agent\n+ Azure OpenAI\n+ Domain Tools"] + A1["MAF Agent
+ Azure OpenAI
+ Domain Tools"] end subgraph MCP2["MCP Server — Team Beta"] - A2["LangGraph ReAct Agent\n+ MemorySaver\n+ Architecture Tools"] + A2["LangGraph ReAct Agent
+ MemorySaver
+ Architecture Tools"] end subgraph MCP3["MCP Server — Team Gamma"] - A3["Any Framework\n+ Any Model\n+ Any Tools"] + A3["Any Framework
+ Any Model
+ Any Tools"] end style Orchestrator fill:#f3e5f5,stroke:#6a1b9a @@ -316,14 +316,14 @@ servers. Consuming teams choose how to integrate: ```mermaid graph TB subgraph Registry["Agent / MCP Discovery Layer"] - D["MCP Server Registry\n(catalog of available agent-tools)"] + D["MCP Server Registry
(catalog of available agent-tools)"] end subgraph Publishers["Agent Publishers"] - P1["Team A\nMAF Agent → MCP Server"] - P2["Team B\nLangGraph Agent → MCP Server"] - P3["Team C\nCrewAI Agent → MCP Server"] - P4["Team D\nCustom Agent → MCP Server"] + P1["Team A
MAF Agent → MCP Server"] + P2["Team B
LangGraph Agent → MCP Server"] + P3["Team C
CrewAI Agent → MCP Server"] + P4["Team D
Custom Agent → MCP Server"] end P1 -->|publish| D @@ -335,12 +335,12 @@ graph TB direction TB subgraph C1["Consumer — Pattern 1: Agent-as-Tool"] CA1["Any Agent (any framework)"] - CA1 -->|"call_tool()"| TOOL["Remote Agent\nused as a tool"] + CA1 -->|"call_tool()"| TOOL["Remote Agent
used as a tool"] end subgraph C2["Consumer — Pattern 2: Agent Adapter"] CA2["Multi-Agent Orchestration"] CA2 --> AD["Agent Adapter"] - AD -->|"wraps MCP tool as\nnative agent"| NATIVE["Remote Agent\nparticipates as native\nagent in orchestration"] + AD -->|"wraps MCP tool as
native agent"| NATIVE["Remote Agent
participates as native
agent in orchestration"] end end From 5515120a62fbd2048917056cf4ebdf4740a3a78a Mon Sep 17 00:00:00 2001 From: "James N." Date: Thu, 19 Feb 2026 20:51:43 -0800 Subject: [PATCH 16/37] README: fix A2A comparison table - elicitation supported, structured schemas not --- agentic_ai/agents/mcp_agent_demo/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md index 4d810ff32..5ff167b8c 100644 --- a/agentic_ai/agents/mcp_agent_demo/README.md +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -34,9 +34,9 @@ that A2A provides: | Stateful sessions | ✅ `Mcp-Session-Id` | ✅ | | Streaming | ✅ SSE | ✅ SSE | | Long-running tasks (polling, cancellation, TTL) | ✅ | ✅ | -| Mid-task user input (elicitation) | ✅ | ❌ | +| Mid-task user input (elicitation) | ✅ | ✅ `INPUT_REQUIRED` | | OAuth 2.1 + OIDC auth | ✅ | ✅ | -| Structured input/output schemas | ✅ | ✅ | +| Structured input/output schemas | ✅ JSON Schema | ❌ Descriptive only | | Ecosystem adoption | 🟢 Every major LLM platform | 🟡 Growing | So instead of waiting for A2A to mature, you can use what you already From 23837d8d9b6c13e903ac271162a77eb5c7f418b0 Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 08:28:11 -0800 Subject: [PATCH 17/37] Upgrade agent-framework to 1.0.0rc1 and fastmcp to 3.0.2 Breaking changes migrated: - agent-framework 1.0.0rc1: ChatAgent->Agent, AgentThread->AgentSession, run_stream->run(stream=True), model->default_options(ChatOptions), WorkflowOutputEvent/AgentRunEvent/RequestInfoEvent->unified WorkflowEvent, MagenticBuilder now uses constructor kwargs, orchestrations moved to agent_framework_orchestrations package - fastmcp 3.0.2: removed mcp version pin (now pulled by fastmcp), no code changes needed (fully compatible) Files updated: - agentic_ai/agents/agent_framework/single_agent.py - agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py - agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py - agentic_ai/agents/agent_framework/multi_agent/magentic_group.py - agentic_ai/applications/pyproject.toml (agent-framework==1.0.0rc1) - agentic_ai/workflow/fraud_detection_durable/pyproject.toml (agent-framework==1.0.0rc1) - mcp/pyproject.toml (fastmcp==3.0.2) - tests/test_agent_framework_rc1_regression.py (51 regression tests, all passing) --- .../multi_agent/handoff_multi_domain_agent.py | 48 +- .../multi_agent/magentic_group.py | 139 +++-- .../multi_agent/reflection_agent.py | 44 +- .../agents/agent_framework/single_agent.py | 32 +- agentic_ai/applications/pyproject.toml | 2 +- agentic_ai/applications/requirements.txt | 2 +- agentic_ai/applications/uv.lock | 160 ++++- .../fraud_detection_durable/pyproject.toml | 2 +- .../workflow/fraud_detection_durable/uv.lock | 135 ++++- mcp/pyproject.toml | 3 +- mcp/uv.lock | 434 +++++++++---- tests/test_agent_framework_rc1_regression.py | 571 ++++++++++++++++++ 12 files changed, 1313 insertions(+), 259 deletions(-) create mode 100644 tests/test_agent_framework_rc1_regression.py diff --git a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py index dca76b14a..448f0a783 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py @@ -22,7 +22,7 @@ from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field -from agent_framework import ChatAgent, ChatMessage, Role, MCPStreamableHTTPTool +from agent_framework import Agent as FrameworkAgent, AgentSession, ChatOptions, Message, Role, MCPStreamableHTTPTool from agent_framework.azure import AzureOpenAIChatClient from agents.base_agent import BaseAgent, ToolCallTrackingMixin @@ -176,8 +176,8 @@ def __init__(self, state_store: Dict[str, Any], session_id: str, access_token: s # Track current agent and conversation history per domain self._current_domain = state_store.get(f"{session_id}_current_domain", None) - self._domain_agents: Dict[str, ChatAgent] = {} - self._domain_threads: Dict[str, Any] = {} + self._domain_agents: Dict[str, FrameworkAgent] = {} + self._domain_sessions: Dict[str, AgentSession] = {} self._initialized = False # Turn tracking for tool grouping @@ -313,26 +313,26 @@ async def _setup_agents(self) -> None: agent_name=domain_id ) - agent = ChatAgent( + agent = FrameworkAgent( + client=chat_client, name=domain_id, - chat_client=chat_client, instructions=domain_config["instructions"], tools=domain_tools, # Pass list of filtered AIFunction objects - model=self.openai_model_name, + default_options=ChatOptions(model_id=self.openai_model_name), ) # Enter agent context await agent.__aenter__() self._domain_agents[domain_id] = agent - # Create or restore thread for this domain - thread_state_key = f"{self.session_id}_thread_{domain_id}" - thread_state = self.state_store.get(thread_state_key) + # Create or restore session for this domain + session_state_key = f"{self.session_id}_session_{domain_id}" + session_state = self.state_store.get(session_state_key) - if thread_state: - self._domain_threads[domain_id] = await agent.deserialize_thread(thread_state) + if session_state: + self._domain_sessions[domain_id] = AgentSession.from_dict(session_state) else: - self._domain_threads[domain_id] = agent.get_new_thread() + self._domain_sessions[domain_id] = agent.create_session() self._initialized = True logger.info(f"[HANDOFF] Initialized {len(self._domain_agents)} domain specialists with filtered tools") @@ -563,9 +563,9 @@ async def chat_async(self, prompt: str) -> str: self._current_domain = target_domain self.state_store[f"{self.session_id}_current_domain"] = target_domain - # Get the specialist agent and thread + # Get the specialist agent and session agent = self._domain_agents[target_domain] - thread = self._domain_threads[target_domain] + session = self._domain_sessions[target_domain] # Prepare the prompt with context if this is a handoff actual_prompt = prompt @@ -591,7 +591,8 @@ async def chat_async(self, prompt: str) -> str: full_response = [] try: - async for chunk in agent.run_stream(actual_prompt, thread=thread): + response_stream = agent.run(actual_prompt, stream=True, session=session) + async for chunk in response_stream.updates: # Process contents in the chunk if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -677,9 +678,9 @@ async def chat_async(self, prompt: str) -> str: }, ) - # Get new agent and thread + # Get new agent and session new_agent = self._domain_agents[new_target_domain] - new_thread = self._domain_threads[new_target_domain] + new_session = self._domain_sessions[new_target_domain] # Build context prefix context_prefix = await self._build_context_prefix(target_domain, new_target_domain) @@ -700,7 +701,8 @@ async def chat_async(self, prompt: str) -> str: # Get response from new agent full_response_handoff = [] try: - async for chunk in new_agent.run_stream(actual_prompt_handoff, thread=new_thread): + response_stream_handoff = new_agent.run(actual_prompt_handoff, stream=True, session=new_session) + async for chunk in response_stream_handoff.updates: if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: if content.type == "function_call": @@ -747,7 +749,7 @@ async def chat_async(self, prompt: str) -> str: # Use handoff response assistant_response = ''.join(full_response_handoff) target_domain = new_target_domain - thread = new_thread + session = new_session # Send final result if self._ws_manager: @@ -766,10 +768,10 @@ async def chat_async(self, prompt: str) -> str: ] self.append_to_chat_history(messages) - # Save thread state for this domain - thread_state_key = f"{self.session_id}_thread_{target_domain}" - new_state = await thread.serialize() - self.state_store[thread_state_key] = new_state + # Save session state for this domain + session_state_key = f"{self.session_id}_session_{target_domain}" + new_state = session.to_dict() + self.state_store[session_state_key] = new_state # Save overall state self._setstate({ diff --git a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py index 9ac4db5c8..0b3c9737b 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py +++ b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py @@ -7,17 +7,19 @@ from typing import Any, Callable, Dict, Iterable, List, Optional, cast from agent_framework import ( - ChatAgent, - MagenticBuilder, + Agent as FrameworkAgent, + ChatOptions, MCPStreamableHTTPTool, WorkflowCheckpoint, - WorkflowOutputEvent, + WorkflowEvent, CheckpointStorage, - AgentRunUpdateEvent, - AgentRunEvent, + ResponseStream, + WorkflowRunResult, +) +from agent_framework_orchestrations import ( + MagenticBuilder, MagenticOrchestratorEvent, MagenticOrchestratorEventType, - RequestInfoEvent, MagenticPlanReviewRequest, MagenticPlanReviewResponse, ) @@ -441,48 +443,37 @@ async def _build_workflow( ) -> Any: participants = await self._create_participants(participant_client, tools) - builder = MagenticBuilder().participants(list(participants.values())) - - # Note: Streaming is now handled in _run_workflow by processing events from run_stream() + # Note: Streaming is now handled in _run_workflow by processing events from run() if self._ws_manager: logger.info(f"[STREAMING] WebSocket manager available for session_id={self.session_id}") - logger.info("[STREAMING] Events will be streamed via run_stream() processing") + logger.info("[STREAMING] Events will be streamed via run(stream=True) processing") # Create manager agent for the StandardMagenticManager - manager_agent = ChatAgent( - chat_client=manager_client, + manager_agent = FrameworkAgent( + client=manager_client, name="magentic_manager", instructions=self._manager_instructions, ) - builder = ( - builder - .with_manager( - agent=manager_agent, - max_round_count=self._max_round_count, - max_stall_count=self._max_stall_count, - max_reset_count=self._max_reset_count, - progress_ledger_prompt=self.CUSTOM_PROGRESS_LEDGER_PROMPT, - ) - .with_checkpointing(checkpoint_storage) + # Build the MagenticBuilder with constructor kwargs (RC1 API) + builder = MagenticBuilder( + participants=list(participants.values()), + manager_agent=manager_agent, + max_round_count=self._max_round_count, + max_stall_count=self._max_stall_count, + max_reset_count=self._max_reset_count, + progress_ledger_prompt=self.CUSTOM_PROGRESS_LEDGER_PROMPT, + checkpoint_storage=checkpoint_storage, + enable_plan_review=self._enable_plan_review, ) - # Optional: enable plan review - if self._enable_plan_review: - try: - builder = builder.with_plan_review(True) - except Exception as exc: - logger.warning( - "[AgentFramework-Magentic] Failed to enable plan review: %s", exc - ) - return builder.build() async def _create_participants( self, participant_client: AzureOpenAIChatClient, tools: Iterable[MCPStreamableHTTPTool] | None, - ) -> Dict[str, ChatAgent]: + ) -> Dict[str, FrameworkAgent]: # Get base MCP tool (connect once, filter per agent) base_mcp_tool = tools[0] if tools else None logger.info(f"[MCP PARTICIPANTS] Creating participants with base MCP tool: {base_mcp_tool}") @@ -578,12 +569,12 @@ async def _create_participants( }, } - participants: Dict[str, ChatAgent] = {} + participants: Dict[str, FrameworkAgent] = {} for participant_id, defaults in base_definitions.items(): agent_kwargs: Dict[str, Any] = { **defaults, - "chat_client": participant_client, - "model": self.openai_model_name, + "client": participant_client, + "default_options": ChatOptions(model_id=self.openai_model_name), } # Apply tool filtering for this participant's domain @@ -602,7 +593,7 @@ async def _create_participants( logger.warning(f"[MCP PARTICIPANTS] No tool filter for '{participant_id}', using all tools") merged_kwargs = self._apply_participant_overrides(participant_id, agent_kwargs) - agent = ChatAgent(**merged_kwargs) + agent = FrameworkAgent(**merged_kwargs) # Initialize agent session (MCP tool already connected above) await agent.__aenter__() @@ -634,32 +625,37 @@ async def _run_workflow( final_answer: str | None = None try: - # Start the initial stream + # Start the initial stream using the new run() API with stream=True + run_kwargs: Dict[str, Any] = {"stream": True} if checkpoint_id: - event_stream = workflow.run_stream_from_checkpoint(checkpoint_id, checkpoint_storage) + run_kwargs["checkpoint_id"] = checkpoint_id + run_kwargs["checkpoint_storage"] = checkpoint_storage + + if task is not None: + response_stream = workflow.run(task, **run_kwargs) else: - event_stream = workflow.run_stream(task) + response_stream = workflow.run(**run_kwargs) pending_responses: dict[str, Any] | None = None output_received = False while not output_received: - # If we have pending plan-review responses, resume via send_responses_streaming + # If we have pending plan-review responses, resume via run() with responses if pending_responses is not None: - event_stream = workflow.send_responses_streaming(pending_responses) + response_stream = workflow.run(stream=True, responses=pending_responses) pending_responses = None - pending_request: RequestInfoEvent | None = None + pending_request: WorkflowEvent | None = None - async for event in event_stream: + async for event in response_stream.updates: # Stream events to WebSocket if available await self._process_workflow_event(event) - if isinstance(event, WorkflowOutputEvent): + if event.type == "output": final_answer = self._extract_text_from_event(event) output_received = True - elif isinstance(event, RequestInfoEvent) and event.request_type is MagenticPlanReviewRequest: + elif event.type == "request_info" and event.request_type is MagenticPlanReviewRequest: # Capture plan review request — stream will pause after this pending_request = event @@ -696,8 +692,16 @@ async def _run_workflow( return final_answer - async def _process_workflow_event(self, event: Any) -> None: - """Process workflow events and stream to WebSocket clients.""" + async def _process_workflow_event(self, event: WorkflowEvent) -> None: + """Process workflow events and stream to WebSocket clients. + + In 1.0.0rc1, all events are WorkflowEvent with a .type field: + - 'magentic_orchestrator': plan, replan, progress ledger updates + - 'request_info': plan review requests + - 'data': streaming tokens from participant agents + - 'executor_completed': complete agent response + - 'output': final workflow output + """ if not self._ws_manager: # Just log if no WebSocket manager if self._workflow_event_logging_enabled: @@ -706,9 +710,10 @@ async def _process_workflow_event(self, event: Any) -> None: try: # Handle MagenticOrchestratorEvent (plan, replan, progress ledger) - if isinstance(event, MagenticOrchestratorEvent): - message_text = getattr(event.data, "text", "") or str(event.data) - kind = event.event_type.value # e.g. "plan_created", "replanned", "progress_ledger_updated" + if event.type == "magentic_orchestrator" and isinstance(event.data, MagenticOrchestratorEvent): + orch_event = event.data + message_text = getattr(orch_event.content, "text", "") or str(orch_event.content) + kind = orch_event.event_type.value # e.g. "plan_created", "replanned", "progress_ledger_updated" await self._ws_manager.broadcast( self.session_id, { @@ -718,10 +723,10 @@ async def _process_workflow_event(self, event: Any) -> None: }, ) - # Handle RequestInfoEvent for plan review - elif isinstance(event, RequestInfoEvent) and event.request_type is MagenticPlanReviewRequest: + # Handle plan review requests + elif event.type == "request_info" and event.request_type is MagenticPlanReviewRequest: request_data = cast(MagenticPlanReviewRequest, event.data) - plan_text = getattr(request_data.plan, "text", "") or str(request_data.plan) + plan_text = getattr(request_data.plan, "text", "") or str(request_data.plan) if hasattr(request_data, "plan") else str(request_data) await self._ws_manager.broadcast( self.session_id, { @@ -732,8 +737,8 @@ async def _process_workflow_event(self, event: Any) -> None: }, ) - # Handle AgentRunUpdateEvent (streaming tokens from participant agents) - elif isinstance(event, AgentRunUpdateEvent) and event.data: + # Handle streaming tokens from participant agents (data events) + elif event.type == "data" and event.data: agent_id = event.executor_id if self._stream_agent_id != agent_id or not self._stream_line_open: @@ -760,8 +765,8 @@ async def _process_workflow_event(self, event: Any) -> None: }, ) - # Handle AgentRunEvent (complete agent response) - elif isinstance(event, AgentRunEvent) and event.data: + # Handle complete agent response (executor_completed events) + elif event.type == "executor_completed" and event.data: if self._stream_line_open: self._stream_line_open = False @@ -782,8 +787,8 @@ async def _process_workflow_event(self, event: Any) -> None: }, ) - # Handle WorkflowOutputEvent (final result) - elif isinstance(event, WorkflowOutputEvent): + # Handle final workflow output + elif event.type == "output": final_text = self._extract_text_from_event(event) cleaned_final_text = self._sanitize_final_answer(final_text) or final_text @@ -806,13 +811,13 @@ async def _process_workflow_event(self, event: Any) -> None: logger.error("[AgentFramework-Magentic] Failed to process event: %s", exc, exc_info=True) @staticmethod - def _extract_text_from_event(event: WorkflowOutputEvent) -> str: - """Extract text content from WorkflowOutputEvent data. + def _extract_text_from_event(event: WorkflowEvent) -> str: + """Extract text content from a WorkflowEvent with type='output'. Handles various data formats: - - Single ChatMessage object with .text attribute - - List of ChatMessage objects - - AgentRunResponse with .text attribute + - Single Message object with .text attribute + - List of Message objects + - AgentResponse with .text attribute - Plain string """ data = event.data @@ -848,11 +853,11 @@ def _extract_text_from_event(event: WorkflowOutputEvent) -> str: # Fallback: convert to string return str(data) - async def _log_workflow_event(self, event: Any) -> None: - if isinstance(event, WorkflowOutputEvent): + async def _log_workflow_event(self, event: WorkflowEvent) -> None: + if event.type == "output": logger.debug("[AgentFramework-Magentic] Workflow output event: %s", event.data) else: - logger.debug("[AgentFramework-Magentic] Workflow event emitted: %s", getattr(event, "name", type(event).__name__)) + logger.debug("[AgentFramework-Magentic] Workflow event emitted: type=%s, executor=%s", event.type, event.executor_id) def _render_task_with_history(self, prompt: str) -> str: if not self.chat_history: diff --git a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py index e06b020c8..161f4d55c 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py @@ -10,7 +10,7 @@ import logging from typing import Any, Dict, List -from agent_framework import AgentThread, ChatAgent, MCPStreamableHTTPTool +from agent_framework import Agent as FrameworkAgent, AgentSession, ChatOptions, MCPStreamableHTTPTool from agent_framework.azure import AzureOpenAIChatClient from agents.base_agent import BaseAgent, ToolCallTrackingMixin @@ -48,9 +48,9 @@ def __init__( max_refinements: int = 2, ) -> None: super().__init__(state_store, session_id) - self._primary_agent: ChatAgent | None = None - self._reviewer: ChatAgent | None = None - self._thread: AgentThread | None = None + self._primary_agent: FrameworkAgent | None = None + self._reviewer: FrameworkAgent | None = None + self._session: AgentSession | None = None self._initialized = False self._access_token = access_token self._ws_manager = None @@ -103,31 +103,31 @@ async def _setup_agents(self) -> None: tools = await self._create_mcp_tools() # Create agents - self._primary_agent = ChatAgent( + self._primary_agent = FrameworkAgent( + client=chat_client, name="PrimaryAgent", - chat_client=chat_client, instructions=PRIMARY_AGENT_INSTRUCTIONS, tools=tools, - model=self.openai_model_name, + default_options=ChatOptions(model_id=self.openai_model_name), ) - self._reviewer = ChatAgent( + self._reviewer = FrameworkAgent( + client=chat_client, name="Reviewer", - chat_client=chat_client, instructions=REVIEWER_INSTRUCTIONS, tools=tools, - model=self.openai_model_name, + default_options=ChatOptions(model_id=self.openai_model_name), ) # Initialize agents await self._primary_agent.__aenter__() await self._reviewer.__aenter__() - # Load or create thread + # Load or create session if self.state: - self._thread = await self._primary_agent.deserialize_thread(self.state) + self._session = AgentSession.from_dict(self.state) else: - self._thread = self._primary_agent.get_new_thread() + self._session = self._primary_agent.create_session() self._initialized = True logger.info("[Reflection] Agents initialized") @@ -152,13 +152,13 @@ async def _create_mcp_tools(self) -> MCPStreamableHTTPTool | None: async def _run_agent( self, - agent: ChatAgent, + agent: FrameworkAgent, prompt: str, agent_id: str, ) -> str: """Run an agent with optional streaming. - Even without WebSocket, we use run_stream to capture tool calls for evaluation. + Even without WebSocket, we use streaming to capture tool calls for evaluation. """ if self._ws_manager: return await self._run_agent_streaming(agent, prompt, agent_id) @@ -168,14 +168,15 @@ async def _run_agent( async def _run_agent_non_streaming( self, - agent: ChatAgent, + agent: FrameworkAgent, prompt: str, agent_id: str, ) -> str: """Run agent without WebSocket but still capture tool calls.""" chunks: List[str] = [] - async for chunk in agent.run_stream(prompt, thread=self._thread): + response_stream = agent.run(prompt, stream=True, session=self._session) + async for chunk in response_stream.updates: # Track tool calls for evaluation if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -201,7 +202,7 @@ async def _run_agent_non_streaming( async def _run_agent_streaming( self, - agent: ChatAgent, + agent: FrameworkAgent, prompt: str, agent_id: str, ) -> str: @@ -216,7 +217,8 @@ async def _run_agent_streaming( chunks: List[str] = [] - async for chunk in agent.run_stream(prompt, thread=self._thread): + response_stream = agent.run(prompt, stream=True, session=self._session) + async for chunk in response_stream.updates: # Handle tool calls with argument tracking if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -270,7 +272,7 @@ async def chat_async(self, prompt: str) -> str: logger.info(f"[Reflection] Processing: {prompt[:50]}...") await self._setup_agents() - if not self._primary_agent or not self._reviewer or not self._thread: + if not self._primary_agent or not self._reviewer or not self._session: raise RuntimeError("Agents not initialized") # Clear tool calls from previous request (from mixin) @@ -333,7 +335,7 @@ async def chat_async(self, prompt: str) -> str: {"role": "user", "content": prompt}, {"role": "assistant", "content": response}, ]) - self._setstate(await self._thread.serialize()) + self._setstate(self._session.to_dict()) return response diff --git a/agentic_ai/agents/agent_framework/single_agent.py b/agentic_ai/agents/agent_framework/single_agent.py index ae0a4c178..338995561 100644 --- a/agentic_ai/agents/agent_framework/single_agent.py +++ b/agentic_ai/agents/agent_framework/single_agent.py @@ -1,7 +1,7 @@ import logging from typing import Any, Dict, List -from agent_framework import AgentThread, ChatAgent, MCPStreamableHTTPTool +from agent_framework import Agent as FrameworkAgent, AgentSession, ChatOptions, MCPStreamableHTTPTool from agent_framework.azure import AzureOpenAIChatClient from agents.base_agent import BaseAgent, ToolCallTrackingMixin @@ -14,8 +14,8 @@ class Agent(ToolCallTrackingMixin, BaseAgent): def __init__(self, state_store: Dict[str, Any], session_id: str, access_token: str | None = None) -> None: super().__init__(state_store, session_id) - self._agent: ChatAgent | None = None - self._thread: AgentThread | None = None + self._agent: FrameworkAgent | None = None + self._session: AgentSession | None = None self._initialized = False self._access_token = access_token self._ws_manager = None # WebSocket manager for streaming @@ -80,12 +80,12 @@ async def _setup_single_agent(self) -> None: tools = mcp_tools[0] if mcp_tools else None - self._agent = ChatAgent( + self._agent = FrameworkAgent( + client=chat_client, name="ai_assistant", - chat_client=chat_client, instructions=instructions, tools=tools, - model=self.openai_model_name, + default_options=ChatOptions(model_id=self.openai_model_name), ) try: @@ -97,9 +97,9 @@ async def _setup_single_agent(self) -> None: await self._log_mcp_tool_details() if self.state: - self._thread = await self._agent.deserialize_thread(self.state) + self._session = AgentSession.from_dict(self.state) else: - self._thread = self._agent.get_new_thread() + self._session = self._agent.create_session() self._initialized = True @@ -154,7 +154,7 @@ async def _log_mcp_tool_details(self) -> None: async def chat_async(self, prompt: str) -> str: await self._setup_single_agent() - if not self._agent or not self._thread: + if not self._agent or not self._session: raise RuntimeError("Agent Framework single agent failed to initialize correctly.") # Clear tool calls from previous request (from mixin) @@ -168,9 +168,10 @@ async def chat_async(self, prompt: str) -> str: if self._ws_manager: return await self._chat_async_streaming(prompt) - # Non-streaming path - use run_stream to capture tool calls + # Non-streaming path - use run with stream=True to capture tool calls full_response = [] - async for chunk in self._agent.run_stream(prompt, thread=self._thread): + response_stream = self._agent.run(prompt, stream=True, session=self._session) + async for chunk in response_stream.updates: # Extract tool calls from contents if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -206,14 +207,14 @@ async def chat_async(self, prompt: str) -> str: ] self.append_to_chat_history(messages) - new_state = await self._thread.serialize() + new_state = self._session.to_dict() self._setstate(new_state) return assistant_response async def _chat_async_streaming(self, prompt: str) -> str: """Handle chat with streaming support via WebSocket.""" - if not self._agent or not self._thread: + if not self._agent or not self._session: raise RuntimeError("Agent Framework single agent failed to initialize correctly.") # Notify UI that agent started - with convention flag @@ -231,7 +232,8 @@ async def _chat_async_streaming(self, prompt: str) -> str: full_response = [] try: - async for chunk in self._agent.run_stream(prompt, thread=self._thread): + response_stream = self._agent.run(prompt, stream=True, session=self._session) + async for chunk in response_stream.updates: # Process contents in the chunk if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -301,7 +303,7 @@ async def _chat_async_streaming(self, prompt: str) -> str: ] self.append_to_chat_history(messages) - new_state = await self._thread.serialize() + new_state = self._session.to_dict() self._setstate(new_state) return assistant_response diff --git a/agentic_ai/applications/pyproject.toml b/agentic_ai/applications/pyproject.toml index eacd63989..cc51fc58f 100644 --- a/agentic_ai/applications/pyproject.toml +++ b/agentic_ai/applications/pyproject.toml @@ -5,7 +5,7 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.12" dependencies = [ - "agent-framework==1.0.0b260130", + "agent-framework==1.0.0rc1", "azure-ai-evaluation>=1.14.0", "azure-ai-projects>=2.0.0b2", "azure-cosmos==4.9.0", diff --git a/agentic_ai/applications/requirements.txt b/agentic_ai/applications/requirements.txt index 01d01b186..ac571ecd3 100644 --- a/agentic_ai/applications/requirements.txt +++ b/agentic_ai/applications/requirements.txt @@ -4,7 +4,7 @@ a2a-sdk==0.3.12 # via agent-framework-a2a ag-ui-protocol==0.1.10 # via agent-framework-ag-ui -agent-framework==1.0.0b260130 +agent-framework==1.0.0rc1 # via applications (pyproject.toml) agent-framework-a2a==1.0.0b251114 # via agent-framework-core diff --git 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Import compatibility with the new RC1 API +2. Constructor signatures work correctly +3. Session management (AgentThread → AgentSession migration) +4. Streaming API (run_stream → run(stream=True)) +5. Event processing (unified WorkflowEvent model) +6. MagenticBuilder new constructor-based API +""" + +import asyncio +import json +import logging +import os +import sys +from typing import Any, Dict, List +from unittest.mock import AsyncMock, MagicMock, patch, PropertyMock + +import pytest + +# Ensure the agent packages are importable +sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'agentic_ai')) + + +# ============================================================================= +# Section 1: Import Compatibility Tests +# ============================================================================= + + +class TestImportCompatibility: + """Verify all imports resolve correctly against agent-framework 1.0.0rc1.""" + + def test_core_framework_imports(self): + """Core agent_framework symbols used across all agents.""" + from agent_framework import ( + Agent, + AgentSession, + ChatOptions, + MCPStreamableHTTPTool, + WorkflowCheckpoint, + WorkflowEvent, + CheckpointStorage, + ResponseStream, + WorkflowRunResult, + Role, + Message, + Content, + ) + assert Agent is not None + assert AgentSession is not None + assert ChatOptions is not None + + def test_azure_client_import(self): + """AzureOpenAIChatClient import.""" + from agent_framework.azure import AzureOpenAIChatClient + assert AzureOpenAIChatClient is not None + + def test_orchestration_imports(self): + """Orchestration symbols moved to agent_framework_orchestrations.""" + from agent_framework_orchestrations import ( + MagenticBuilder, + MagenticOrchestratorEvent, + MagenticOrchestratorEventType, + MagenticPlanReviewRequest, + MagenticPlanReviewResponse, + ) + assert MagenticBuilder is not None + + def test_single_agent_import(self): + """Single agent module loads cleanly.""" + from agents.agent_framework.single_agent import Agent + assert Agent is not None + + def test_handoff_agent_import(self): + """Handoff multi-domain agent module loads cleanly.""" + from agents.agent_framework.multi_agent.handoff_multi_domain_agent import Agent + assert Agent is not None + + def test_reflection_agent_import(self): + """Reflection agent module loads cleanly.""" + from agents.agent_framework.multi_agent.reflection_agent import Agent + assert Agent is not None + + def test_magentic_group_import(self): + """Magentic group agent module loads cleanly.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + assert Agent is not None + + def test_default_agent_export(self): + """Default agent_framework package re-exports single_agent.Agent.""" + from agents.agent_framework import Agent + from agents.agent_framework.single_agent import Agent as SingleAgent + assert Agent is SingleAgent + + def test_old_symbols_removed(self): + """Verify old symbols (ChatAgent, AgentThread, etc.) are NOT available.""" + import agent_framework + assert not hasattr(agent_framework, 'ChatAgent'), "ChatAgent should be renamed to Agent" + assert not hasattr(agent_framework, 'AgentThread'), "AgentThread should be renamed to AgentSession" + assert not hasattr(agent_framework, 'ChatMessage'), "ChatMessage should be renamed to Message" + + +# ============================================================================= +# Section 2: Agent Constructor Tests +# ============================================================================= + + +class TestAgentConstructors: + """Verify agent constructors use the new RC1 signatures.""" + + def test_framework_agent_constructor_signature(self): + """Agent(client, instructions, *, name, tools, default_options).""" + from agent_framework import Agent + import inspect + sig = inspect.signature(Agent.__init__) + params = list(sig.parameters.keys()) + # First positional after self should be 'client' + assert params[1] == 'client', f"First param should be 'client', got {params[1]}" + assert 'name' in params + assert 'tools' in params + assert 'default_options' in params + + def test_chat_options_model_id(self): + """ChatOptions accepts model_id for specifying the model.""" + from agent_framework import ChatOptions + opts = ChatOptions(model_id="gpt-4o") + assert opts["model_id"] == "gpt-4o" + + def test_azure_client_constructor(self): + """AzureOpenAIChatClient accepts deployment_name, endpoint, api_version.""" + from agent_framework.azure import AzureOpenAIChatClient + import inspect + sig = inspect.signature(AzureOpenAIChatClient.__init__) + params = list(sig.parameters.keys()) + assert 'api_key' in params + assert 'deployment_name' in params + assert 'endpoint' in params + assert 'api_version' in params + assert 'credential' in params + + +# ============================================================================= +# Section 3: Session Management Tests (AgentThread → AgentSession) +# ============================================================================= + + +class TestSessionManagement: + """Verify AgentSession works correctly as the replacement for AgentThread.""" + + def test_agent_session_creation(self): + """AgentSession can be created with default or custom session_id.""" + from agent_framework import AgentSession + session = AgentSession() + assert session.session_id is not None + + custom = AgentSession(session_id="test-123") + assert custom.session_id == "test-123" + + def test_agent_session_serialization(self): + """AgentSession supports to_dict() and from_dict().""" + from agent_framework import AgentSession + session = AgentSession(session_id="test-serialize") + data = session.to_dict() + assert isinstance(data, dict) + assert data.get("session_id") == "test-serialize" + + restored = AgentSession.from_dict(data) + assert restored.session_id == "test-serialize" + + def test_agent_has_create_session(self): + """Agent has create_session() method (replaces get_new_thread).""" + from agent_framework import Agent + assert hasattr(Agent, 'create_session') + assert callable(getattr(Agent, 'create_session')) + + def test_agent_no_old_thread_methods(self): + """Agent does NOT have old thread methods.""" + from agent_framework import Agent + assert not hasattr(Agent, 'get_new_thread') + assert not hasattr(Agent, 'deserialize_thread') + assert not hasattr(Agent, 'run_stream') + + +# ============================================================================= +# Section 4: Streaming API Tests +# ============================================================================= + + +class TestStreamingAPI: + """Verify run(stream=True) replaces the old run_stream() method.""" + + def test_agent_run_supports_stream_kwarg(self): + """Agent.run() accepts stream=True.""" + from agent_framework import Agent + import inspect + sig = inspect.signature(Agent.run) + assert 'stream' in sig.parameters + + def test_agent_run_supports_session_kwarg(self): + """Agent.run() accepts session=... (replaces thread=...).""" + from agent_framework import Agent + import inspect + sig = inspect.signature(Agent.run) + assert 'session' in sig.parameters + + def test_response_stream_has_updates(self): + """ResponseStream has .updates property for iteration.""" + from agent_framework import ResponseStream + assert hasattr(ResponseStream, 'updates') + + def test_agent_response_update_structure(self): + """AgentResponseUpdate has contents and text.""" + from agent_framework import AgentResponseUpdate + update = AgentResponseUpdate() + assert hasattr(update, 'contents') + assert hasattr(update, 'text') + + def test_content_types(self): + """Content factory methods exist for function_call and function_result.""" + from agent_framework import Content + assert hasattr(Content, 'from_function_call') + assert hasattr(Content, 'from_function_result') + assert hasattr(Content, 'from_text') + + +# ============================================================================= +# Section 5: Workflow Event Model Tests (Unified WorkflowEvent) +# ============================================================================= + + +class TestWorkflowEventModel: + """Verify the unified WorkflowEvent replaces old typed event classes.""" + + def test_workflow_event_output(self): + """WorkflowEvent.output() factory creates an output event.""" + from agent_framework import WorkflowEvent + event = WorkflowEvent.output("executor_1", "final answer") + assert event.type == "output" + assert event.executor_id == "executor_1" + assert event.data == "final answer" + + def test_workflow_event_executor_completed(self): + """WorkflowEvent.executor_completed() for completed agent responses.""" + from agent_framework import WorkflowEvent + event = WorkflowEvent.executor_completed("crm_billing", "response text") + assert event.type == "executor_completed" + assert event.executor_id == "crm_billing" + + def test_workflow_event_emit(self): + """WorkflowEvent.emit() for streaming data.""" + from agent_framework import WorkflowEvent + event = WorkflowEvent.emit("agent_1", "streaming chunk") + assert event.type == "data" + assert event.executor_id == "agent_1" + + def test_workflow_event_request_info(self): + """WorkflowEvent.request_info() for plan reviews.""" + from agent_framework import WorkflowEvent + event = WorkflowEvent.request_info( + "req-123", "magentic_manager", "plan data", response_type=str + ) + assert event.type == "request_info" + assert event.request_id == "req-123" + + def test_old_event_types_removed(self): + """Old event types no longer exist in agent_framework.""" + import agent_framework + for old_type in ['WorkflowOutputEvent', 'AgentRunEvent', 'AgentRunUpdateEvent', 'RequestInfoEvent']: + assert not hasattr(agent_framework, old_type), f"{old_type} should no longer exist" + + def test_magentic_orchestrator_event_type(self): + """MagenticOrchestratorEventType enum values.""" + from agent_framework_orchestrations import MagenticOrchestratorEventType + values = {e.value for e in MagenticOrchestratorEventType} + assert "plan_created" in values + assert "replanned" in values + assert "progress_ledger_updated" in values + + +# ============================================================================= +# Section 6: MagenticBuilder API Tests +# ============================================================================= + + +class TestMagenticBuilder: + """Verify MagenticBuilder uses the new constructor-based API.""" + + def test_magentic_builder_requires_participants(self): + """MagenticBuilder requires participants as a constructor kwarg.""" + from agent_framework_orchestrations import MagenticBuilder + with pytest.raises(TypeError, match="participants"): + MagenticBuilder() # Should fail without participants + + def test_magentic_builder_constructor_params(self): + """MagenticBuilder constructor accepts all needed params.""" + from agent_framework_orchestrations import MagenticBuilder + import inspect + sig = inspect.signature(MagenticBuilder.__init__) + params = set(sig.parameters.keys()) - {'self'} + assert 'participants' in params + assert 'manager_agent' in params + assert 'max_round_count' in params + assert 'max_stall_count' in params + assert 'max_reset_count' in params + assert 'progress_ledger_prompt' in params + assert 'checkpoint_storage' in params + assert 'enable_plan_review' in params + + def test_magentic_builder_has_build(self): + """MagenticBuilder has build() method.""" + from agent_framework_orchestrations import MagenticBuilder + assert hasattr(MagenticBuilder, 'build') + + def test_magentic_builder_no_old_methods(self): + """MagenticBuilder no longer has the old chained builder methods.""" + from agent_framework_orchestrations import MagenticBuilder + # Old API: .participants().with_manager().with_checkpointing() + # The .participants() was a chained method, now it's a constructor param + # with_manager() is gone, replaced by manager_agent constructor param + assert not hasattr(MagenticBuilder, 'with_manager'), \ + "with_manager() replaced by constructor params" + + +# ============================================================================= +# Section 7: Workflow.run() API Tests +# ============================================================================= + + +class TestWorkflowRunAPI: + """Verify Workflow.run() supports the new unified API.""" + + def test_workflow_run_signature(self): + """Workflow.run() accepts stream, responses, checkpoint_id, checkpoint_storage.""" + from agent_framework import Workflow + import inspect + sig = inspect.signature(Workflow.run) + params = set(sig.parameters.keys()) - {'self'} + assert 'stream' in params + assert 'responses' in params + assert 'checkpoint_id' in params + assert 'checkpoint_storage' in params + + def test_workflow_no_old_streaming_methods(self): + """Workflow no longer has run_stream/run_stream_from_checkpoint/send_responses_streaming.""" + from agent_framework import Workflow + assert not hasattr(Workflow, 'run_stream'), "run_stream replaced by run(stream=True)" + assert not hasattr(Workflow, 'run_stream_from_checkpoint'), \ + "run_stream_from_checkpoint replaced by run(checkpoint_id=..., stream=True)" + assert not hasattr(Workflow, 'send_responses_streaming'), \ + "send_responses_streaming replaced by run(responses=..., stream=True)" + + +# ============================================================================= +# Section 8: MCP Tool Tests +# ============================================================================= + + +class TestMCPTools: + """Verify MCPStreamableHTTPTool API compatibility.""" + + def test_mcp_tool_constructor(self): + """MCPStreamableHTTPTool accepts name, url, request_timeout + headers/timeout via kwargs.""" + from agent_framework import MCPStreamableHTTPTool + import inspect + sig = inspect.signature(MCPStreamableHTTPTool.__init__) + params = set(sig.parameters.keys()) - {'self'} + assert 'name' in params + assert 'url' in params + assert 'request_timeout' in params + # headers and timeout are now accepted via **kwargs + assert 'kwargs' in params, "MCPStreamableHTTPTool should accept **kwargs for headers/timeout" + + def test_mcp_tool_is_context_manager(self): + """MCPStreamableHTTPTool supports async context manager protocol.""" + from agent_framework import MCPStreamableHTTPTool + assert hasattr(MCPStreamableHTTPTool, '__aenter__') + assert hasattr(MCPStreamableHTTPTool, '__aexit__') + + +# ============================================================================= +# Section 9: Agent Module Integration Tests (mock-based) +# ============================================================================= + + +class TestSingleAgentIntegration: + """Test single_agent.Agent can be constructed and wired up correctly.""" + + def test_single_agent_init(self): + """Agent can be instantiated with state_store and session_id.""" + from agents.agent_framework.single_agent import Agent + store = {} + agent = Agent(state_store=store, session_id="test-session") + assert agent.session_id == "test-session" + assert agent._agent is None + assert agent._session is None + assert agent._initialized is False + + def test_single_agent_has_tool_tracking(self): + """Agent has tool tracking methods from mixin.""" + from agents.agent_framework.single_agent import Agent + agent = Agent(state_store={}, session_id="test") + assert hasattr(agent, 'get_tool_calls') + assert hasattr(agent, 'track_function_call_start') + assert hasattr(agent, 'finalize_tool_tracking') + + def test_single_agent_websocket_manager(self): + """set_websocket_manager injects the WS manager.""" + from agents.agent_framework.single_agent import Agent + agent = Agent(state_store={}, session_id="test") + mock_manager = MagicMock() + agent.set_websocket_manager(mock_manager) + assert agent._ws_manager is mock_manager + + +class TestHandoffAgentIntegration: + """Test handoff agent construction and domain configuration.""" + + def test_handoff_agent_init(self): + """Handoff agent initializes with domain tracking.""" + from agents.agent_framework.multi_agent.handoff_multi_domain_agent import Agent + store = {} + agent = Agent(state_store=store, session_id="handoff-test") + assert agent._current_domain is None + assert agent._domain_agents == {} + assert agent._domain_sessions == {} + + def test_handoff_agent_restores_domain(self): + """Handoff agent restores current domain from state_store.""" + from agents.agent_framework.multi_agent.handoff_multi_domain_agent import Agent + store = {"handoff-test_current_domain": "crm_billing"} + agent = Agent(state_store=store, session_id="handoff-test") + assert agent._current_domain == "crm_billing" + + def test_handoff_detects_handoff_phrases(self): + """Handoff detection logic works.""" + from agents.agent_framework.multi_agent.handoff_multi_domain_agent import Agent + agent = Agent(state_store={}, session_id="test") + assert agent._detect_handoff_request( + "This is outside my area. Let me connect you with the right specialist." + ) + assert not agent._detect_handoff_request( + "Your billing summary shows $50.00 due." + ) + + +class TestReflectionAgentIntegration: + """Test reflection agent construction.""" + + def test_reflection_agent_init(self): + """Reflection agent initializes with primary + reviewer agents.""" + from agents.agent_framework.multi_agent.reflection_agent import Agent + agent = Agent(state_store={}, session_id="reflect-test") + assert agent._primary_agent is None + assert agent._reviewer is None + assert agent._session is None + assert agent._max_refinements == 2 + + def test_reflection_approval_detection(self): + """Reviewer approval detection works.""" + from agents.agent_framework.multi_agent.reflection_agent import Agent + agent = Agent(state_store={}, session_id="test") + assert agent._is_approved("APPROVE - looks good") + assert not agent._is_approved("Needs improvement on point 3") + + +class TestMagenticGroupIntegration: + """Test magentic group agent construction.""" + + def test_magentic_agent_init(self): + """Magentic agent initializes with configuration.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + agent = Agent(state_store={}, session_id="magentic-test") + assert agent._max_round_count == 4 + assert agent._max_stall_count == 2 + + def test_magentic_checkpoint_storage(self): + """DictCheckpointStorage works for checkpointing.""" + from agents.agent_framework.multi_agent.magentic_group import DictCheckpointStorage + from agent_framework import WorkflowCheckpoint + + backing = {} + storage = DictCheckpointStorage(backing) + assert storage.latest_checkpoint_id is None + + def test_magentic_sanitize_final_answer(self): + """FINAL_ANSWER prefix is stripped from workflow output.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + agent = Agent(state_store={}, session_id="test") + assert agent._sanitize_final_answer("FINAL_ANSWER: Hello world") == "Hello world" + assert agent._sanitize_final_answer("Just a normal response") == "Just a normal response" + assert agent._sanitize_final_answer(None) is None + + def test_magentic_extract_text_from_event(self): + """Text extraction handles various WorkflowEvent data formats.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + from agent_framework import WorkflowEvent + + # Plain string data + event = WorkflowEvent.output("exec1", "Hello") + assert Agent._extract_text_from_event(event) == "Hello" + + # Object with .text attribute + mock_msg = MagicMock() + mock_msg.text = "From message" + event2 = WorkflowEvent.output("exec1", mock_msg) + assert Agent._extract_text_from_event(event2) == "From message" + + def test_magentic_process_output_event(self): + """_process_workflow_event handles output events correctly.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + from agent_framework import WorkflowEvent + + agent = Agent(state_store={}, session_id="test") + ws_manager = AsyncMock() + agent.set_websocket_manager(ws_manager) + + event = WorkflowEvent.output("exec1", "Final answer text") + asyncio.get_event_loop().run_until_complete( + agent._process_workflow_event(event) + ) + + ws_manager.broadcast.assert_called() + call_args = ws_manager.broadcast.call_args[0] + assert call_args[0] == "test" # session_id + msg = call_args[1] + assert msg["type"] == "final_result" + + def test_magentic_process_data_event(self): + """_process_workflow_event handles streaming data events.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + from agent_framework import WorkflowEvent + + agent = Agent(state_store={}, session_id="test") + ws_manager = AsyncMock() + agent.set_websocket_manager(ws_manager) + + # Create a mock data event with text + mock_data = MagicMock() + mock_data.text = "streaming token" + event = WorkflowEvent.emit("crm_billing", mock_data) + + asyncio.get_event_loop().run_until_complete( + agent._process_workflow_event(event) + ) + + # Should broadcast agent_start and agent_token + assert ws_manager.broadcast.call_count >= 1 + + +# ============================================================================= +# Section 10: Framework Version Check +# ============================================================================= + + +class TestFrameworkVersion: + """Verify we're running against the expected RC1 version.""" + + def test_agent_framework_version(self): + """agent_framework is at 1.0.0rc1.""" + import agent_framework + assert agent_framework.__version__ == "1.0.0rc1", \ + f"Expected 1.0.0rc1, got {agent_framework.__version__}" + + def test_agent_framework_core_installed(self): + """agent-framework-core is installed as a dependency.""" + import importlib.metadata + version = importlib.metadata.version('agent-framework-core') + assert version == "1.0.0rc1", \ + f"Expected 1.0.0rc1, got {version}" From 82bfbee305ce7f6fbd26ee47eabdd7bfda767473 Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 08:45:34 -0800 Subject: [PATCH 18/37] Fix CI: regenerate requirements.txt with agent-framework-core==1.0.0rc1 The Docker build uses requirements.txt (not pyproject.toml/uv.lock), and it still had agent-framework-core==1.0.0b260130 pinned, which conflicts with agent-framework==1.0.0rc1 requiring agent-framework-core==1.0.0rc1. --- agentic_ai/applications/requirements.txt | 51 ++++++++++++++++++++---- 1 file changed, 44 insertions(+), 7 deletions(-) diff --git a/agentic_ai/applications/requirements.txt b/agentic_ai/applications/requirements.txt index ac571ecd3..deaa31f3b 100644 --- a/agentic_ai/applications/requirements.txt +++ b/agentic_ai/applications/requirements.txt @@ -1,5 +1,5 @@ # This file was autogenerated by uv via the following command: -# uv pip compile pyproject.toml -o requirements.txt +# uv pip compile pyproject.toml -o requirements.txt --prerelease=allow a2a-sdk==0.3.12 # via agent-framework-a2a ag-ui-protocol==0.1.10 @@ -18,11 +18,15 @@ agent-framework-azure-ai-search==1.0.0b260130 # via agent-framework-core agent-framework-azurefunctions==1.0.0b260130 # via agent-framework-core +agent-framework-bedrock==1.0.0b260219 + # via agent-framework-core agent-framework-chatkit==1.0.0b260130 # via agent-framework-core +agent-framework-claude==1.0.0b260219 + # via agent-framework-core agent-framework-copilotstudio==1.0.0b251114 # via agent-framework-core -agent-framework-core==1.0.0b260130 +agent-framework-core==1.0.0rc1 # via # agent-framework # agent-framework-a2a @@ -31,15 +35,19 @@ agent-framework-core==1.0.0b260130 # agent-framework-azure-ai # agent-framework-azure-ai-search # agent-framework-azurefunctions + # agent-framework-bedrock # agent-framework-chatkit + # agent-framework-claude # agent-framework-copilotstudio # agent-framework-declarative # agent-framework-devui # agent-framework-durabletask + # agent-framework-foundry-local # agent-framework-github-copilot # agent-framework-lab # agent-framework-mem0 # agent-framework-ollama + # agent-framework-orchestrations # agent-framework-purview # agent-framework-redis agent-framework-declarative==1.0.0b260130 @@ -50,6 +58,8 @@ agent-framework-durabletask==1.0.0b260130 # via # agent-framework-azurefunctions # agent-framework-core +agent-framework-foundry-local==1.0.0b260219 + # via agent-framework-core agent-framework-github-copilot==1.0.0b260130 # via agent-framework-core agent-framework-lab==1.0.0b251024 @@ -58,6 +68,8 @@ agent-framework-mem0==1.0.0b251114 # via agent-framework-core agent-framework-ollama==1.0.0b260130 # via agent-framework-core +agent-framework-orchestrations==1.0.0b260219 + # via agent-framework-core agent-framework-purview==1.0.0b251114 # via agent-framework-core agent-framework-redis==1.0.0b251114 @@ -80,6 +92,7 @@ anthropic==0.78.0 anyio==4.11.0 # via # anthropic + # claude-agent-sdk # httpx # mcp # openai @@ -99,10 +112,11 @@ azure-ai-agents==1.2.0b5 # via agent-framework-azure-ai azure-ai-evaluation==1.14.0 # via applications (pyproject.toml) -azure-ai-projects==2.0.0b2 +azure-ai-projects==2.0.0b3 # via # applications (pyproject.toml) # agent-framework-azure-ai + # agent-framework-core azure-common==1.1.28 # via azure-search-documents azure-core==1.36.0 @@ -134,6 +148,7 @@ azure-identity==1.26.0b1 # via # agent-framework-core # azure-ai-evaluation + # azure-ai-projects # azure-monitor-opentelemetry-exporter # durabletask-azuremanaged azure-monitor-opentelemetry==1.8.6 @@ -152,6 +167,13 @@ blinker==1.9.0 # via # flask # streamlit +boto3==1.42.54 + # via agent-framework-bedrock +botocore==1.42.54 + # via + # agent-framework-bedrock + # boto3 + # s3transfer cachetools==5.5.2 # via # google-auth @@ -169,6 +191,8 @@ cffi==2.0.0 # powerfx charset-normalizer==3.4.4 # via requests +claude-agent-sdk==0.1.39 + # via agent-framework-claude click==8.3.1 # via # flask @@ -213,6 +237,8 @@ flask==3.0.3 # via # applications (pyproject.toml) # flasgger +foundry-local-sdk==0.5.1 + # via agent-framework-foundry-local frozenlist==1.8.0 # via # aiohttp @@ -258,6 +284,7 @@ httpx==0.28.1 # agent-framework-purview # anthropic # azure-ai-evaluation + # foundry-local-sdk # mcp # ollama # openai @@ -297,6 +324,10 @@ jiter==0.12.0 # via # anthropic # openai +jmespath==1.1.0 + # via + # boto3 + # botocore joblib==1.5.3 # via nltk jsonpath-ng==1.7.0 @@ -315,6 +346,7 @@ markupsafe==3.0.3 mcp==1.26.0 # via # agent-framework-core + # claude-agent-sdk # openai-agents mem0ai==1.0.1 # via agent-framework-mem0 @@ -365,6 +397,7 @@ openai==2.8.0 # applications (pyproject.toml) # agent-framework-core # azure-ai-evaluation + # azure-ai-projects # mem0ai # openai-agents # openai-chatkit @@ -520,6 +553,7 @@ pydantic==2.11.4 # agent-framework-core # anthropic # fastapi + # foundry-local-sdk # github-copilot-sdk # mcp # mem0ai @@ -534,9 +568,7 @@ pydantic==2.11.4 pydantic-core==2.33.2 # via pydantic pydantic-settings==2.12.0 - # via - # agent-framework-core - # mcp + # via mcp pydeck==0.9.1 # via streamlit pyjwt==2.10.1 @@ -549,12 +581,14 @@ python-dateutil==2.9.0.post0 # via # agent-framework-durabletask # azure-functions-durable + # botocore # github-copilot-sdk # pandas # posthog python-dotenv==1.2.1 # via # applications (pyproject.toml) + # agent-framework-core # agent-framework-devui # microsoft-agents-hosting-core # pydantic-settings @@ -617,6 +651,8 @@ ruamel-yaml==0.18.16 # via azure-ai-evaluation ruamel-yaml-clib==0.2.15 # via ruamel-yaml +s3transfer==0.16.0 + # via boto3 six==1.17.0 # via # flasgger @@ -652,6 +688,7 @@ tornado==6.5.2 # via streamlit tqdm==4.67.1 # via + # foundry-local-sdk # nltk # openai types-requests==2.32.4.20260107 @@ -664,7 +701,6 @@ typing-extensions==4.15.0 # anthropic # anyio # azure-ai-agents - # azure-ai-projects # azure-core # azure-cosmos # azure-identity @@ -695,6 +731,7 @@ tzdata==2025.2 # via pandas urllib3==2.5.0 # via + # botocore # qdrant-client # requests # types-requests From 0cd61f991591a536eadf311b93c82bf06358501c Mon Sep 17 00:00:00 2001 From: James Nguyen Date: Mon, 23 Feb 2026 08:53:42 -0800 Subject: [PATCH 19/37] chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22315210980 --- .../multi_agent/handoff_multi_domain_agent.py | 48 +- .../multi_agent/magentic_group.py | 139 +- .../multi_agent/reflection_agent.py | 44 +- .../agents/agent_framework/single_agent.py | 32 +- agentic_ai/agents/mcp_agent_demo/README.md | 449 ++++ .../agents/mcp_agent_demo/mcp_client_agent.py | 89 + .../mcp_agent_demo/mcp_client_hybrid.py | 280 +++ .../mcp_agent_demo/mcp_client_stateful.py | 120 + .../agents/mcp_agent_demo/mcp_server.py | 143 ++ .../mcp_agent_demo/mcp_server_hybrid.py | 605 +++++ .../mcp_agent_demo/mcp_server_langgraph.py | 352 +++ .../mcp_agent_demo/mcp_server_stateful.py | 210 ++ .../agents/mcp_agent_demo/pyproject.toml | 19 + agentic_ai/agents/mcp_agent_demo/uv.lock | 2091 +++++++++++++++++ .../mcp_agent_demo/workflow_group_chat.py | 436 ++++ agentic_ai/applications/pyproject.toml | 2 +- agentic_ai/applications/requirements.txt | 53 +- agentic_ai/applications/uv.lock | 160 +- .../fraud_detection_durable/pyproject.toml | 2 +- .../workflow/fraud_detection_durable/uv.lock | 135 +- mcp/pyproject.toml | 3 +- mcp/uv.lock | 434 +++- tests/test_agent_framework_rc1_regression.py | 571 +++++ 23 files changed, 6151 insertions(+), 266 deletions(-) create mode 100644 agentic_ai/agents/mcp_agent_demo/README.md create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_server.py create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py create mode 100644 agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py create mode 100644 agentic_ai/agents/mcp_agent_demo/pyproject.toml create mode 100644 agentic_ai/agents/mcp_agent_demo/uv.lock create mode 100644 agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py create mode 100644 tests/test_agent_framework_rc1_regression.py diff --git a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py index dca76b14a..448f0a783 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py @@ -22,7 +22,7 @@ from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field -from agent_framework import ChatAgent, ChatMessage, Role, MCPStreamableHTTPTool +from agent_framework import Agent as FrameworkAgent, AgentSession, ChatOptions, Message, Role, MCPStreamableHTTPTool from agent_framework.azure import AzureOpenAIChatClient from agents.base_agent import BaseAgent, ToolCallTrackingMixin @@ -176,8 +176,8 @@ def __init__(self, state_store: Dict[str, Any], session_id: str, access_token: s # Track current agent and conversation history per domain self._current_domain = state_store.get(f"{session_id}_current_domain", None) - self._domain_agents: Dict[str, ChatAgent] = {} - self._domain_threads: Dict[str, Any] = {} + self._domain_agents: Dict[str, FrameworkAgent] = {} + self._domain_sessions: Dict[str, AgentSession] = {} self._initialized = False # Turn tracking for tool grouping @@ -313,26 +313,26 @@ async def _setup_agents(self) -> None: agent_name=domain_id ) - agent = ChatAgent( + agent = FrameworkAgent( + client=chat_client, name=domain_id, - chat_client=chat_client, instructions=domain_config["instructions"], tools=domain_tools, # Pass list of filtered AIFunction objects - model=self.openai_model_name, + default_options=ChatOptions(model_id=self.openai_model_name), ) # Enter agent context await agent.__aenter__() self._domain_agents[domain_id] = agent - # Create or restore thread for this domain - thread_state_key = f"{self.session_id}_thread_{domain_id}" - thread_state = self.state_store.get(thread_state_key) + # Create or restore session for this domain + session_state_key = f"{self.session_id}_session_{domain_id}" + session_state = self.state_store.get(session_state_key) - if thread_state: - self._domain_threads[domain_id] = await agent.deserialize_thread(thread_state) + if session_state: + self._domain_sessions[domain_id] = AgentSession.from_dict(session_state) else: - self._domain_threads[domain_id] = agent.get_new_thread() + self._domain_sessions[domain_id] = agent.create_session() self._initialized = True logger.info(f"[HANDOFF] Initialized {len(self._domain_agents)} domain specialists with filtered tools") @@ -563,9 +563,9 @@ async def chat_async(self, prompt: str) -> str: self._current_domain = target_domain self.state_store[f"{self.session_id}_current_domain"] = target_domain - # Get the specialist agent and thread + # Get the specialist agent and session agent = self._domain_agents[target_domain] - thread = self._domain_threads[target_domain] + session = self._domain_sessions[target_domain] # Prepare the prompt with context if this is a handoff actual_prompt = prompt @@ -591,7 +591,8 @@ async def chat_async(self, prompt: str) -> str: full_response = [] try: - async for chunk in agent.run_stream(actual_prompt, thread=thread): + response_stream = agent.run(actual_prompt, stream=True, session=session) + async for chunk in response_stream.updates: # Process contents in the chunk if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -677,9 +678,9 @@ async def chat_async(self, prompt: str) -> str: }, ) - # Get new agent and thread + # Get new agent and session new_agent = self._domain_agents[new_target_domain] - new_thread = self._domain_threads[new_target_domain] + new_session = self._domain_sessions[new_target_domain] # Build context prefix context_prefix = await self._build_context_prefix(target_domain, new_target_domain) @@ -700,7 +701,8 @@ async def chat_async(self, prompt: str) -> str: # Get response from new agent full_response_handoff = [] try: - async for chunk in new_agent.run_stream(actual_prompt_handoff, thread=new_thread): + response_stream_handoff = new_agent.run(actual_prompt_handoff, stream=True, session=new_session) + async for chunk in response_stream_handoff.updates: if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: if content.type == "function_call": @@ -747,7 +749,7 @@ async def chat_async(self, prompt: str) -> str: # Use handoff response assistant_response = ''.join(full_response_handoff) target_domain = new_target_domain - thread = new_thread + session = new_session # Send final result if self._ws_manager: @@ -766,10 +768,10 @@ async def chat_async(self, prompt: str) -> str: ] self.append_to_chat_history(messages) - # Save thread state for this domain - thread_state_key = f"{self.session_id}_thread_{target_domain}" - new_state = await thread.serialize() - self.state_store[thread_state_key] = new_state + # Save session state for this domain + session_state_key = f"{self.session_id}_session_{target_domain}" + new_state = session.to_dict() + self.state_store[session_state_key] = new_state # Save overall state self._setstate({ diff --git a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py index 9ac4db5c8..0b3c9737b 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py +++ b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py @@ -7,17 +7,19 @@ from typing import Any, Callable, Dict, Iterable, List, Optional, cast from agent_framework import ( - ChatAgent, - MagenticBuilder, + Agent as FrameworkAgent, + ChatOptions, MCPStreamableHTTPTool, WorkflowCheckpoint, - WorkflowOutputEvent, + WorkflowEvent, CheckpointStorage, - AgentRunUpdateEvent, - AgentRunEvent, + ResponseStream, + WorkflowRunResult, +) +from agent_framework_orchestrations import ( + MagenticBuilder, MagenticOrchestratorEvent, MagenticOrchestratorEventType, - RequestInfoEvent, MagenticPlanReviewRequest, MagenticPlanReviewResponse, ) @@ -441,48 +443,37 @@ async def _build_workflow( ) -> Any: participants = await self._create_participants(participant_client, tools) - builder = MagenticBuilder().participants(list(participants.values())) - - # Note: Streaming is now handled in _run_workflow by processing events from run_stream() + # Note: Streaming is now handled in _run_workflow by processing events from run() if self._ws_manager: logger.info(f"[STREAMING] WebSocket manager available for session_id={self.session_id}") - logger.info("[STREAMING] Events will be streamed via run_stream() processing") + logger.info("[STREAMING] Events will be streamed via run(stream=True) processing") # Create manager agent for the StandardMagenticManager - manager_agent = ChatAgent( - chat_client=manager_client, + manager_agent = FrameworkAgent( + client=manager_client, name="magentic_manager", instructions=self._manager_instructions, ) - builder = ( - builder - .with_manager( - agent=manager_agent, - max_round_count=self._max_round_count, - max_stall_count=self._max_stall_count, - max_reset_count=self._max_reset_count, - progress_ledger_prompt=self.CUSTOM_PROGRESS_LEDGER_PROMPT, - ) - .with_checkpointing(checkpoint_storage) + # Build the MagenticBuilder with constructor kwargs (RC1 API) + builder = MagenticBuilder( + participants=list(participants.values()), + manager_agent=manager_agent, + max_round_count=self._max_round_count, + max_stall_count=self._max_stall_count, + max_reset_count=self._max_reset_count, + progress_ledger_prompt=self.CUSTOM_PROGRESS_LEDGER_PROMPT, + checkpoint_storage=checkpoint_storage, + enable_plan_review=self._enable_plan_review, ) - # Optional: enable plan review - if self._enable_plan_review: - try: - builder = builder.with_plan_review(True) - except Exception as exc: - logger.warning( - "[AgentFramework-Magentic] Failed to enable plan review: %s", exc - ) - return builder.build() async def _create_participants( self, participant_client: AzureOpenAIChatClient, tools: Iterable[MCPStreamableHTTPTool] | None, - ) -> Dict[str, ChatAgent]: + ) -> Dict[str, FrameworkAgent]: # Get base MCP tool (connect once, filter per agent) base_mcp_tool = tools[0] if tools else None logger.info(f"[MCP PARTICIPANTS] Creating participants with base MCP tool: {base_mcp_tool}") @@ -578,12 +569,12 @@ async def _create_participants( }, } - participants: Dict[str, ChatAgent] = {} + participants: Dict[str, FrameworkAgent] = {} for participant_id, defaults in base_definitions.items(): agent_kwargs: Dict[str, Any] = { **defaults, - "chat_client": participant_client, - "model": self.openai_model_name, + "client": participant_client, + "default_options": ChatOptions(model_id=self.openai_model_name), } # Apply tool filtering for this participant's domain @@ -602,7 +593,7 @@ async def _create_participants( logger.warning(f"[MCP PARTICIPANTS] No tool filter for '{participant_id}', using all tools") merged_kwargs = self._apply_participant_overrides(participant_id, agent_kwargs) - agent = ChatAgent(**merged_kwargs) + agent = FrameworkAgent(**merged_kwargs) # Initialize agent session (MCP tool already connected above) await agent.__aenter__() @@ -634,32 +625,37 @@ async def _run_workflow( final_answer: str | None = None try: - # Start the initial stream + # Start the initial stream using the new run() API with stream=True + run_kwargs: Dict[str, Any] = {"stream": True} if checkpoint_id: - event_stream = workflow.run_stream_from_checkpoint(checkpoint_id, checkpoint_storage) + run_kwargs["checkpoint_id"] = checkpoint_id + run_kwargs["checkpoint_storage"] = checkpoint_storage + + if task is not None: + response_stream = workflow.run(task, **run_kwargs) else: - event_stream = workflow.run_stream(task) + response_stream = workflow.run(**run_kwargs) pending_responses: dict[str, Any] | None = None output_received = False while not output_received: - # If we have pending plan-review responses, resume via send_responses_streaming + # If we have pending plan-review responses, resume via run() with responses if pending_responses is not None: - event_stream = workflow.send_responses_streaming(pending_responses) + response_stream = workflow.run(stream=True, responses=pending_responses) pending_responses = None - pending_request: RequestInfoEvent | None = None + pending_request: WorkflowEvent | None = None - async for event in event_stream: + async for event in response_stream.updates: # Stream events to WebSocket if available await self._process_workflow_event(event) - if isinstance(event, WorkflowOutputEvent): + if event.type == "output": final_answer = self._extract_text_from_event(event) output_received = True - elif isinstance(event, RequestInfoEvent) and event.request_type is MagenticPlanReviewRequest: + elif event.type == "request_info" and event.request_type is MagenticPlanReviewRequest: # Capture plan review request — stream will pause after this pending_request = event @@ -696,8 +692,16 @@ async def _run_workflow( return final_answer - async def _process_workflow_event(self, event: Any) -> None: - """Process workflow events and stream to WebSocket clients.""" + async def _process_workflow_event(self, event: WorkflowEvent) -> None: + """Process workflow events and stream to WebSocket clients. + + In 1.0.0rc1, all events are WorkflowEvent with a .type field: + - 'magentic_orchestrator': plan, replan, progress ledger updates + - 'request_info': plan review requests + - 'data': streaming tokens from participant agents + - 'executor_completed': complete agent response + - 'output': final workflow output + """ if not self._ws_manager: # Just log if no WebSocket manager if self._workflow_event_logging_enabled: @@ -706,9 +710,10 @@ async def _process_workflow_event(self, event: Any) -> None: try: # Handle MagenticOrchestratorEvent (plan, replan, progress ledger) - if isinstance(event, MagenticOrchestratorEvent): - message_text = getattr(event.data, "text", "") or str(event.data) - kind = event.event_type.value # e.g. "plan_created", "replanned", "progress_ledger_updated" + if event.type == "magentic_orchestrator" and isinstance(event.data, MagenticOrchestratorEvent): + orch_event = event.data + message_text = getattr(orch_event.content, "text", "") or str(orch_event.content) + kind = orch_event.event_type.value # e.g. "plan_created", "replanned", "progress_ledger_updated" await self._ws_manager.broadcast( self.session_id, { @@ -718,10 +723,10 @@ async def _process_workflow_event(self, event: Any) -> None: }, ) - # Handle RequestInfoEvent for plan review - elif isinstance(event, RequestInfoEvent) and event.request_type is MagenticPlanReviewRequest: + # Handle plan review requests + elif event.type == "request_info" and event.request_type is MagenticPlanReviewRequest: request_data = cast(MagenticPlanReviewRequest, event.data) - plan_text = getattr(request_data.plan, "text", "") or str(request_data.plan) + plan_text = getattr(request_data.plan, "text", "") or str(request_data.plan) if hasattr(request_data, "plan") else str(request_data) await self._ws_manager.broadcast( self.session_id, { @@ -732,8 +737,8 @@ async def _process_workflow_event(self, event: Any) -> None: }, ) - # Handle AgentRunUpdateEvent (streaming tokens from participant agents) - elif isinstance(event, AgentRunUpdateEvent) and event.data: + # Handle streaming tokens from participant agents (data events) + elif event.type == "data" and event.data: agent_id = event.executor_id if self._stream_agent_id != agent_id or not self._stream_line_open: @@ -760,8 +765,8 @@ async def _process_workflow_event(self, event: Any) -> None: }, ) - # Handle AgentRunEvent (complete agent response) - elif isinstance(event, AgentRunEvent) and event.data: + # Handle complete agent response (executor_completed events) + elif event.type == "executor_completed" and event.data: if self._stream_line_open: self._stream_line_open = False @@ -782,8 +787,8 @@ async def _process_workflow_event(self, event: Any) -> None: }, ) - # Handle WorkflowOutputEvent (final result) - elif isinstance(event, WorkflowOutputEvent): + # Handle final workflow output + elif event.type == "output": final_text = self._extract_text_from_event(event) cleaned_final_text = self._sanitize_final_answer(final_text) or final_text @@ -806,13 +811,13 @@ async def _process_workflow_event(self, event: Any) -> None: logger.error("[AgentFramework-Magentic] Failed to process event: %s", exc, exc_info=True) @staticmethod - def _extract_text_from_event(event: WorkflowOutputEvent) -> str: - """Extract text content from WorkflowOutputEvent data. + def _extract_text_from_event(event: WorkflowEvent) -> str: + """Extract text content from a WorkflowEvent with type='output'. Handles various data formats: - - Single ChatMessage object with .text attribute - - List of ChatMessage objects - - AgentRunResponse with .text attribute + - Single Message object with .text attribute + - List of Message objects + - AgentResponse with .text attribute - Plain string """ data = event.data @@ -848,11 +853,11 @@ def _extract_text_from_event(event: WorkflowOutputEvent) -> str: # Fallback: convert to string return str(data) - async def _log_workflow_event(self, event: Any) -> None: - if isinstance(event, WorkflowOutputEvent): + async def _log_workflow_event(self, event: WorkflowEvent) -> None: + if event.type == "output": logger.debug("[AgentFramework-Magentic] Workflow output event: %s", event.data) else: - logger.debug("[AgentFramework-Magentic] Workflow event emitted: %s", getattr(event, "name", type(event).__name__)) + logger.debug("[AgentFramework-Magentic] Workflow event emitted: type=%s, executor=%s", event.type, event.executor_id) def _render_task_with_history(self, prompt: str) -> str: if not self.chat_history: diff --git a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py index e06b020c8..161f4d55c 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py @@ -10,7 +10,7 @@ import logging from typing import Any, Dict, List -from agent_framework import AgentThread, ChatAgent, MCPStreamableHTTPTool +from agent_framework import Agent as FrameworkAgent, AgentSession, ChatOptions, MCPStreamableHTTPTool from agent_framework.azure import AzureOpenAIChatClient from agents.base_agent import BaseAgent, ToolCallTrackingMixin @@ -48,9 +48,9 @@ def __init__( max_refinements: int = 2, ) -> None: super().__init__(state_store, session_id) - self._primary_agent: ChatAgent | None = None - self._reviewer: ChatAgent | None = None - self._thread: AgentThread | None = None + self._primary_agent: FrameworkAgent | None = None + self._reviewer: FrameworkAgent | None = None + self._session: AgentSession | None = None self._initialized = False self._access_token = access_token self._ws_manager = None @@ -103,31 +103,31 @@ async def _setup_agents(self) -> None: tools = await self._create_mcp_tools() # Create agents - self._primary_agent = ChatAgent( + self._primary_agent = FrameworkAgent( + client=chat_client, name="PrimaryAgent", - chat_client=chat_client, instructions=PRIMARY_AGENT_INSTRUCTIONS, tools=tools, - model=self.openai_model_name, + default_options=ChatOptions(model_id=self.openai_model_name), ) - self._reviewer = ChatAgent( + self._reviewer = FrameworkAgent( + client=chat_client, name="Reviewer", - chat_client=chat_client, instructions=REVIEWER_INSTRUCTIONS, tools=tools, - model=self.openai_model_name, + default_options=ChatOptions(model_id=self.openai_model_name), ) # Initialize agents await self._primary_agent.__aenter__() await self._reviewer.__aenter__() - # Load or create thread + # Load or create session if self.state: - self._thread = await self._primary_agent.deserialize_thread(self.state) + self._session = AgentSession.from_dict(self.state) else: - self._thread = self._primary_agent.get_new_thread() + self._session = self._primary_agent.create_session() self._initialized = True logger.info("[Reflection] Agents initialized") @@ -152,13 +152,13 @@ async def _create_mcp_tools(self) -> MCPStreamableHTTPTool | None: async def _run_agent( self, - agent: ChatAgent, + agent: FrameworkAgent, prompt: str, agent_id: str, ) -> str: """Run an agent with optional streaming. - Even without WebSocket, we use run_stream to capture tool calls for evaluation. + Even without WebSocket, we use streaming to capture tool calls for evaluation. """ if self._ws_manager: return await self._run_agent_streaming(agent, prompt, agent_id) @@ -168,14 +168,15 @@ async def _run_agent( async def _run_agent_non_streaming( self, - agent: ChatAgent, + agent: FrameworkAgent, prompt: str, agent_id: str, ) -> str: """Run agent without WebSocket but still capture tool calls.""" chunks: List[str] = [] - async for chunk in agent.run_stream(prompt, thread=self._thread): + response_stream = agent.run(prompt, stream=True, session=self._session) + async for chunk in response_stream.updates: # Track tool calls for evaluation if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -201,7 +202,7 @@ async def _run_agent_non_streaming( async def _run_agent_streaming( self, - agent: ChatAgent, + agent: FrameworkAgent, prompt: str, agent_id: str, ) -> str: @@ -216,7 +217,8 @@ async def _run_agent_streaming( chunks: List[str] = [] - async for chunk in agent.run_stream(prompt, thread=self._thread): + response_stream = agent.run(prompt, stream=True, session=self._session) + async for chunk in response_stream.updates: # Handle tool calls with argument tracking if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -270,7 +272,7 @@ async def chat_async(self, prompt: str) -> str: logger.info(f"[Reflection] Processing: {prompt[:50]}...") await self._setup_agents() - if not self._primary_agent or not self._reviewer or not self._thread: + if not self._primary_agent or not self._reviewer or not self._session: raise RuntimeError("Agents not initialized") # Clear tool calls from previous request (from mixin) @@ -333,7 +335,7 @@ async def chat_async(self, prompt: str) -> str: {"role": "user", "content": prompt}, {"role": "assistant", "content": response}, ]) - self._setstate(await self._thread.serialize()) + self._setstate(self._session.to_dict()) return response diff --git a/agentic_ai/agents/agent_framework/single_agent.py b/agentic_ai/agents/agent_framework/single_agent.py index ae0a4c178..338995561 100644 --- a/agentic_ai/agents/agent_framework/single_agent.py +++ b/agentic_ai/agents/agent_framework/single_agent.py @@ -1,7 +1,7 @@ import logging from typing import Any, Dict, List -from agent_framework import AgentThread, ChatAgent, MCPStreamableHTTPTool +from agent_framework import Agent as FrameworkAgent, AgentSession, ChatOptions, MCPStreamableHTTPTool from agent_framework.azure import AzureOpenAIChatClient from agents.base_agent import BaseAgent, ToolCallTrackingMixin @@ -14,8 +14,8 @@ class Agent(ToolCallTrackingMixin, BaseAgent): def __init__(self, state_store: Dict[str, Any], session_id: str, access_token: str | None = None) -> None: super().__init__(state_store, session_id) - self._agent: ChatAgent | None = None - self._thread: AgentThread | None = None + self._agent: FrameworkAgent | None = None + self._session: AgentSession | None = None self._initialized = False self._access_token = access_token self._ws_manager = None # WebSocket manager for streaming @@ -80,12 +80,12 @@ async def _setup_single_agent(self) -> None: tools = mcp_tools[0] if mcp_tools else None - self._agent = ChatAgent( + self._agent = FrameworkAgent( + client=chat_client, name="ai_assistant", - chat_client=chat_client, instructions=instructions, tools=tools, - model=self.openai_model_name, + default_options=ChatOptions(model_id=self.openai_model_name), ) try: @@ -97,9 +97,9 @@ async def _setup_single_agent(self) -> None: await self._log_mcp_tool_details() if self.state: - self._thread = await self._agent.deserialize_thread(self.state) + self._session = AgentSession.from_dict(self.state) else: - self._thread = self._agent.get_new_thread() + self._session = self._agent.create_session() self._initialized = True @@ -154,7 +154,7 @@ async def _log_mcp_tool_details(self) -> None: async def chat_async(self, prompt: str) -> str: await self._setup_single_agent() - if not self._agent or not self._thread: + if not self._agent or not self._session: raise RuntimeError("Agent Framework single agent failed to initialize correctly.") # Clear tool calls from previous request (from mixin) @@ -168,9 +168,10 @@ async def chat_async(self, prompt: str) -> str: if self._ws_manager: return await self._chat_async_streaming(prompt) - # Non-streaming path - use run_stream to capture tool calls + # Non-streaming path - use run with stream=True to capture tool calls full_response = [] - async for chunk in self._agent.run_stream(prompt, thread=self._thread): + response_stream = self._agent.run(prompt, stream=True, session=self._session) + async for chunk in response_stream.updates: # Extract tool calls from contents if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -206,14 +207,14 @@ async def chat_async(self, prompt: str) -> str: ] self.append_to_chat_history(messages) - new_state = await self._thread.serialize() + new_state = self._session.to_dict() self._setstate(new_state) return assistant_response async def _chat_async_streaming(self, prompt: str) -> str: """Handle chat with streaming support via WebSocket.""" - if not self._agent or not self._thread: + if not self._agent or not self._session: raise RuntimeError("Agent Framework single agent failed to initialize correctly.") # Notify UI that agent started - with convention flag @@ -231,7 +232,8 @@ async def _chat_async_streaming(self, prompt: str) -> str: full_response = [] try: - async for chunk in self._agent.run_stream(prompt, thread=self._thread): + response_stream = self._agent.run(prompt, stream=True, session=self._session) + async for chunk in response_stream.updates: # Process contents in the chunk if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -301,7 +303,7 @@ async def _chat_async_streaming(self, prompt: str) -> str: ] self.append_to_chat_history(messages) - new_state = await self._thread.serialize() + new_state = self._session.to_dict() self._setstate(new_state) return assistant_response diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md new file mode 100644 index 000000000..5ff167b8c --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -0,0 +1,449 @@ +# MCP as the Universal Agent Interop Layer + +## The Problem: Multi-Framework Reality + +In any large organization, different teams build AI agents with different +frameworks. One team uses **LangGraph**, another uses **Microsoft Agent +Framework (MAF)**, a third runs **AutoGen** or **CrewAI**. Each +framework has its own agent abstractions, orchestration patterns, and +communication protocols. + +When you need these agents to work together — a multi-agent workflow +where a business analyst agent hands off to a technical architect agent +built by a different team — the framework boundary becomes a wall. + +**A2A (Agent-to-Agent protocol)** is the solution people typically reach +for. It defines agent cards, task lifecycles, and cross-agent messaging. +But in practice: + +- **A2A adoption is still early.** Few production systems use it today. +- **Framework support is uneven.** Not all agentic frameworks have mature + A2A implementations. +- **It adds conceptual overhead** — agent cards, task objects, streaming + negotiation — for what often boils down to "call this agent and get a + response." + +Meanwhile, **MCP (Model Context Protocol)** is everywhere. Virtually +every major agentic framework — LangChain, LangGraph, MAF, AutoGen, +CrewAI, OpenAI Agents SDK — already speaks MCP. Every LLM platform +supports it. And the MCP specification now covers almost every capability +that A2A provides: + +| Capability | MCP | A2A | +|---|---|---| +| Stateful sessions | ✅ `Mcp-Session-Id` | ✅ | +| Streaming | ✅ SSE | ✅ SSE | +| Long-running tasks (polling, cancellation, TTL) | ✅ | ✅ | +| Mid-task user input (elicitation) | ✅ | ✅ `INPUT_REQUIRED` | +| OAuth 2.1 + OIDC auth | ✅ | ✅ | +| Structured input/output schemas | ✅ JSON Schema | ❌ Descriptive only | +| Ecosystem adoption | 🟢 Every major LLM platform | 🟡 Growing | + +So instead of waiting for A2A to mature, you can use what you already +have: **expose agents as MCP tool servers and consume them from any +framework, right now.** + +## Two Design Patterns + +This demo introduces two complementary patterns for cross-framework +agent interop over MCP: + +### Pattern 1: Agent-as-Tool + +Expose an external agent as an **MCP tool endpoint**. The consuming +agent calls it like any other tool — `call_tool("ask_expert", question)` +— and gets a typed response back. The caller has no idea what framework, +model, or infrastructure powers the tool. + +This is the simplest pattern. It works for stateless queries, and with +MCP's session support (`Mcp-Session-Id`), it extends to stateful +multi-turn conversations as well. + +### Pattern 2: Agent Adapter + +Go further: wrap the MCP tool in an **adapter** that presents the remote +agent as a **native participant** in your framework's orchestration. The +adapter handles protocol translation, message extraction, and session +mapping so the orchestrator treats the remote agent identically to its +local agents. + +In this demo, `MCPProxyAgent` adapts a LangGraph agent (running behind +MCP on a different port) into a native MAF agent. MAF's `GroupChatBuilder` +orchestrates it alongside local MAF agents — it cannot tell which +participant is local and which is remote, or which framework powers it. + +This is the key insight: **you don't need A2A to do native multi-agent +orchestration across frameworks. MCP + an adapter gets you there.** + +## What This Demo Proves + +Each layer adds one capability, building to a cross-framework +multi-agent orchestration where the MCP boundary is invisible. + +```mermaid +graph LR + L1["Layer 1
Agent-as-a-Service"] + L2["Layer 2
Stateful Sessions"] + L3["Layer 3
Strict + NL Tools"] + L4["Layer 4
Cross-Framework
Orchestration"] + + L1 --> L2 --> L3 --> L4 + + style L1 fill:#e3f2fd,stroke:#1565c0 + style L2 fill:#e8f5e9,stroke:#2e7d32 + style L3 fill:#fff3e0,stroke:#e65100 + style L4 fill:#fce4ec,stroke:#c62828 +``` + +--- + +### Layer 1: Agent-as-a-Service (Scripts 1–2) — *Pattern 1: Agent-as-Tool* + +> Any agent can be exposed as an MCP tool server. Any other agent can +> consume it. The caller doesn't know what's behind the tool. + +```mermaid +sequenceDiagram + participant Client as 🤖 Coordinator Agent
(MAF) + participant MCP as MCP Protocol
(Streamable HTTP) + participant Server as 🧠 Expert Advisor Agent
(MAF, port 8002) + + Client->>MCP: call_tool("ask_expert", question) + MCP->>Server: HTTP POST /mcp + Server-->>MCP: Typed response + MCP-->>Client: Result text + + Note over Client,Server: Client has no idea what framework,
model, or infrastructure powers the tool +``` + +| Script | Role | Description | +|---|---|---| +| `mcp_server.py` | Server | MAF Agent with domain tools exposed as MCP endpoint on port 8002 | +| `mcp_client_agent.py` | Client | Coordinator agent that delegates to the remote agent via MCP | + +--- + +### Layer 2: Stateful Sessions (Scripts 3–4) + +> MCP sessions (`Mcp-Session-Id`) enable multi-turn conversations with +> remote agents. The server remembers context — no client-side history needed. + +```mermaid +sequenceDiagram + participant Client as 🤖 Client Agent + participant Server as 🧠 Stateful MCP Server
(port 8002) + + Client->>Server: call_tool("chat", Q1)
Mcp-Session-Id: abc123 + Server-->>Client: R1 + Note right of Server: AgentSession[abc123]
history: [Q1, R1] + + Client->>Server: call_tool("chat", Q2)
Mcp-Session-Id: abc123 + Server-->>Client: R2 (references R1) + Note right of Server: history: [Q1, R1, Q2, R2] + + Client->>Server: call_tool("chat", Q3)
Mcp-Session-Id: abc123 + Server-->>Client: R3 (full context) + Note right of Server: history: [Q1..R2, Q3, R3] +``` + +| Script | Role | Description | +|---|---|---| +| `mcp_server_stateful.py` | Server | Each MCP session → own `AgentSession` with accumulated history | +| `mcp_client_stateful.py` | Client | 3-turn conversation: risks → market data → executive summary | + +--- + +### Layer 3: Strict + Natural Language Tools (Scripts 5–6) + +> Real enterprise platforms need both machine-consumable (strict-schema) +> and human-consumable (natural-language) tools in the same endpoint. + +```mermaid +graph TB + subgraph MCP["Hybrid MCP Server (port 8002)"] + direction TB + subgraph Strict["🔧 Strict-Schema Tools"] + T1["triage_alert
raw text → SecurityAlert"] + T2["assess_threat
SecurityAlert → ThreatAssessment"] + T3["create_response
ThreatAssessment → IncidentResponse"] + end + subgraph NL["💬 Natural Language Tools"] + T4["ask_security_advisor
free-form Q&A"] + T5["explain_for_customer
incident → plain English"] + end + SS[("Shared Session State
last_alert, last_threat,
last_response")] + end + + T1 --> T2 --> T3 + T3 -.-> SS + SS -.-> T4 + SS -.-> T5 + + style Strict fill:#e8f5e9,stroke:#2e7d32 + style NL fill:#e3f2fd,stroke:#1565c0 + style SS fill:#fff9c4,stroke:#f9a825 +``` + +| Script | Role | Description | +|---|---|---| +| `mcp_server_hybrid.py` | Server | Pydantic-validated strict tools + NL tools, shared session state | +| `mcp_client_hybrid.py` | Client | Full SOC incident flow using both tool types in sequence | + +--- + +### Layer 4: Cross-Framework Orchestration (Scripts 7–8) — *Pattern 2: Agent Adapter* + +> MCP is framework-agnostic. A LangGraph agent behind MCP is +> indistinguishable from a MAF agent. The `MCPProxyAgent` adapter +> makes the framework boundary invisible to the orchestrator. + +```mermaid +graph TB + User["👤 User"] + User --> GC + + subgraph GC["GroupChatBuilder (MAF Orchestration)"] + Fac["🎯 Facilitator
LLM Orchestrator
decides who speaks"] + + Fac --> BS + Fac --> TA + Fac --> PL + + BS["👔 BusinessStrategist
Local MAF Agent
business impact, ROI, risk"] + + subgraph MCP_Bridge["MCP Boundary (port 8003)"] + TA["🏗️ TechnicalArchitect
MCPProxyAgent"] + LG["LangGraph ReAct Agent
+ MemorySaver"] + TA -->|"call_tool(ask_architect)"| LG + end + + PL["📋 Planner
Local MAF Agent
synthesizes plan"] + end + + style GC fill:#fafafa,stroke:#333 + style MCP_Bridge fill:#fff3e0,stroke:#e65100 + style BS fill:#e3f2fd,stroke:#1565c0 + style PL fill:#e8f5e9,stroke:#2e7d32 + style TA fill:#fff3e0,stroke:#e65100 + style LG fill:#fce4ec,stroke:#c62828 + style Fac fill:#f3e5f5,stroke:#6a1b9a +``` + +**Conversation flow:** + +```mermaid +sequenceDiagram + participant U as 👤 User + participant F as 🎯 Facilitator + participant BS as 👔 BusinessStrategist
(MAF) + participant TA as 🏗️ TechnicalArchitect
(LangGraph via MCP) + participant PL as 📋 Planner
(MAF) + + U->>F: "Migrate our e-commerce to cloud" + F->>BS: Your turn + BS-->>F: Business perspective & ROI + F->>TA: Your turn + Note right of TA: Proxy sends only latest msg
LangGraph MemorySaver keeps history + TA-->>F: Architecture & tech stack + F->>PL: Synthesize the plan + PL-->>F: Consolidated action plan + F-->>U: ✅ Discussion complete + + U->>F: "What about risks and phasing?" + F->>BS: Your turn + BS-->>F: Risk analysis + F->>TA: Your turn + Note right of TA: Remembers prior discussion
via MemorySaver + TA-->>F: Migration phasing strategy + F->>PL: Update the plan + PL-->>F: Revised plan with risk mitigations + F-->>U: ✅ Discussion complete +``` + +| Script | Role | Description | +|---|---|---| +| `mcp_server_langgraph.py` | Server | LangGraph ReAct agent as MCP server (port 8003), `MemorySaver` for statefulness | +| `workflow_group_chat.py` | Orchestrator | MAF GroupChat: 3 participants + LLM facilitator, multi-turn with predefined questions | + +**This is the capstone.** Three execution models (local MAF, remote +LangGraph via MCP, LLM orchestrator) participate in the same conversation. +MAF's `GroupChatBuilder` treats all participants identically. The Agent +Adapter pattern made the framework boundary invisible. + +--- + +## The Core Pattern + +```mermaid +graph TB + subgraph Orchestrator["Orchestrating Agent (any framework)"] + O["MAF / LangChain / AutoGen / CrewAI"] + end + + O -->|"call_tool()
Pattern 1: Agent-as-Tool"| MCP1 + O -->|"call_tool()
Pattern 1: Agent-as-Tool"| MCP2 + O -->|"Agent Adapter
Pattern 2: Native Participant"| MCP3 + + subgraph MCP1["MCP Server — Team Alpha"] + A1["MAF Agent
+ Azure OpenAI
+ Domain Tools"] + end + + subgraph MCP2["MCP Server — Team Beta"] + A2["LangGraph ReAct Agent
+ MemorySaver
+ Architecture Tools"] + end + + subgraph MCP3["MCP Server — Team Gamma"] + A3["Any Framework
+ Any Model
+ Any Tools"] + end + + style Orchestrator fill:#f3e5f5,stroke:#6a1b9a + style MCP1 fill:#e3f2fd,stroke:#1565c0 + style MCP2 fill:#fff3e0,stroke:#e65100 + style MCP3 fill:#e8f5e9,stroke:#2e7d32 +``` + +--- + +## Conceptual Architecture: MCP as the Agent Interop Bus + +In production, teams across the organization publish their agents as MCP +servers. Consuming teams choose how to integrate: + +- **As a tool** — call the remote agent like any function (`Pattern 1`) +- **As a native agent** — wrap it in an adapter so it participates in + local multi-agent orchestration as a first-class citizen (`Pattern 2`) + +```mermaid +graph TB + subgraph Registry["Agent / MCP Discovery Layer"] + D["MCP Server Registry
(catalog of available agent-tools)"] + end + + subgraph Publishers["Agent Publishers"] + P1["Team A
MAF Agent → MCP Server"] + P2["Team B
LangGraph Agent → MCP Server"] + P3["Team C
CrewAI Agent → MCP Server"] + P4["Team D
Custom Agent → MCP Server"] + end + + P1 -->|publish| D + P2 -->|publish| D + P3 -->|publish| D + P4 -->|publish| D + + subgraph Consumers["Agent Consumers"] + direction TB + subgraph C1["Consumer — Pattern 1: Agent-as-Tool"] + CA1["Any Agent (any framework)"] + CA1 -->|"call_tool()"| TOOL["Remote Agent
used as a tool"] + end + subgraph C2["Consumer — Pattern 2: Agent Adapter"] + CA2["Multi-Agent Orchestration"] + CA2 --> AD["Agent Adapter"] + AD -->|"wraps MCP tool as
native agent"| NATIVE["Remote Agent
participates as native
agent in orchestration"] + end + end + + D -->|discover| CA1 + D -->|discover| CA2 + + style Registry fill:#fff9c4,stroke:#f9a825 + style Publishers fill:#e8f5e9,stroke:#2e7d32 + style C1 fill:#e3f2fd,stroke:#1565c0 + style C2 fill:#fce4ec,stroke:#c62828 + style AD fill:#fff3e0,stroke:#e65100 +``` + +**The key idea:** every team publishes agents through MCP servers. Every +consuming team — regardless of their framework — can discover and use +those agents either as tools (simple, stateless or stateful calls) or as +native participants in their own multi-agent orchestrations (via the +Agent Adapter pattern). No A2A required. No framework lock-in. The +protocol everyone already supports becomes the universal interop layer. + +## Quick Start + +### Prerequisites + +- Python 3.12+, [uv](https://docs.astral.sh/uv/) +- Azure OpenAI credentials in `mcp/.env` + +### Running the demos + +```bash +cd agentic_ai/agents/mcp_agent_demo +uv sync +``` + +#### Layer 1 — Basic Agent-as-MCP-Service + +```bash +# Terminal 1 +uv run python mcp_server.py # port 8002 + +# Terminal 2 +uv run python mcp_client_agent.py +``` + +#### Layer 2 — Stateful Multi-Turn Sessions + +```bash +# Terminal 1 (stop Layer 1 first — same port) +uv run python mcp_server_stateful.py # port 8002 + +# Terminal 2 +uv run python mcp_client_stateful.py +``` + +#### Layer 3 — Hybrid Strict + NL Tools + +```bash +# Terminal 1 (stop Layer 2 first — same port) +uv run python mcp_server_hybrid.py # port 8002 + +# Terminal 2 +uv run python mcp_client_hybrid.py +``` + +#### Layer 4 — Cross-Framework Group Chat + +```bash +# Terminal 1 +uv run python mcp_server_langgraph.py # port 8003 + +# Terminal 2 +uv run python workflow_group_chat.py +``` + +> **Note:** Layers 1–3 share port 8002 — run one at a time. Layer 4 uses +> port 8003 and can run alongside any of the others. + +## Technologies + +| Package | Purpose | +|---------|---------| +| [agent-framework-core](https://github.com/microsoft/agent-framework) | Microsoft Agent Framework — agents, tools, MCP client | +| [agent-framework-orchestrations](https://github.com/microsoft/agent-framework) | GroupChatBuilder for multi-agent workflows | +| [fastmcp](https://github.com/jlowin/fastmcp) | PrefectHQ FastMCP v3 — stateful MCP server with session support | +| [langgraph](https://github.com/langchain-ai/langgraph) | Stateful agent graphs with MemorySaver | +| [langchain-openai](https://github.com/langchain-ai/langchain) | Azure OpenAI integration for LangGraph | + +## File Inventory + +``` +mcp_agent_demo/ +├── mcp_server.py # Layer 1: MAF agent as MCP server +├── mcp_client_agent.py # Layer 1: Client consuming the MCP service +├── mcp_server_stateful.py # Layer 2: Stateful MCP server (session memory) +├── mcp_client_stateful.py # Layer 2: Multi-turn conversation client +├── mcp_server_hybrid.py # Layer 3: Strict-schema + NL tools +├── mcp_client_hybrid.py # Layer 3: SOC incident flow +├── mcp_server_langgraph.py # Layer 4: LangGraph agent as MCP server +├── workflow_group_chat.py # Layer 4: GroupChat — MAF + LangGraph via MCP +├── pyproject.toml +└── README.md +``` + +## License + +MIT diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py b/agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py new file mode 100644 index 000000000..8bd4fb6f9 --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py @@ -0,0 +1,89 @@ +""" +Part 2 — Consume the agent-powered MCP service from another agent. + +This script creates a local "Coordinator" agent that connects to the +remote Expert Advisor MCP server (Part 1) and delegates business +analysis questions to it. + +Prerequisites: + mcp_server.py must be running on http://localhost:8002/mcp + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_client_agent.py +""" + +import asyncio +import os +import sys + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, MCPStreamableHTTPTool +from agent_framework.azure import AzureOpenAIChatClient + + +async def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + mcp_server_url = "http://localhost:8002/mcp" + print(f"🔗 Connecting to MCP server at {mcp_server_url} ...") + + # 1) Create the MCPStreamableHTTPTool — this connects to the remote agent + async with MCPStreamableHTTPTool( + name="expert_advisor", + description="Remote Expert Advisor agent accessible via MCP — can analyze risk, look up market data, and summarize findings.", + url=mcp_server_url, + ) as mcp_tool: + print(f"✅ Connected! Tools available from MCP server: {[t.name for t in mcp_tool.functions]}") + + # 2) Create a local Coordinator agent that wraps the MCP tool + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + async with Agent( + client=client, + name="Coordinator", + instructions=( + "You are a project coordinator. When users ask business strategy questions, " + "use the expert_advisor MCP tool to get expert analysis. Present the results " + "clearly and add your own coordination notes." + ), + tools=mcp_tool, + ) as coordinator: + # 3) Ask some questions + queries = [ + "What are the main risks if we expand into the technology sector?", + "Give me current market data for the healthcare sector and summarize it.", + ] + + for query in queries: + print(f"\n{'='*60}") + print(f"📝 User: {query}") + print(f"{'='*60}") + + response = await coordinator.run(query) + + print(f"\n🤖 Coordinator:\n{response.text}") + print(f"{'─'*60}") + + print("\n✅ Demo complete!") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py b/agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py new file mode 100644 index 000000000..a9d2b121d --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py @@ -0,0 +1,280 @@ +""" +Hybrid MCP Client — demonstrates calling BOTH strict-schema and +natural-language tools from the same MCP server in one incident flow. + +This client simulates a realistic MSP SOC workflow: + + Phase 1 (STRICT SCHEMA): + 1. Feed a raw EDR alert → triage_alert → SecurityAlert (Pydantic) + 2. Pass SecurityAlert → assess_threat → ThreatAssessment (Pydantic) + 3. Pass ThreatAssessment → create_response → IncidentResponse (Pydantic) + + Phase 2 (NATURAL LANGUAGE): + 4. "What's our SLA obligation for this severity?" → ask_security_advisor + 5. "Draft a customer notification" → explain_for_customer + + The point: strict schema for automation (isolation, blocking, SLA timers), + natural language for humans (advisor questions, customer emails). + Same server, same session, both tool types working together. + +Prerequisites: + mcp_server_hybrid.py must be running on http://localhost:8002/mcp + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_client_hybrid.py +""" + +import asyncio +import json +import os +import sys + +from dotenv import load_dotenv + +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, MCPStreamableHTTPTool +from agent_framework.azure import AzureOpenAIChatClient + +# ── The same raw EDR alert used in the typed-contracts demo ───────────────── + +RAW_ALERT = """ +=== EDR ALERT — Managed Endpoint Security === +Timestamp: 2026-02-19T14:23:17Z +Source: Endpoint Detection & Response (EDR Agent v4.2) +Tenant: Contoso Financial Services (tenant: contoso-fin-2024) + +ALERT: Suspicious PowerShell execution detected on CONTOSO-DC01 + +Endpoint: CONTOSO-DC01 (10.42.1.5) — Domain Controller +User Context: CONTOSO\\svc_backup (service account) + +Observed Activity: +- PowerShell process spawned by svc_backup at 14:23:17Z +- Encoded command detected: Base64-encoded Invoke-Mimikatz variant +- LSASS memory access attempted (credential dumping pattern) +- Outbound connection to 185.220.101.42:443 (known C2 infrastructure) +- Kerberos ticket request anomaly: TGT requested for domain admin group +- Shadow copy deletion command queued (vssadmin delete shadows) + +File Hash (SHA-256): a1b2c3d4e5f67890abcdef1234567890abcdef1234567890abcdef1234567890 +Secondary Hash: f0e1d2c3b4a596870fedcba9876543210fedcba9876543210fedcba987654321 +DNS Query: update-service.kfrp[.]xyz (suspicious DGA-like domain) + +Additional Telemetry: +- 3 failed RDP attempts from CONTOSO-WS-047 to CONTOSO-DC01 prior to event +- svc_backup account password last changed 847 days ago +- Similar PowerShell pattern seen on CONTOSO-FS01 (file server) 47 min earlier +- No MFA enforcement on service accounts per tenant policy + +Raw Log: EventID=4688|ProcessName=powershell.exe|CommandLine=-enc +SQBtAHAAbwByAHQALQBNAG8AZAB1AGwAZQAgAC4AXABJAHaA...|ParentProcess=cmd.exe +|User=CONTOSO\\svc_backup|LogonType=3|SourceIP=10.42.3.47 +""" + + +async def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + mcp_url = "http://localhost:8002/mcp" + print("=" * 78) + print("🛡️ HYBRID MCP DEMO — Strict Schema + Natural Language in One Server") + print("=" * 78) + print() + print(" This demo processes a security incident through the SAME MCP server") + print(" using BOTH tool types:") + print() + print(" Phase 1 — STRICT SCHEMA (drives automation):") + print(" triage_alert → assess_threat → create_response") + print(" Output: Pydantic-validated JSON → APIs, SLA timers, tickets") + print() + print(" Phase 2 — NATURAL LANGUAGE (helps humans):") + print(" ask_security_advisor → explain_for_customer") + print(" Output: plain English prose → analysts, customers, executives") + print() + print(f" Connecting to: {mcp_url}") + print() + + async with MCPStreamableHTTPTool( + name="security_ops", + description=( + "Hybrid security operations server with BOTH strict-schema tools " + "(triage_alert, assess_threat, create_response) AND natural-language " + "tools (ask_security_advisor, explain_for_customer). Use strict tools " + "for structured incident data, natural tools for human questions." + ), + url=mcp_url, + ) as mcp_tool: + tool_names = [t.name for t in mcp_tool.functions] + print(f" ✅ Connected — {len(tool_names)} tools: {', '.join(tool_names)}") + print() + + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + async with Agent( + client=client, + name="SOC_Analyst", + instructions=( + "You are a SOC analyst using a hybrid security operations platform.\n\n" + "You have access to TWO TYPES of tools:\n\n" + "STRICT-SCHEMA tools (for automation — always use these for incident data):\n" + " • triage_alert: Pass raw alert text → get SecurityAlert JSON\n" + " • assess_threat: Pass SecurityAlert JSON → get ThreatAssessment JSON\n" + " • create_response: Pass ThreatAssessment JSON + endpoint/account details\n" + " → get IncidentResponse JSON\n\n" + "NATURAL-LANGUAGE tools (for human questions and communication):\n" + " • ask_security_advisor: Ask any security policy/practice question\n" + " • explain_for_customer: Translate technical details to plain English\n\n" + "RULES:\n" + "1. For incident processing, ALWAYS use strict-schema tools in order\n" + "2. Pass the FULL JSON output from one strict tool as input to the next\n" + "3. For questions about policy/practice/SLA, use ask_security_advisor\n" + "4. For customer communication, use explain_for_customer\n" + "5. Present strict-schema results with key fields highlighted\n" + "6. Present natural-language results as-is with brief intro\n" + "7. Do NOT reformulate or summarize the raw alert — pass it directly" + ), + tools=mcp_tool, + ) as analyst: + + # ════════════════════════════════════════════════════════════ + # PHASE 1 — STRICT SCHEMA: Incident processing pipeline + # ════════════════════════════════════════════════════════════ + + print("━" * 78) + print(" PHASE 1 │ STRICT-SCHEMA TOOLS — Incident Processing Pipeline") + print("━" * 78) + + # Step 1: Triage + print() + print(" STEP 1 │ triage_alert: Raw EDR → SecurityAlert (12 typed fields)") + print(" " + "─" * 60) + r1 = await analyst.run( + f"Triage this raw alert using the triage_alert tool. " + f"Pass the ENTIRE raw text:\n\n{RAW_ALERT}" + ) + print(f"\n 🤖 Analyst:\n{_indent(r1.text)}") + + # Step 2: Threat Assessment (pass Step 1 output explicitly) + print() + print(" STEP 2 │ assess_threat: SecurityAlert → ThreatAssessment (11 typed fields)") + print(" " + "─" * 60) + r2 = await analyst.run( + "Now pass this SecurityAlert JSON to assess_threat for " + "threat intelligence analysis. Here is the SecurityAlert " + f"output from the previous step:\n\n{r1.text}" + ) + print(f"\n 🤖 Analyst:\n{_indent(r2.text)}") + + # Step 3: Response Plan (pass Step 2 output explicitly) + print() + print(" STEP 3 │ create_response: ThreatAssessment → IncidentResponse (15 typed fields)") + print(" " + "─" * 60) + r3 = await analyst.run( + "Now create an incident response plan using create_response. " + "Pass this ThreatAssessment JSON along with " + "affected_endpoints='CONTOSO-DC01,CONTOSO-FS01', " + "compromised_accounts='CONTOSO\\\\svc_backup,domain_admin', and " + f"data_at_risk=true.\n\nThreatAssessment from previous step:\n\n{r2.text}" + ) + print(f"\n 🤖 Analyst:\n{_indent(r3.text)}") + + # ════════════════════════════════════════════════════════════ + # PHASE 2 — NATURAL LANGUAGE: Human-facing tools + # ════════════════════════════════════════════════════════════ + + print() + print("━" * 78) + print(" PHASE 2 │ NATURAL-LANGUAGE TOOLS — Human-Facing Communication") + print("━" * 78) + + # Step 4: Ask advisor + print() + print(" STEP 4 │ ask_security_advisor: Policy/SLA question (free-form answer)") + print(" " + "─" * 60) + r4 = await analyst.run( + "Ask the security advisor: Given this incident involves credential " + "theft on a domain controller for a financial services tenant, " + "what are our SOC 2 and SOX compliance notification obligations? " + "What's the timeline?\n\n" + f"Incident context from prior steps:\n{r3.text}" + ) + print(f"\n 🤖 Analyst:\n{_indent(r4.text)}") + + # Step 5: Customer explanation + print() + print(" STEP 5 │ explain_for_customer: Plain-English customer notification") + print(" " + "─" * 60) + r5 = await analyst.run( + "Now use explain_for_customer to draft a notification for the " + "Contoso Financial Services executive team (audience: 'executive'). " + "Summarize the incident and what we're doing about it — no jargon.\n\n" + f"Full incident context:\n{r3.text}" + ) + print(f"\n 🤖 Analyst:\n{_indent(r5.text)}") + + # ════════════════════════════════════════════════════════════ + # SUMMARY + # ════════════════════════════════════════════════════════════ + + print() + print("=" * 78) + print("📊 DEMO SUMMARY — Same server, two tool types, one incident") + print("=" * 78) + print(""" + ┌────────────────────────────────────────────────────────────────────────┐ + │ STRICT-SCHEMA TOOLS │ NATURAL-LANGUAGE TOOLS │ + │ (Pydantic-validated JSON) │ (Free-form prose) │ + ├────────────────────────────────────────────────────────────────────────┤ + │ │ │ + │ triage_alert │ ask_security_advisor │ + │ → SecurityAlert (12 fields) │ → "Our SOC 2 obligations │ + │ → severity=HIGH (not "bad") │ require notification within │ + │ → Starts 30-min SLA timer │ 24 hours..." │ + │ │ → Human reads, understands, │ + │ assess_threat │ makes decision │ + │ → ThreatAssessment (11 flds) │ │ + │ → attack_vector=CRED_THEFT │ explain_for_customer │ + │ → Selects Mimikatz playbook │ → "We detected unauthorized │ + │ │ access to your network..." │ + │ create_response │ → CFO reads this, not the JSON │ + │ → IncidentResponse (15 flds) │ │ + │ → isolate_endpoint API call │ │ + │ → disable_account API call │ │ + │ → block_ip firewall rule │ │ + │ │ │ + │ WHO READS THIS: machines │ WHO READS THIS: humans │ + │ → APIs, automation, tickets │ → analysts, customers, executives │ + └────────────────────────────────────────────────────────────────────────┘ + + Both tool types coexist in ONE MCP server, ONE session, ONE incident. + The right tool for the right job. +""") + print("=" * 78) + print("✅ Hybrid demo complete — strict schemas for machines, prose for people.") + print("=" * 78) + + print() + + +def _indent(text: str, prefix: str = " ") -> str: + """Indent each line of text for nicer console output.""" + return "\n".join(f"{prefix}{line}" for line in text.split("\n")) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py b/agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py new file mode 100644 index 000000000..88ec20689 --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py @@ -0,0 +1,120 @@ +""" +Stateful MCP Client — multi-turn agent-to-agent conversation. + +This script demonstrates a local agent-framework Agent having a multi-turn, +stateful conversation with a remote Expert Advisor agent served via MCP. + +The key insight: + • MCPStreamableHTTPTool maintains the MCP session (sends mcp-session-id + header automatically), so the *remote* agent remembers prior turns. + • The local coordinator agent also maintains its own conversation context. + +Prerequisites: + mcp_server_stateful.py must be running on http://localhost:8002/mcp + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_client_stateful.py +""" + +import asyncio +import os +import sys + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, MCPStreamableHTTPTool +from agent_framework.azure import AzureOpenAIChatClient + + +async def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + mcp_server_url = "http://localhost:8002/mcp" + print(f"🔗 Connecting to stateful MCP server at {mcp_server_url} ...") + + # ── Connect to the stateful MCP server ────────────────────────────── + # MCPStreamableHTTPTool maintains the MCP session automatically. + # All calls within this context block share the same mcp-session-id, + # so the remote agent retains conversation history. + async with MCPStreamableHTTPTool( + name="stateful_expert", + description=( + "Remote Expert Advisor agent (stateful). Supports multi-turn " + "conversations — the expert remembers what was discussed earlier. " + "Tools: chat_with_expert, get_session_info, reset_conversation." + ), + url=mcp_server_url, + ) as mcp_tool: + tool_names = [t.name for t in mcp_tool.functions] + print(f"✅ Connected! Tools available: {tool_names}") + + # ── Create the local coordinator agent ────────────────────────── + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + async with Agent( + client=client, + name="MultiTurnCoordinator", + instructions=( + "You coordinate multi-turn conversations with a remote expert advisor.\n\n" + "RULES:\n" + "1. When the user asks a question, use the chat_with_expert tool to send it.\n" + "2. The expert remembers previous turns — you can ask follow-up questions.\n" + "3. Present the expert's response clearly, adding brief coordination notes.\n" + "4. If asked for session info, use the get_session_info tool.\n" + "5. If asked to reset, use the reset_conversation tool.\n" + "6. Do NOT repeat the full question in your tool call — keep it concise." + ), + tools=mcp_tool, + ) as coordinator: + + # ── Multi-turn conversation ───────────────────────────────── + # Each question builds on the previous one. The remote expert + # maintains state across calls thanks to the stateful MCP session. + conversation = [ + "What are the main risks if we expand into the technology sector?", + "Based on that risk analysis, what does current market data for technology look like? Are the risks justified by the opportunity?", + "Now summarize everything we've discussed — the risks AND the market data — into a brief executive recommendation.", + ] + + for i, question in enumerate(conversation, 1): + print(f"\n{'='*70}") + print(f" TURN {i}") + print(f"{'='*70}") + print(f"📝 User: {question}\n") + + response = await coordinator.run(question) + + print(f"🤖 Coordinator:\n{response.text}") + print(f"{'─'*70}") + + # ── Show session metadata ─────────────────────────────────── + print(f"\n{'='*70}") + print(" SESSION INFO") + print(f"{'='*70}") + info_response = await coordinator.run( + "Use the get_session_info tool and tell me how many turns we've had." + ) + print(f"📊 {info_response.text}") + + print("\n✅ Stateful multi-turn demo complete!") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server.py b/agentic_ai/agents/mcp_agent_demo/mcp_server.py new file mode 100644 index 000000000..1fda8ceea --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server.py @@ -0,0 +1,143 @@ +""" +Part 1 — Expose an Agent-Framework agent as an MCP HTTP server. + +This script creates an Azure OpenAI–powered agent with domain tools, +then serves it as an MCP Streamable-HTTP endpoint on port 8002. + +Any MCP client (Claude Desktop, VS Code Copilot, or another agent using +MCPStreamableHTTPTool) can connect to http://localhost:8002/mcp and +invoke the agent. + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_server.py +""" + +import asyncio +import os +import sys +from typing import Annotated + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, tool +from agent_framework.azure import AzureOpenAIChatClient +from mcp.server.fastmcp import FastMCP + +# ── Domain tools the agent can use ────────────────────────────────────────── + +@tool(approval_mode="never_require") +def analyze_risk( + scenario: Annotated[str, "A brief description of the business scenario to assess"], +) -> Annotated[str, "A risk analysis summary"]: + """Analyze business risk for a given scenario and return a structured assessment.""" + # Simulated risk analysis — in production this would call a model or DB + return ( + f"Risk Analysis for: {scenario}\n" + "─────────────────────────────\n" + "• Market Risk: MEDIUM — competitive landscape is evolving\n" + "• Financial Risk: LOW — strong cash position\n" + "• Operational Risk: MEDIUM — supply chain dependencies noted\n" + "• Regulatory Risk: LOW — compliant with current frameworks\n" + "• Overall Rating: MEDIUM\n" + "• Recommendation: Proceed with standard due diligence." + ) + + +@tool(approval_mode="never_require") +def get_market_data( + sector: Annotated[str, "Industry sector to look up, e.g. 'technology', 'healthcare'"], +) -> Annotated[str, "Market data summary for the sector"]: + """Retrieve current market data for a given industry sector.""" + data = { + "technology": "Tech sector: YTD +18%, P/E 28x, top movers: AI infrastructure, cloud security.", + "healthcare": "Healthcare sector: YTD +8%, P/E 22x, top movers: GLP-1 drugs, digital health.", + "energy": "Energy sector: YTD +5%, P/E 14x, top movers: renewables, grid storage.", + "finance": "Finance sector: YTD +12%, P/E 16x, top movers: fintech, digital payments.", + } + return data.get(sector.lower(), f"No data available for sector '{sector}'. Known sectors: {', '.join(data.keys())}") + + +@tool(approval_mode="never_require") +def summarize_findings( + text: Annotated[str, "The text to summarize"], + max_sentences: Annotated[int, "Maximum number of sentences in the summary"] = 3, +) -> Annotated[str, "A concise summary"]: + """Produce a concise summary of the provided text.""" + sentences = [s.strip() for s in text.replace("\n", " ").split(".") if s.strip()] + summary = ". ".join(sentences[:max_sentences]) + "." + return f"Summary ({max_sentences} sentences max): {summary}" + + +# ── Build and serve ───────────────────────────────────────────────────────── + +def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + print(f"🔧 Azure OpenAI endpoint : {endpoint}") + print(f"🔧 Deployment : {deployment}") + + # 1) Create the agent-framework Agent + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + agent = Agent( + client=client, + name="ExpertAdvisor", + description=( + "An expert business advisor that can analyze risk, retrieve market data, " + "and summarize findings. Ask it any business strategy question." + ), + instructions=( + "You are an expert business strategy advisor. Use your tools to provide " + "thorough, data-backed analysis. Always call analyze_risk for risk questions, " + "get_market_data for sector insights, and summarize_findings to condense results." + ), + tools=[analyze_risk, get_market_data, summarize_findings], + ) + + # 2) Create a FastMCP wrapper that delegates to the agent + # We register the agent as a single MCP tool so any MCP client can invoke it. + mcp_server = FastMCP( + "ExpertAdvisor", + stateless_http=True, + json_response=True, + host="0.0.0.0", + port=8002, + ) + + @mcp_server.tool() + async def ask_expert( + question: Annotated[str, "The business question to ask the expert advisor"], + ) -> str: + """Ask the Expert Advisor agent a business strategy question. It can analyze risk, look up market data, and summarize findings.""" + async with agent: + response = await agent.run(question) + return response.text + + print("──────────────────────────────────────────────────") + print("🚀 MCP Agent Server starting on http://localhost:8002/mcp") + print(" Transport: Streamable HTTP (stateless)") + print(" Tools exposed: ask_expert (wraps ExpertAdvisor agent)") + print("──────────────────────────────────────────────────") + + mcp_server.run(transport="streamable-http") + + +if __name__ == "__main__": + main() diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py b/agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py new file mode 100644 index 000000000..8e691c76b --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py @@ -0,0 +1,605 @@ +""" +Hybrid MCP Server — Strict-Schema + Natural-Language tools in one service. + +This MCP server demonstrates a realistic MSP / IT security platform that +exposes BOTH kinds of tools through a single endpoint: + + STRICT-SCHEMA tools (Pydantic-validated, drive automation): + • triage_alert — raw alert text → SecurityAlert (12 typed fields) + • assess_threat — SecurityAlert → ThreatAssessment (11 typed fields) + • create_response — ThreatAssessment + context → IncidentResponse (15 typed fields) + + NATURAL-LANGUAGE tools (conversational, for human consumption): + • ask_security_advisor — free-form security questions, best practices, policy + • explain_for_customer — translate a technical incident into plain English + for a non-technical customer/executive + +WHY this matters: + Strict-schema tools MUST return exact enums, scores, and lists because their + output feeds automated systems (endpoint isolation APIs, SLA timers, ticket + creation, compliance notifications). Getting severity wrong = wrong SLA = + contractual breach. + + Natural-language tools are fine with prose because a HUMAN reads the output. + "What's our patching policy?" doesn't need a Pydantic model — it needs a + clear, helpful paragraph. + + A real MSP platform needs BOTH. This demo shows them coexisting in one + MCP server, with the client agent choosing the right tool for each task. + +Architecture: + ┌─────────────────────────────────────────────────────────┐ + │ FastMCP (stateful, streamable-http, port 8002) │ + │ │ + │ ┌─ STRICT SCHEMA ──────────────────────────────────┐ │ + │ │ triage_alert → SecurityAlert (Pydantic) │ │ + │ │ assess_threat → ThreatAssessment (Pydantic) │ │ + │ │ create_response → IncidentResponse (Pydantic) │ │ + │ └──────────────────────────────────────────────────┘ │ + │ │ + │ ┌─ NATURAL LANGUAGE ───────────────────────────────┐ │ + │ │ ask_security_advisor → free-form text │ │ + │ │ explain_for_customer → plain-English narrative │ │ + │ └──────────────────────────────────────────────────┘ │ + │ │ + │ MCP session_id → AgentSession (history preserved) │ + └─────────────────────────────────────────────────────────┘ + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_server_hybrid.py + + # Then in another terminal: + uv run python mcp_client_hybrid.py +""" + +import json +import os +import sys +from datetime import datetime, timezone +from enum import Enum +from typing import Annotated, cast + +from dotenv import load_dotenv +from pydantic import BaseModel, Field + +# ── Load credentials ──────────────────────────────────────────────────────── +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, AgentResponse +from agent_framework._sessions import AgentSession +from agent_framework.azure import AzureOpenAIChatClient +from fastmcp import FastMCP +from fastmcp.server.context import Context + + +# ═══════════════════════════════════════════════════════════════════════════ +# PYDANTIC CONTRACTS — strict schemas for automation-critical tools +# ═══════════════════════════════════════════════════════════════════════════ + + +class AlertSource(str, Enum): + EDR = "edr" + SIEM = "siem" + NETWORK_IDS = "network_ids" + EMAIL_GATEWAY = "email_gateway" + VULNERABILITY_SCANNER = "vulnerability_scanner" + USER_REPORT = "user_report" + + +class Severity(str, Enum): + CRITICAL = "critical" # SLA: 15 min response, 1 hr containment + HIGH = "high" # SLA: 30 min response, 4 hr containment + MEDIUM = "medium" # SLA: 2 hr response, 24 hr containment + LOW = "low" # SLA: 8 hr response, 72 hr containment + INFORMATIONAL = "informational" + + +class AttackVector(str, Enum): + RANSOMWARE = "ransomware" + PHISHING = "phishing" + LATERAL_MOVEMENT = "lateral_movement" + CREDENTIAL_THEFT = "credential_theft" + MALWARE_EXECUTION = "malware_execution" + DATA_EXFILTRATION = "data_exfiltration" + PRIVILEGE_ESCALATION = "privilege_escalation" + COMMAND_AND_CONTROL = "command_and_control" + SUPPLY_CHAIN = "supply_chain" + UNKNOWN = "unknown" + + +class ThreatCategory(str, Enum): + APT = "apt" + COMMODITY_MALWARE = "commodity_malware" + INSIDER_THREAT = "insider_threat" + OPPORTUNISTIC_ATTACK = "opportunistic_attack" + TARGETED_ATTACK = "targeted_attack" + FALSE_POSITIVE = "false_positive" + + +class RemediationAction(str, Enum): + ISOLATE_ENDPOINT = "isolate_endpoint" + DISABLE_ACCOUNT = "disable_account" + BLOCK_IP = "block_ip" + BLOCK_HASH = "block_hash" + FORCE_PASSWORD_RESET = "force_password_reset" + REVOKE_SESSIONS = "revoke_sessions" + QUARANTINE_EMAIL = "quarantine_email" + DEPLOY_PATCH = "deploy_patch" + RESTORE_FROM_BACKUP = "restore_from_backup" + ESCALATE_TO_HUMAN = "escalate_to_human" + + +class ResponsePriority(str, Enum): + P1 = "P1" + P2 = "P2" + P3 = "P3" + P4 = "P4" + + +class ImpactScope(str, Enum): + SINGLE_ENDPOINT = "single_endpoint" + MULTIPLE_ENDPOINTS = "multiple_endpoints" + SUBNET = "subnet" + SITE = "site" + TENANT_WIDE = "tenant_wide" + CROSS_TENANT = "cross_tenant" + + +# ── Contract: triage_alert output ─────────────────────────────────────────── + +class SecurityAlert(BaseModel): + """Structured alert — every field drives downstream automation.""" + alert_id: str = Field(description="Unique alert ID (e.g. ALT-2026-00847)") + timestamp: str = Field(description="ISO 8601 timestamp of first detection") + source: AlertSource = Field(description="System that generated the alert") + severity: Severity = Field(description="Assessed severity level") + title: str = Field(description="One-line alert title") + description: str = Field(description="Concise description of observed activity") + affected_hostname: str = Field(description="Primary affected endpoint hostname") + affected_ip: str = Field(description="IP address of affected endpoint") + affected_user: str = Field(description="Username associated with the activity") + tenant_id: str = Field(description="MSP customer/tenant identifier") + indicators_of_compromise: list[str] = Field( + description="IOCs: file hashes, IPs, domains, registry keys" + ) + raw_log_snippet: str = Field(description="Relevant raw log excerpt (first 500 chars)") + + +# ── Contract: assess_threat output ────────────────────────────────────────── + +class ThreatAssessment(BaseModel): + """Enriched threat intelligence with confidence scores.""" + alert_id: str = Field(description="Alert ID for traceability") + threat_category: ThreatCategory = Field(description="Classified threat category") + attack_vector: AttackVector = Field(description="MITRE ATT&CK vector") + threat_score: float = Field(ge=0, le=100, description="Threat score 0-100") + known_threat_actor: str | None = Field( + description="Known actor/group name, or null" + ) + mitre_techniques: list[str] = Field( + description="MITRE ATT&CK technique IDs (e.g. T1003.001)" + ) + ioc_matches: list[str] = Field( + description="IOCs that matched threat intel feeds" + ) + confidence_pct: float = Field(ge=0, le=100, description="Confidence 0-100%") + is_known_campaign: bool = Field(description="True if matches known campaign") + recommended_severity_override: Severity | None = Field( + description="Override severity if intel warrants it, null to keep" + ) + intel_summary: str = Field(description="Brief threat intel narrative") + + +# ── Contract: create_response output ──────────────────────────────────────── + +class IncidentResponse(BaseModel): + """Automated response plan — drives real-world remediation APIs.""" + incident_id: str = Field(description="Generated incident ID (e.g. INC-2026-00412)") + alert_id: str = Field(description="Originating alert ID") + priority: ResponsePriority = Field(description="Response priority P1-P4") + final_severity: Severity = Field(description="Final severity after all analysis") + immediate_actions: list[RemediationAction] = Field( + description="Ordered remediation actions to execute NOW" + ) + endpoints_to_isolate: list[str] = Field(description="Hostnames to isolate") + accounts_to_disable: list[str] = Field(description="Accounts to disable/lock") + ips_to_block: list[str] = Field(description="IPs for firewall blocklist") + hashes_to_block: list[str] = Field(description="File hashes to block fleet-wide") + notification_list: list[str] = Field( + description="Roles/teams to notify (e.g. soc_lead, tenant_admin, ciso)" + ) + customer_communication_required: bool = Field( + description="True if MSP must notify customer per SLA" + ) + compliance_flags: list[str] = Field( + description="Regulatory flags (e.g. GDPR_72hr, SOX_incident)" + ) + sla_response_deadline: str = Field(description="ISO 8601 response deadline") + sla_containment_deadline: str = Field(description="ISO 8601 containment deadline") + decision_rationale: str = Field(description="Brief explanation of response decisions") + + +# ═══════════════════════════════════════════════════════════════════════════ +# SERVER +# ═══════════════════════════════════════════════════════════════════════════ + + +def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + print(f"🔧 Azure OpenAI endpoint : {endpoint}") + print(f"🔧 Deployment : {deployment}") + + llm_client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + now_iso = datetime.now(timezone.utc).isoformat() + + # ── Internal agents (not exposed directly — called by MCP tools) ──── + + triage_agent = Agent( + client=llm_client, + name="AlertTriageAgent", + instructions=( + "You are a Tier-1 SOC analyst performing initial alert triage for a " + "managed security services provider (MSP). Extract structured data " + "from raw SIEM/EDR alerts.\n" + "- Assign severity: critical=active ransomware/data exfil, " + "high=credential theft/lateral movement, medium=suspicious behavior, " + "low=policy violation, informational=benign anomaly\n" + "- Extract ALL IOCs: SHA-256 hashes, IPs, domains, registry keys\n" + "- alert_id format: ALT-YYYY-NNNNN\n" + f"- Current UTC time: {now_iso}" + ), + ) + + threat_agent = Agent( + client=llm_client, + name="ThreatIntelAgent", + instructions=( + "You are a threat intelligence analyst. Enrich alerts with MITRE ATT&CK " + "classification and threat intel.\n" + "- threat_score: base 50, +20 known campaign, +15 credential theft, " + "+10 per IOC match, -20 likely false positive\n" + "- confidence_pct: 90%+ only with multiple IOC matches\n" + "- Override severity ONLY if intel clearly contradicts triage" + ), + ) + + response_agent = Agent( + client=llm_client, + name="ResponseOrchestrator", + instructions=( + "You are the automated incident response engine for an MSP platform.\n" + "SLA matrix: CRITICAL=P1 (15min/1hr), HIGH=P2 (30min/4hr), " + "MEDIUM=P3 (2hr/24hr), LOW=P4 (8hr/72hr)\n" + "- Ransomware/data_exfil → isolate + disable accounts\n" + "- Credential theft → force password reset + revoke sessions\n" + "- Lateral movement → isolate ALL affected + at-risk endpoints\n" + "- If data_at_risk AND regulated → add compliance flags\n" + "- Customer communication required for P1 and P2\n" + "- incident_id format: INC-YYYY-NNNNN\n" + f"- Current UTC time: {now_iso}" + ), + ) + + # Natural-language advisor agent (no strict output — free-form helpful answers) + advisor_agent = Agent( + client=llm_client, + name="SecurityAdvisor", + instructions=( + "You are a senior security advisor for a managed IT platform serving " + "MSP customers. You answer security questions in clear, practical language.\n\n" + "Topics you cover:\n" + "- Security best practices (patching, MFA, zero trust, EDR config)\n" + "- Compliance guidance (SOC 2, HIPAA, GDPR, SOX, NIST CSF)\n" + "- Incident response procedures and playbook recommendations\n" + "- Risk assessment methodology and prioritization\n" + "- Vendor security evaluation\n" + "- Security architecture review\n\n" + "Style: concise, practical, actionable. Cite frameworks where relevant. " + "You are NOT producing structured data for automation — you are helping " + "humans understand security concepts and make informed decisions." + ), + ) + + # Customer-facing explanation agent + explainer_agent = Agent( + client=llm_client, + name="CustomerExplainer", + instructions=( + "You translate technical security incidents into clear, non-technical " + "language for MSP customers, executives, and board members.\n\n" + "Rules:\n" + "- NO jargon: replace 'lateral movement' with 'the attacker moved between computers'\n" + "- NO acronyms without explanation: 'EDR (endpoint protection software)'\n" + "- Include: what happened, what's at risk, what we're doing about it\n" + "- Tone: professional, reassuring, transparent — not alarmist\n" + "- Mention specific actions being taken to protect the customer\n" + "- Keep it under 200 words unless asked for more detail\n\n" + "Remember: the reader may be a CFO or office manager, not an IT person." + ), + ) + + # ── Session stores ────────────────────────────────────────────────── + agent_sessions: dict[str, AgentSession] = {} + session_turns: dict[str, int] = {} + # Store typed results per session for cross-tool reference + session_data: dict[str, dict] = {} + + # ── FastMCP server ────────────────────────────────────────────────── + mcp_server = FastMCP("HybridSecurityServer") + + # ════════════════════════════════════════════════════════════════════ + # STRICT-SCHEMA TOOLS — output is Pydantic-validated JSON + # These drive automation: isolation APIs, SLA timers, ticket creation + # ════════════════════════════════════════════════════════════════════ + + @mcp_server.tool + async def triage_alert( + raw_alert: Annotated[str, "Raw alert text from SIEM/EDR/IDS to triage"], + ctx: Context, + ) -> str: + """[STRICT SCHEMA] Triage a raw security alert into a structured + SecurityAlert with 12 typed fields (severity enum, IOC list, etc.). + Output drives SLA timers and downstream automation — must be exact.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — triaging alert (strict schema)") + + _ensure_session(session_id, agent_sessions, session_turns, session_data) + session_turns[session_id] += 1 + + async with triage_agent: + response: AgentResponse = await triage_agent.run( + f"Triage this security alert and extract structured data:\n\n{raw_alert}", + options={"response_format": SecurityAlert}, + ) + alert = cast(SecurityAlert, response.value) + + # Store for cross-tool reference + session_data[session_id]["last_alert"] = alert.model_dump(mode="json") + session_data[session_id]["last_alert_model"] = alert + + result = alert.model_dump(mode="json") + await ctx.info( + f"✅ SecurityAlert validated: severity={alert.severity.value}, " + f"host={alert.affected_hostname}, {len(alert.indicators_of_compromise)} IOCs" + ) + return json.dumps(result, indent=2) + + @mcp_server.tool + async def assess_threat( + alert_json: Annotated[str, "SecurityAlert JSON from triage_alert output"], + ctx: Context, + ) -> str: + """[STRICT SCHEMA] Analyze a triaged SecurityAlert with threat intelligence. + Returns ThreatAssessment with 11 typed fields (MITRE ATT&CK vectors, + threat score 0-100, confidence %). Drives playbook selection.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — assessing threat (strict schema)") + + _ensure_session(session_id, agent_sessions, session_turns, session_data) + session_turns[session_id] += 1 + + async with threat_agent: + response: AgentResponse = await threat_agent.run( + f"Analyze this security alert with threat intelligence:\n\n{alert_json}", + options={"response_format": ThreatAssessment}, + ) + threat = cast(ThreatAssessment, response.value) + + session_data[session_id]["last_threat"] = threat.model_dump(mode="json") + session_data[session_id]["last_threat_model"] = threat + + result = threat.model_dump(mode="json") + await ctx.info( + f"✅ ThreatAssessment validated: vector={threat.attack_vector.value}, " + f"score={threat.threat_score:.0f}/100, confidence={threat.confidence_pct:.0f}%" + ) + return json.dumps(result, indent=2) + + @mcp_server.tool + async def create_response( + threat_json: Annotated[str, "ThreatAssessment JSON from assess_threat output"], + affected_endpoints: Annotated[str, "Comma-separated hostnames of affected endpoints"], + compromised_accounts: Annotated[str, "Comma-separated compromised user accounts"], + data_at_risk: Annotated[bool, "Whether sensitive/regulated data may be exposed"], + ctx: Context, + ) -> str: + """[STRICT SCHEMA] Generate an automated incident response plan. + Returns IncidentResponse with 15 typed fields including ordered + remediation actions, SLA deadlines, and compliance flags. + This output DIRECTLY drives endpoint isolation and account lockout APIs.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — creating response plan (strict schema)") + + _ensure_session(session_id, agent_sessions, session_turns, session_data) + session_turns[session_id] += 1 + + endpoints_list = [e.strip() for e in affected_endpoints.split(",")] + accounts_list = [a.strip() for a in compromised_accounts.split(",")] + + prompt = ( + "Generate the incident response plan with specific automated actions.\n\n" + f"Threat assessment:\n{threat_json}\n\n" + f"Affected endpoints: {json.dumps(endpoints_list)}\n" + f"Compromised accounts: {json.dumps(accounts_list)}\n" + f"Data at risk: {data_at_risk}\n" + ) + + # Pull in alert context if available + alert_data = session_data[session_id].get("last_alert") + if alert_data: + prompt += f"\nOriginal alert context: severity={alert_data.get('severity')}, tenant={alert_data.get('tenant_id')}\n" + + async with response_agent: + response: AgentResponse = await response_agent.run( + prompt, + options={"response_format": IncidentResponse}, + ) + incident = cast(IncidentResponse, response.value) + + session_data[session_id]["last_response"] = incident.model_dump(mode="json") + session_data[session_id]["last_response_model"] = incident + + result = incident.model_dump(mode="json") + await ctx.info( + f"✅ IncidentResponse validated: {incident.priority.value}, " + f"{len(incident.immediate_actions)} actions, " + f"isolate={incident.endpoints_to_isolate}" + ) + return json.dumps(result, indent=2) + + # ════════════════════════════════════════════════════════════════════ + # NATURAL-LANGUAGE TOOLS — output is free-form text for humans + # No Pydantic validation — prose answers for questions/explanations + # ════════════════════════════════════════════════════════════════════ + + @mcp_server.tool + async def ask_security_advisor( + question: Annotated[str, "Any security question — policy, best practices, compliance, architecture"], + ctx: Context, + ) -> str: + """[NATURAL LANGUAGE] Ask a senior security advisor any question. + Returns a free-form, practical answer. Good for: security policy, + compliance guidance, best practices, risk methodology, architecture. + The advisor remembers conversation context within the session.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — consulting advisor (natural language)") + + _ensure_session(session_id, agent_sessions, session_turns, session_data) + session_turns[session_id] += 1 + + session = agent_sessions[session_id] + + # Add incident context if we have it + context_note = "" + if session_data[session_id].get("last_alert"): + alert = session_data[session_id]["last_alert"] + context_note = ( + f"\n\n[Context: there is an active incident — alert {alert.get('alert_id')}, " + f"severity {alert.get('severity')}, affecting {alert.get('affected_hostname')} " + f"at tenant {alert.get('tenant_id')}. Reference this if the question relates to it.]" + ) + + async with advisor_agent: + response = await advisor_agent.run( + question + context_note, + session=session, + ) + + return response.text + + @mcp_server.tool + async def explain_for_customer( + incident_context: Annotated[str, "Technical incident details or JSON to translate"], + audience: Annotated[str, "Who will read this: 'executive', 'office_manager', 'board', or 'technical_lead'"] = "executive", + ctx: Context = None, + ) -> str: + """[NATURAL LANGUAGE] Translate a technical security incident into + plain English for a non-technical audience. Outputs a clear, jargon-free + explanation suitable for customer communication. Specify the audience + to adjust the tone and detail level.""" + session_id = ctx.session_id if ctx else "default" + if ctx: + await ctx.info(f"Session {session_id[:8]}… — drafting customer explanation (natural language)") + + _ensure_session(session_id, agent_sessions, session_turns, session_data) + session_turns[session_id] += 1 + + # Build context from session data + full_context = f"Audience: {audience}\n\nTechnical details:\n{incident_context}" + + # Enrich with any stored incident data + stored = session_data[session_id] + if stored.get("last_response"): + resp = stored["last_response"] + full_context += ( + f"\n\nIncident response plan:\n" + f"- Priority: {resp.get('priority')}\n" + f"- Severity: {resp.get('final_severity')}\n" + f"- Actions taken: {json.dumps(resp.get('immediate_actions', []))}\n" + f"- Customer communication required: {resp.get('customer_communication_required')}\n" + f"- Compliance flags: {json.dumps(resp.get('compliance_flags', []))}\n" + ) + + async with explainer_agent: + response = await explainer_agent.run(full_context) + + return response.text + + # ── Utility tools ─────────────────────────────────────────────────── + + @mcp_server.tool + async def get_session_info(ctx: Context) -> dict: + """Get metadata about the current session — turn count, available data.""" + session_id = ctx.session_id + stored = session_data.get(session_id, {}) + return { + "session_id": session_id, + "turn_count": session_turns.get(session_id, 0), + "has_alert": "last_alert" in stored, + "has_threat_assessment": "last_threat" in stored, + "has_response_plan": "last_response" in stored, + "alert_severity": stored.get("last_alert", {}).get("severity"), + "incident_priority": stored.get("last_response", {}).get("priority"), + } + + @mcp_server.tool + async def reset_session(ctx: Context) -> str: + """Clear all data and conversation history for the current session.""" + session_id = ctx.session_id + agent_sessions.pop(session_id, None) + session_turns.pop(session_id, None) + session_data.pop(session_id, None) + return f"Session {session_id[:8]}… cleared — all data and history reset." + + # ── Launch ────────────────────────────────────────────────────────── + + print("──────────────────────────────────────────────────────────────") + print("🛡️ Hybrid MCP Security Server") + print(" URL: http://localhost:8002/mcp") + print() + print(" STRICT-SCHEMA tools (automation-critical):") + print(" • triage_alert → SecurityAlert (12 fields)") + print(" • assess_threat → ThreatAssessment (11 fields)") + print(" • create_response → IncidentResponse (15 fields)") + print() + print(" NATURAL-LANGUAGE tools (human-facing):") + print(" • ask_security_advisor → free-form advisory") + print(" • explain_for_customer → plain-English translation") + print() + print(" Utility: get_session_info, reset_session") + print("──────────────────────────────────────────────────────────────") + + mcp_server.run(transport="streamable-http", host="0.0.0.0", port=8002) + + +def _ensure_session( + session_id: str, + sessions: dict[str, AgentSession], + turns: dict[str, int], + data: dict[str, dict], +) -> None: + """Lazily initialize session stores.""" + if session_id not in sessions: + sessions[session_id] = AgentSession() + turns[session_id] = 0 + data[session_id] = {} + + +if __name__ == "__main__": + main() diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py b/agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py new file mode 100644 index 000000000..f4c8ed0d1 --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server_langgraph.py @@ -0,0 +1,352 @@ +""" +Part 8 — Stateful LangGraph Agent as MCP Server (Cross-Framework Interop) + +A LangGraph-based Technical Architect agent exposed as a **stateful** +MCP endpoint using PrefectHQ FastMCP v3. Each MCP session gets its own +LangGraph conversation thread (via MemorySaver checkpointer), so the +agent remembers prior turns without the client resending history. + +Cross-framework interoperability: the agent is built entirely with +LangGraph (ReAct loop + tools) but any MCP client can consume it — +MAF, AutoGen, CrewAI — regardless of framework. + +Architecture: + ┌──────────────────────────────────────────────────────┐ + │ FastMCP (stateful, streamable-http, port 8003) │ + │ │ + │ MCP session_id → LangGraph thread_id │ + │ ↓ │ + │ LangGraph ReAct Agent + MemorySaver (conversation) │ + │ • evaluate_architecture_pattern │ + │ • estimate_migration_effort │ + │ • recommend_tech_stack │ + │ │ + │ Tools: │ + │ ask_architect — multi-turn architecture Q&A │ + │ get_session_info — session metadata │ + │ reset_conversation — clear history │ + └──────────────────────────────────────────────────────┘ + +Prerequisites: + uv sync (installs langgraph, langchain-openai, langchain-core, fastmcp) + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_server_langgraph.py +""" + +import os +import sys +from typing import Annotated + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from fastmcp import FastMCP +from fastmcp.server.context import Context +from langchain_core.messages import HumanMessage, SystemMessage +from langchain_core.tools import tool as lc_tool +from langchain_openai import AzureChatOpenAI +from langgraph.checkpoint.memory import MemorySaver +from langgraph.graph import END, START, MessagesState, StateGraph +from langgraph.prebuilt import ToolNode + + +# ═══════════════════════════════════════════════════════════════════════════ +# LangGraph Tools — architecture domain +# ═══════════════════════════════════════════════════════════════════════════ + + +@lc_tool +def evaluate_architecture_pattern(pattern: str) -> str: + """Evaluate an architecture pattern and return pros, cons, and guidance. + Supported patterns: microservices, monolith, serverless, event-driven.""" + patterns = { + "microservices": ( + "Microservices Pattern Evaluation:\n" + "✓ Independent deployment & scaling per service\n" + "✓ Technology diversity — polyglot-friendly\n" + "✓ Fault isolation — one service failure doesn't cascade\n" + "✗ Distributed system complexity (network latency, partial failures)\n" + "✗ Data consistency challenges (eventual consistency, saga pattern)\n" + "✗ Operational overhead — requires mature DevOps practices\n" + "• Best for: Large teams, complex domains, high scalability needs\n" + "• Maturity required: HIGH — need CI/CD, observability, service mesh" + ), + "monolith": ( + "Monolith Pattern Evaluation:\n" + "✓ Simple to develop, test, and deploy initially\n" + "✓ Single codebase — easy to understand end-to-end\n" + "✓ No inter-service communication overhead\n" + "✗ Scaling requires scaling the entire application\n" + "✗ Technology lock-in — one stack for everything\n" + "✗ Deployment risk — small change requires full redeploy\n" + "• Best for: Small teams, MVPs, low-complexity domains\n" + "• Consider: Modular monolith as a stepping stone" + ), + "serverless": ( + "Serverless Pattern Evaluation:\n" + "✓ Zero infrastructure management — cloud handles scaling\n" + "✓ Pay-per-execution — cost-efficient for variable workloads\n" + "✓ Rapid development — focus on business logic only\n" + "✗ Cold start latency — first invocation can be slow\n" + "✗ Vendor lock-in — tied to cloud provider's runtime\n" + "✗ Debugging complexity — distributed tracing is essential\n" + "• Best for: Event-driven workloads, APIs, batch processing\n" + "• Watch out: Long-running tasks may exceed time limits" + ), + "event-driven": ( + "Event-Driven Pattern Evaluation:\n" + "✓ Loose coupling — producers and consumers are independent\n" + "✓ Natural fit for async workflows and real-time processing\n" + "✓ Excellent scalability — consumers scale independently\n" + "✗ Event ordering and exactly-once delivery are hard\n" + "✗ Debugging event chains requires correlation IDs\n" + "✗ Schema evolution needs careful versioning\n" + "• Best for: Real-time analytics, IoT, workflow orchestration\n" + "• Key tech: Kafka, Event Hubs, EventGrid, Pub/Sub" + ), + } + key = pattern.strip().lower().replace("-", "").replace("_", "").replace(" ", "") + for k, v in patterns.items(): + if k in key or key in k: + return v + return ( + f"Pattern '{pattern}' not in evaluation database.\n" + f"Available patterns: {', '.join(patterns.keys())}.\n" + "Provide a general assessment based on the pattern name." + ) + + +@lc_tool +def estimate_migration_effort(source: str, target: str, components: int) -> str: + """Estimate migration effort from source to target architecture. + source/target: e.g. 'monolith', 'microservices', 'serverless'. + components: number of major components/modules to migrate.""" + complexity_map = { + ("monolith", "microservices"): ("HIGH", 18, 45), + ("monolith", "serverless"): ("MEDIUM-HIGH", 12, 35), + ("monolith", "event-driven"): ("MEDIUM", 10, 30), + ("microservices", "serverless"): ("LOW-MEDIUM", 6, 15), + } + key = (source.strip().lower(), target.strip().lower()) + complexity, base_weeks, base_cost_k = complexity_map.get(key, ("MEDIUM", 8, 25)) + weeks = base_weeks + (components * 2) + cost_k = base_cost_k + (components * 8) + return ( + f"Migration Effort Estimate: {source} → {target}\n" + f"─────────────────────────────────────────\n" + f"• Components to migrate: {components}\n" + f"• Complexity: {complexity}\n" + f"• Estimated timeline: {weeks} weeks\n" + f"• Estimated cost: ${cost_k}K–${int(cost_k * 1.4)}K\n" + f"• Recommended team size: {max(3, components // 2)} engineers\n" + f"• Phases: Discovery (2w) → Pilot (4w) → Migration ({weeks - 8}w) → Validation (2w)\n" + f"• Risk factors: Data migration, API compatibility, testing coverage" + ) + + +@lc_tool +def recommend_tech_stack(domain: str, scale: str) -> str: + """Recommend a technology stack for a given domain and scale. + domain: e.g. 'e-commerce', 'fintech', 'healthcare', 'saas'. + scale: 'small', 'medium', 'large', 'enterprise'.""" + stacks = { + "e-commerce": { + "frontend": "Next.js + React + Tailwind CSS", + "backend": "Node.js / Python FastAPI (microservices)", + "database": "PostgreSQL (catalog) + Redis (sessions/cache) + Elasticsearch (search)", + "cloud": "Azure AKS or AWS EKS (Kubernetes)", + "ai_ml": "Azure OpenAI (personalization) + Azure ML (recommendations)", + "messaging": "Azure Service Bus / Kafka (order events)", + "observability": "OpenTelemetry + Grafana + Azure Monitor", + }, + "fintech": { + "frontend": "React + TypeScript + Material UI", + "backend": "Java Spring Boot / .NET (regulatory compliance)", + "database": "PostgreSQL (ACID) + TimescaleDB (time-series)", + "cloud": "Azure (compliance certifications) + Azure Confidential Computing", + "ai_ml": "Azure OpenAI (fraud detection) + custom ML models", + "messaging": "Kafka (event sourcing, audit trail)", + "observability": "Datadog + Azure Monitor + custom audit logging", + }, + "healthcare": { + "frontend": "React + HIPAA-compliant hosting", + "backend": "Python FastAPI / .NET (HL7 FHIR support)", + "database": "Azure Health Data Services (FHIR) + PostgreSQL", + "cloud": "Azure (HIPAA BAA) + Azure Confidential Ledger", + "ai_ml": "Azure OpenAI (clinical notes) + Azure Health Bot", + "messaging": "Azure Service Bus (secure messaging)", + "observability": "Azure Monitor + HIPAA-compliant logging", + }, + } + stack = stacks.get(domain.strip().lower(), stacks["e-commerce"]) + scale_notes = { + "small": "Start with managed services, minimize ops overhead", + "medium": "Introduce Kubernetes, add CI/CD automation", + "large": "Multi-region, auto-scaling, dedicated SRE team", + "enterprise": "Global deployment, multi-cloud strategy, SOC2/ISO compliance", + } + note = scale_notes.get(scale.strip().lower(), scale_notes["medium"]) + lines = [ + f"Tech Stack Recommendation: {domain} ({scale} scale)", + "─" * 50, + ] + for layer, tech in stack.items(): + lines.append(f" {layer.replace('_', '/').title():20s} → {tech}") + lines.append(f"\n Scale guidance: {note}") + return "\n".join(lines) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Build the LangGraph agent (ReAct loop with MemorySaver) +# ═══════════════════════════════════════════════════════════════════════════ + +SYSTEM_PROMPT = ( + "You are a Technical Architect with deep expertise in cloud-native systems, " + "distributed architectures, and technology strategy. When asked a question:\n" + "1. Use your tools to gather concrete data (patterns, estimates, tech stacks)\n" + "2. Synthesize the tool outputs into a cohesive, actionable recommendation\n" + "3. Be specific — cite numbers, timelines, and trade-offs\n" + "4. You are in a multi-turn conversation. Build on prior context rather than " + "starting fresh — reference earlier discussion points.\n" + "Keep responses concise and structured." +) + +tools = [evaluate_architecture_pattern, estimate_migration_effort, recommend_tech_stack] + +# MemorySaver keeps conversation history per thread_id in memory. +# Each MCP session maps to a unique LangGraph thread. +checkpointer = MemorySaver() + + +def _build_model() -> AzureChatOpenAI: + return AzureChatOpenAI( + azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], + api_key=os.environ["AZURE_OPENAI_API_KEY"], + azure_deployment=os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1"), + api_version=os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview"), + ) + + +def _build_graph(): + """Build a LangGraph ReAct agent with architecture tools and checkpointer.""" + llm = _build_model() + llm_with_tools = llm.bind_tools(tools) + + def agent_node(state: MessagesState): + messages = [SystemMessage(content=SYSTEM_PROMPT)] + state["messages"] + response = llm_with_tools.invoke(messages) + return {"messages": [response]} + + def should_continue(state: MessagesState): + last = state["messages"][-1] + if hasattr(last, "tool_calls") and last.tool_calls: + return "tools" + return END + + graph = StateGraph(MessagesState) + graph.add_node("agent", agent_node) + graph.add_node("tools", ToolNode(tools)) + graph.add_edge(START, "agent") + graph.add_conditional_edges("agent", should_continue) + graph.add_edge("tools", "agent") + # Compile WITH checkpointer — state persisted per thread_id + return graph.compile(checkpointer=checkpointer) + + +# Compile the graph at module level +langgraph_agent = _build_graph() + +# Track turn count per session +session_turns: dict[str, int] = {} + + +# ═══════════════════════════════════════════════════════════════════════════ +# Expose via stateful MCP (port 8003, PrefectHQ FastMCP) +# ═══════════════════════════════════════════════════════════════════════════ + +mcp_server = FastMCP("TechnicalArchitect") + + +@mcp_server.tool +async def ask_architect( + question: Annotated[str, "Architecture question or follow-up message"], + ctx: Context, +) -> str: + """Ask the Technical Architect for architecture advice. The conversation + is maintained across calls within the same MCP session — just send the + new message, no need to resend history.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — processing message") + + if session_id not in session_turns: + session_turns[session_id] = 0 + await ctx.info("New conversation thread started") + + session_turns[session_id] += 1 + turn = session_turns[session_id] + await ctx.info(f"Processing turn {turn}") + + # Use the MCP session_id as the LangGraph thread_id. + # The MemorySaver checkpointer accumulates messages per thread, + # so the agent sees full conversation history automatically. + config = {"configurable": {"thread_id": session_id}} + result = await langgraph_agent.ainvoke( + {"messages": [HumanMessage(content=question)]}, + config=config, + ) + # Extract the last AI message (skip tool-call messages) + for msg in reversed(result["messages"]): + content = getattr(msg, "content", None) + if content and not getattr(msg, "tool_calls", None): + return content if isinstance(content, str) else str(content) + return "No response generated." + + +@mcp_server.tool +async def get_session_info(ctx: Context) -> dict: + """Get metadata about the current conversation session.""" + session_id = ctx.session_id + turn_count = session_turns.get(session_id, 0) + return { + "session_id": session_id, + "turn_count": turn_count, + "has_history": turn_count > 0, + } + + +@mcp_server.tool +async def reset_conversation(ctx: Context) -> str: + """Clear the conversation history for the current session.""" + session_id = ctx.session_id + if session_id in session_turns: + del session_turns[session_id] + return f"Conversation cleared for session {session_id[:8]}…" + return "No conversation history to clear." + + +# ═══════════════════════════════════════════════════════════════════════════ + +if __name__ == "__main__": + print("=" * 70) + print("🏗️ LangGraph Technical Architect — Stateful MCP Server (port 8003)") + print("=" * 70) + print(" Endpoint: http://localhost:8003/mcp") + print(" Transport: Streamable HTTP (stateful sessions)") + print(" Tools: ask_architect, get_session_info, reset_conversation") + print(" Framework: LangGraph (cross-framework interop via MCP)") + print() + print(" Architecture tools available to the agent:") + print(" • evaluate_architecture_pattern") + print(" • estimate_migration_effort") + print(" • recommend_tech_stack") + print() + print(" Session memory: MCP session_id → LangGraph thread_id") + print(" → conversation history maintained server-side") + print(" → clients send only the new message each turn") + print("=" * 70) + mcp_server.run(transport="streamable-http", host="0.0.0.0", port=8003) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py b/agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py new file mode 100644 index 000000000..fc96263fc --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py @@ -0,0 +1,210 @@ +""" +Stateful Agent-as-MCP Server — multi-turn conversations with session memory. + +This script exposes an agent-framework Agent as a stateful MCP HTTP server +using PrefectHQ's FastMCP v3. Each MCP client session gets its own +AgentSession, so conversation history is preserved across tool calls. + +The server exposes three MCP tools: + • chat_with_expert — send a message; the expert remembers prior turns + • get_session_info — inspect turn count / session metadata + • reset_conversation — clear history for the current session + +Architecture: + ┌────────────────────────────────────────────┐ + │ FastMCP (stateful, streamable-http) │ + │ │ + │ MCP session_id → AgentSession (history) │ + │ ↓ │ + │ Agent("ExpertAdvisor") + domain tools │ + │ • analyze_risk │ + │ • get_market_data │ + │ • summarize_findings │ + └────────────────────────────────────────────┘ + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python mcp_server_stateful.py +""" + +import os +import sys +from typing import Annotated + +from dotenv import load_dotenv + +# ── Load credentials ──────────────────────────────────────────────────────── +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import Agent, tool +from agent_framework._sessions import AgentSession +from agent_framework.azure import AzureOpenAIChatClient +from fastmcp import FastMCP +from fastmcp.server.context import Context + +# ── Domain tools the agent can use ────────────────────────────────────────── + + +@tool(approval_mode="never_require") +def analyze_risk( + scenario: Annotated[str, "A brief description of the business scenario to assess"], +) -> Annotated[str, "A risk analysis summary"]: + """Analyze business risk for a given scenario and return a structured assessment.""" + return ( + f"Risk Analysis for: {scenario}\n" + "─────────────────────────────\n" + "• Market Risk: MEDIUM — competitive landscape is evolving\n" + "• Financial Risk: LOW — strong cash position\n" + "• Operational Risk: MEDIUM — supply chain dependencies noted\n" + "• Regulatory Risk: LOW — compliant with current frameworks\n" + "• Overall Rating: MEDIUM\n" + "• Recommendation: Proceed with standard due diligence." + ) + + +@tool(approval_mode="never_require") +def get_market_data( + sector: Annotated[str, "Industry sector to look up, e.g. 'technology', 'healthcare'"], +) -> Annotated[str, "Market data summary for the sector"]: + """Retrieve current market data for a given industry sector.""" + data = { + "technology": "Tech sector: YTD +18%, P/E 28x, top movers: AI infrastructure, cloud security.", + "healthcare": "Healthcare sector: YTD +8%, P/E 22x, top movers: GLP-1 drugs, digital health.", + "energy": "Energy sector: YTD +5%, P/E 14x, top movers: renewables, grid storage.", + "finance": "Finance sector: YTD +12%, P/E 16x, top movers: fintech, digital payments.", + } + return data.get( + sector.lower(), + f"No data available for sector '{sector}'. Known sectors: {', '.join(data.keys())}", + ) + + +@tool(approval_mode="never_require") +def summarize_findings( + text: Annotated[str, "The text to summarize"], + max_sentences: Annotated[int, "Maximum number of sentences in the summary"] = 3, +) -> Annotated[str, "A concise summary"]: + """Produce a concise summary of the provided text.""" + sentences = [s.strip() for s in text.replace("\n", " ").split(".") if s.strip()] + summary = ". ".join(sentences[:max_sentences]) + "." + return f"Summary ({max_sentences} sentences max): {summary}" + + +# ── Build agent and server ────────────────────────────────────────────────── + + +def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + print(f"🔧 Azure OpenAI endpoint : {endpoint}") + print(f"🔧 Deployment : {deployment}") + + # 1) Create the agent-framework Agent + llm_client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + agent = Agent( + client=llm_client, + name="ExpertAdvisor", + description=( + "An expert business advisor that can analyze risk, retrieve market data, " + "and summarize findings. Remembers the full conversation history." + ), + instructions=( + "You are an expert business strategy advisor. Use your tools to provide " + "thorough, data-backed analysis. Always call analyze_risk for risk questions, " + "get_market_data for sector insights, and summarize_findings to condense results.\n\n" + "IMPORTANT: You are in a multi-turn conversation. Reference and build upon " + "earlier turns. If the user asks you to summarize or follow up, recall " + "what was discussed previously." + ), + tools=[analyze_risk, get_market_data, summarize_findings], + ) + + # 2) Session store: MCP session_id → AgentSession + # Each MCP client gets its own conversation thread. + agent_sessions: dict[str, AgentSession] = {} + # Track turn count per session (simple counter) + session_turns: dict[str, int] = {} + + # 3) Create the FastMCP server (stateful by default — sessions persist) + mcp_server = FastMCP("StatefulExpertAdvisor") + + # ── MCP tools ─────────────────────────────────────────────────────── + + @mcp_server.tool + async def chat_with_expert( + message: Annotated[str, "Your message to the expert advisor"], + ctx: Context, + ) -> str: + """Send a message to the Expert Advisor. The conversation history is + maintained across calls within the same MCP session, enabling + multi-turn follow-ups and context-aware responses.""" + session_id = ctx.session_id + await ctx.info(f"Session {session_id[:8]}… — processing message") + + # Get-or-create AgentSession for this MCP session + if session_id not in agent_sessions: + agent_sessions[session_id] = AgentSession() + session_turns[session_id] = 0 + await ctx.info("New conversation started") + + session_turns[session_id] += 1 + turn = session_turns[session_id] + await ctx.info(f"Processing turn {turn}") + + session = agent_sessions[session_id] + + # Run the agent with session history + async with agent: + response = await agent.run(message, session=session) + + return response.text + + @mcp_server.tool + async def get_session_info(ctx: Context) -> dict: + """Get metadata about the current MCP session and conversation state.""" + session_id = ctx.session_id + turn_count = session_turns.get(session_id, 0) + return { + "session_id": session_id, + "turn_count": turn_count, + "has_history": session_id in agent_sessions, + } + + @mcp_server.tool + async def reset_conversation(ctx: Context) -> str: + """Clear the conversation history for the current MCP session.""" + session_id = ctx.session_id + if session_id in agent_sessions: + del agent_sessions[session_id] + session_turns.pop(session_id, None) + return f"Conversation history cleared for session {session_id[:8]}…" + return "No conversation history to clear." + + # ── Launch ────────────────────────────────────────────────────────── + + print("──────────────────────────────────────────────────") + print("🚀 Stateful MCP Agent Server") + print(" URL: http://localhost:8002/mcp") + print(" Transport: Streamable HTTP (stateful sessions)") + print(" Tools: chat_with_expert, get_session_info, reset_conversation") + print("──────────────────────────────────────────────────") + + mcp_server.run(transport="streamable-http", host="0.0.0.0", port=8002) + + +if 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b/agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py new file mode 100644 index 000000000..34d0e3a11 --- /dev/null +++ b/agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py @@ -0,0 +1,436 @@ +""" +Part 9 — Conversational Group Chat: User ↔ Local Agent + LangGraph Agent + +Demonstrates MAF's GroupChatBuilder orchestrating a multi-agent +discussion between: + + 👔 BusinessStrategist — local MAF agent (Azure OpenAI) + 🏗️ TechnicalArchitect — LangGraph agent served via MCP (port 8003) + 📋 Planner — local MAF agent that synthesizes the plan + 🎯 Facilitator — LLM orchestrator that decides who speaks next + +The Facilitator routes the conversation: experts discuss first, then +the Planner delivers a consolidated plan inline — no separate +synthesis step needed. This is more efficient than post-hoc synthesis. + +Simulates a multi-turn conversation with predefined questions: + 1. User poses an initial topic → experts discuss → Planner delivers plan + 2. User asks a follow-up → experts discuss deeper → Planner updates plan + 3. The TechnicalArchitect (LangGraph) remembers prior turns + via MemorySaver — the proxy sends only the NEW message + +This showcases stateful cross-framework orchestration: + • MCP session provides a persistent connection + • LangGraph MemorySaver keeps conversation history server-side + • MCPProxyAgent sends only the latest message (not full history) + • GroupChatBuilder treats both local and remote agents identically + • Planner delivers the plan as a participant (no extra LLM call) + +Prerequisites: + mcp_server_langgraph.py must be running on http://localhost:8003/mcp + +Usage: + cd agentic_ai/agents/mcp_agent_demo + uv run python workflow_group_chat.py +""" + +import asyncio +import os +import sys +import uuid +from typing import Any, cast + +from dotenv import load_dotenv + +# Load credentials from the shared mcp/.env +env_path = os.path.join(os.path.dirname(__file__), "..", "..", "..", "mcp", ".env") +load_dotenv(env_path) + +from agent_framework import ( + AgentResponse, + AgentResponseUpdate, + BaseAgent, + Content, + MCPStreamableHTTPTool, + Message, +) +from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.orchestrations import GroupChatBuilder + + +# ═══════════════════════════════════════════════════════════════════════════ +# MCPProxyAgent — BaseAgent that bridges to a remote MCP tool +# ═══════════════════════════════════════════════════════════════════════════ + + +class MCPProxyAgent(BaseAgent): + """BaseAgent that forwards the latest message to a remote MCP tool. + + Because the MCP server is **stateful** (maintains conversation history + per session via MemorySaver), we only need to send the LATEST message + — not the full conversation history. The server accumulates context + automatically. + """ + + def __init__( + self, + *, + mcp_tool: MCPStreamableHTTPTool, + tool_name: str, + name: str = "mcp_proxy", + description: str | None = None, + param_name: str = "question", + **kwargs: Any, + ) -> None: + super().__init__(name=name, description=description, **kwargs) + self._mcp_tool = mcp_tool + self._tool_name = tool_name + self._param_name = param_name + + # Must be a regular def — GroupChatBuilder iterates with + # ``async for update in agent.run(stream=True)`` + def run(self, messages: Any = None, *, stream: bool = False, **kwargs: Any) -> Any: + if stream: + return self._run_stream(messages) + return self._run_impl(messages) + + async def _run_impl(self, messages: Any) -> AgentResponse: + result_text = await self._call(messages) + return AgentResponse( + messages=[Message("assistant", [result_text], author_name=self.name)], + response_id=f"proxy-{uuid.uuid4().hex[:8]}", + agent_id=self.id, + ) + + async def _run_stream(self, messages: Any): + result_text = await self._call(messages) + yield AgentResponseUpdate( + contents=[Content.from_text(result_text)], + role="assistant", + author_name=self.name, + agent_id=self.id, + response_id=f"proxy-{uuid.uuid4().hex[:8]}", + ) + + async def _call(self, messages: Any) -> str: + """Extract only the LATEST message and forward to the stateful MCP tool. + + The server-side LangGraph agent (with MemorySaver) already has the + full conversation history for this session, so we only send the + new content. This is the key difference from the old implementation + which manually forwarded the entire conversation each turn. + """ + latest_text = _extract_latest_message(messages) + result = await self._mcp_tool.call_tool( + self._tool_name, **{self._param_name: latest_text} + ) + if isinstance(result, (list, tuple)): + return "\n".join( + c.text for c in result if hasattr(c, "text") and c.text + ) or str(result) + return result.text if hasattr(result, "text") and result.text else str(result) + + +def _extract_latest_message(messages: Any) -> str: + """Extract just the last meaningful message from the conversation. + + The orchestrator passes the full conversation as list[Message]. + We only need the LAST message since the MCP server is stateful + and already has prior turns in its MemorySaver checkpointer. + """ + if isinstance(messages, str): + return messages + if isinstance(messages, Message): + return messages.text or "" + if isinstance(messages, (list, tuple)): + # Walk backwards to find the last message with content + for m in reversed(messages): + if isinstance(m, Message): + text = m.text or "" + if text.strip(): + name = m.author_name or m.role or "someone" + return f"[{name}]: {text}" + elif isinstance(m, str) and m.strip(): + return m + return str(messages) if messages else "" + + +# ═══════════════════════════════════════════════════════════════════════════ +# MAIN — Conversational Group Chat with follow-ups +# ═══════════════════════════════════════════════════════════════════════════ + + +async def _run_group_chat( + topic: str, + strategist: Any, + architect: MCPProxyAgent, + planner: Any, + facilitator: Any, + turn_label: str = "Initial Discussion", +) -> int: + """Run one round of group chat. + + Returns: + round_count — number of visible participant messages. + """ + workflow = GroupChatBuilder( + participants=[strategist, architect, planner], + orchestrator_agent=facilitator, + max_rounds=6, + intermediate_outputs=True, + ).build() + + print() + print(f"━{'━' * 76}━") + print(f" 📋 {turn_label}") + print(f"━{'━' * 76}━") + print(f" {topic[:120]}{'…' if len(topic) > 120 else ''}") + print() + + round_num = 0 + async for event in workflow.run(topic, stream=True): + if event.type == "output": + data = event.data + if not isinstance(data, list): + continue + for msg in cast(list[Message], data): + name = msg.author_name or "" + text = msg.text or "" + if not text.strip(): + continue + + # Skip the initial user message echo + if not name or msg.role == "user": + continue + + if name == "BusinessStrategist": + icon, framework = "👔", "MAF Agent" + elif name == "TechnicalArchitect": + icon, framework = "🏗️", "LangGraph via MCP" + elif name == "Planner": + icon, framework = "📋", "MAF Agent" + else: + icon, framework = "💬", name + + round_num += 1 + print(f"{'═' * 78}") + print(f" Round {round_num} — {icon} {name} [{framework}]") + print(f"{'═' * 78}") + print() + print(text) + print() + + return round_num + + + + + +# ═══════════════════════════════════════════════════════════════════════════ +# Predefined conversation — simulates a multi-turn user interaction +# ═══════════════════════════════════════════════════════════════════════════ + +CONVERSATION = [ + { + "role": "user", + "label": "Initial Topic", + "message": ( + "Our company is a mid-size e-commerce retailer with a legacy " + "on-premise monolithic Java application serving 2M monthly users. " + "We want to migrate to a cloud-native architecture to improve " + "scalability, reduce operational costs, and enable AI-powered " + "personalization. We have a $500K budget and 12-month timeline. " + "Discuss the strategy, architecture, and implementation approach." + ), + }, + { + "role": "user", + "label": "Follow-up: Risk & Phasing", + "message": ( + "Thanks for the plan. I have concerns about risk. " + "What are the biggest risks with this migration, and how " + "should we phase the rollout to minimize disruption to our " + "existing customers? Can we keep the monolith running in " + "parallel during the transition?" + ), + }, +] + + +async def main() -> None: + endpoint = os.environ.get("AZURE_OPENAI_ENDPOINT") + api_key = os.environ.get("AZURE_OPENAI_API_KEY") + deployment = os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4.1") + api_version = os.environ.get("AZURE_OPENAI_API_VERSION", "2025-03-01-preview") + + if not endpoint or not api_key: + print("ERROR: Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY in mcp/.env") + sys.exit(1) + + mcp_server_url = "http://localhost:8003/mcp" + + print("=" * 78) + print("🗣️ Conversational Group Chat — User ↔ MAF + LangGraph Agents") + print("=" * 78) + print() + print(" Simulating a multi-turn conversation with predefined questions.") + print(" The TechnicalArchitect (LangGraph) remembers prior turns via") + print(" MemorySaver — the proxy sends only the NEW message each turn.") + print() + + # ── Connect to the stateful LangGraph MCP server ───────────────────── + async with MCPStreamableHTTPTool( + name="technical_architect", + description="Remote Technical Architect (LangGraph) via MCP", + url=mcp_server_url, + ) as mcp_tool: + print(f"🔗 Connected to LangGraph MCP server at {mcp_server_url}") + print(f" Available tools: {[t.name for t in mcp_tool.functions]}") + print(f" Session: stateful (server maintains conversation history)") + print() + + client = AzureOpenAIChatClient( + api_key=api_key, + endpoint=endpoint, + deployment_name=deployment, + api_version=api_version, + ) + + # ── Participant 1: Local Business Strategist (MAF Agent) ───────── + strategist = client.as_agent( + name="BusinessStrategist", + instructions=( + "You are a Business Strategist specializing in digital " + "transformation and cloud migration. Focus on:\n" + "• Business impact and ROI analysis\n" + "• Customer experience implications\n" + "• Risk mitigation and change management\n" + "• Competitive advantage and market positioning\n" + "• Timeline and budget considerations\n\n" + "Build on what others have said in the conversation. " + "Be concise but insightful — 2-3 paragraphs max." + ), + ) + + # ── Participant 2: Remote Technical Architect (LangGraph via MCP) ─ + architect = MCPProxyAgent( + mcp_tool=mcp_tool, + tool_name="ask_architect", + param_name="question", + name="TechnicalArchitect", + description=( + "Technical Architect providing architecture design, " + "technology stack recommendations, cloud infrastructure " + "planning, and migration strategies. Built with LangGraph, " + "served via MCP." + ), + ) + + # ── Participant 3: Planner (synthesizes discussion into plan) ──── + planner = client.as_agent( + name="Planner", + instructions=( + "You are a senior project planner participating in a " + "strategy meeting alongside a BusinessStrategist and " + "TechnicalArchitect.\n\n" + "When the facilitator calls on you, synthesize the team's " + "discussion into a structured, actionable plan:\n" + " • Numbered phases with clear deliverables\n" + " • Timeline and budget allocation\n" + " • Key milestones and success metrics\n" + " • Top risks with mitigations\n" + " • Concrete next steps\n\n" + "Incorporate BOTH business and technical recommendations " + "from your teammates. Write it as a professional plan " + "the client can act on. Be comprehensive but concise." + ), + ) + + # ── Orchestrator: Lightweight Facilitator ───────────────────────── + facilitator = client.as_agent( + name="Facilitator", + instructions=( + "You are a discussion facilitator for a strategy meeting. " + "Three experts are available:\n\n" + " • BusinessStrategist — business impact, ROI, risk, " + "go-to-market\n" + " • TechnicalArchitect — architecture, tech stack, " + "migration patterns, infrastructure\n" + " • Planner — synthesizes discussion into an actionable " + "plan\n\n" + "Your workflow:\n" + " 1. Start with BusinessStrategist for business perspective\n" + " 2. Then TechnicalArchitect for technical depth\n" + " 3. Alternate if needed for a richer discussion\n" + " 4. When both experts have contributed enough, call on " + "Planner to deliver the consolidated plan\n" + " 5. After Planner delivers the plan, TERMINATE\n\n" + "Do NOT let Planner speak until the experts have " + "had a meaningful exchange." + ), + ) + + # ── Print participant info ──────────────────────────────────────── + print("━" * 78) + print("👥 PARTICIPANTS") + print("━" * 78) + print(" 👔 BusinessStrategist — local MAF agent (Azure OpenAI)") + print(" 🏗️ TechnicalArchitect — LangGraph agent via MCP (port 8003)") + print(" 📋 Planner — local MAF agent (synthesizes plan)") + print(" 🎯 Facilitator — LLM orchestrator (decides who speaks)") + print("━" * 78) + print() + + # ── Run the predefined conversation ─────────────────────────────── + total_rounds = 0 + + for turn_num, turn in enumerate(CONVERSATION, 1): + is_last_turn = turn_num == len(CONVERSATION) + + # Print what the "user" is saying + print() + print(f"{'▓' * 78}") + print(f" 👤 USER (Turn {turn_num})") + print(f"{'▓' * 78}") + print(f" {turn['message']}") + print() + + rounds = await _run_group_chat( + turn["message"], strategist, architect, planner, + facilitator, + turn_label=f"Discussion #{turn_num}: {turn['label']}", + ) + total_rounds += rounds + + print("━" * 78) + print(f"✅ Discussion #{turn_num} complete — {rounds} rounds.") + print("━" * 78) + + if not is_last_turn: + print() + print("━" * 78) + print(" 💡 The TechnicalArchitect remembers the prior discussion") + print(" (stateful MCP session with LangGraph MemorySaver)") + print(" Only the NEW question is sent — server keeps history.") + print() + print(" 📝 The user reviews the plan and asks a follow-up...") + print("━" * 78) + + # ── Summary ────────────────────────────────────────────────────── + print() + print("=" * 78) + print(f"📊 Session Summary: {len(CONVERSATION)} discussions, {total_rounds} total rounds") + print("=" * 78) + print(" Key points demonstrated:") + print(" • Multi-turn conversation with predefined questions") + print(" • Planner delivers plan inline — no separate synthesis step") + print(" • Facilitator orchestrates: experts discuss → Planner delivers") + print(" • LangGraph agent remembers prior context (MemorySaver)") + print(" • MCP session provides persistent stateful connection") + print(" • Proxy sends only latest message (server keeps history)") + print(" • Cross-framework: MAF orchestrator + LangGraph agent via MCP") + print("=" * 78) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/agentic_ai/applications/pyproject.toml b/agentic_ai/applications/pyproject.toml index eacd63989..cc51fc58f 100644 --- a/agentic_ai/applications/pyproject.toml +++ b/agentic_ai/applications/pyproject.toml @@ -5,7 +5,7 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.12" dependencies = [ - "agent-framework==1.0.0b260130", + "agent-framework==1.0.0rc1", "azure-ai-evaluation>=1.14.0", "azure-ai-projects>=2.0.0b2", "azure-cosmos==4.9.0", diff --git a/agentic_ai/applications/requirements.txt b/agentic_ai/applications/requirements.txt index 01d01b186..deaa31f3b 100644 --- a/agentic_ai/applications/requirements.txt +++ b/agentic_ai/applications/requirements.txt @@ -1,10 +1,10 @@ # This file was autogenerated by uv via the following command: -# uv pip compile pyproject.toml -o requirements.txt +# uv pip compile pyproject.toml -o requirements.txt --prerelease=allow a2a-sdk==0.3.12 # via agent-framework-a2a ag-ui-protocol==0.1.10 # via agent-framework-ag-ui -agent-framework==1.0.0b260130 +agent-framework==1.0.0rc1 # via applications (pyproject.toml) agent-framework-a2a==1.0.0b251114 # via agent-framework-core @@ -18,11 +18,15 @@ agent-framework-azure-ai-search==1.0.0b260130 # via agent-framework-core agent-framework-azurefunctions==1.0.0b260130 # via agent-framework-core +agent-framework-bedrock==1.0.0b260219 + # via agent-framework-core agent-framework-chatkit==1.0.0b260130 # via agent-framework-core +agent-framework-claude==1.0.0b260219 + # via agent-framework-core agent-framework-copilotstudio==1.0.0b251114 # via agent-framework-core -agent-framework-core==1.0.0b260130 +agent-framework-core==1.0.0rc1 # via # agent-framework # agent-framework-a2a @@ -31,15 +35,19 @@ agent-framework-core==1.0.0b260130 # agent-framework-azure-ai # agent-framework-azure-ai-search # agent-framework-azurefunctions + # agent-framework-bedrock # agent-framework-chatkit + # agent-framework-claude # agent-framework-copilotstudio # agent-framework-declarative # agent-framework-devui # agent-framework-durabletask + # agent-framework-foundry-local # agent-framework-github-copilot # agent-framework-lab # agent-framework-mem0 # agent-framework-ollama + # agent-framework-orchestrations # agent-framework-purview # agent-framework-redis agent-framework-declarative==1.0.0b260130 @@ -50,6 +58,8 @@ agent-framework-durabletask==1.0.0b260130 # via # agent-framework-azurefunctions # agent-framework-core +agent-framework-foundry-local==1.0.0b260219 + # via agent-framework-core agent-framework-github-copilot==1.0.0b260130 # via agent-framework-core agent-framework-lab==1.0.0b251024 @@ -58,6 +68,8 @@ agent-framework-mem0==1.0.0b251114 # via agent-framework-core agent-framework-ollama==1.0.0b260130 # via agent-framework-core +agent-framework-orchestrations==1.0.0b260219 + # via agent-framework-core agent-framework-purview==1.0.0b251114 # via agent-framework-core agent-framework-redis==1.0.0b251114 @@ -80,6 +92,7 @@ anthropic==0.78.0 anyio==4.11.0 # via # anthropic + # claude-agent-sdk # httpx # mcp # openai @@ -99,10 +112,11 @@ azure-ai-agents==1.2.0b5 # via agent-framework-azure-ai azure-ai-evaluation==1.14.0 # via applications (pyproject.toml) -azure-ai-projects==2.0.0b2 +azure-ai-projects==2.0.0b3 # via # applications (pyproject.toml) # agent-framework-azure-ai + # agent-framework-core azure-common==1.1.28 # via azure-search-documents azure-core==1.36.0 @@ -134,6 +148,7 @@ azure-identity==1.26.0b1 # via # agent-framework-core # azure-ai-evaluation + # azure-ai-projects # azure-monitor-opentelemetry-exporter # durabletask-azuremanaged azure-monitor-opentelemetry==1.8.6 @@ -152,6 +167,13 @@ blinker==1.9.0 # via # flask # streamlit +boto3==1.42.54 + # via agent-framework-bedrock +botocore==1.42.54 + # via + # agent-framework-bedrock + # boto3 + # s3transfer cachetools==5.5.2 # via # google-auth @@ -169,6 +191,8 @@ cffi==2.0.0 # powerfx charset-normalizer==3.4.4 # via requests +claude-agent-sdk==0.1.39 + # via agent-framework-claude click==8.3.1 # via # flask @@ -213,6 +237,8 @@ flask==3.0.3 # via # applications (pyproject.toml) # flasgger +foundry-local-sdk==0.5.1 + # via agent-framework-foundry-local frozenlist==1.8.0 # via # aiohttp @@ -258,6 +284,7 @@ httpx==0.28.1 # agent-framework-purview # anthropic # azure-ai-evaluation + # foundry-local-sdk # mcp # ollama # openai @@ -297,6 +324,10 @@ jiter==0.12.0 # via # anthropic # openai +jmespath==1.1.0 + # via + # boto3 + # botocore joblib==1.5.3 # via nltk jsonpath-ng==1.7.0 @@ -315,6 +346,7 @@ markupsafe==3.0.3 mcp==1.26.0 # via # 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Import compatibility with the new RC1 API +2. Constructor signatures work correctly +3. Session management (AgentThread → AgentSession migration) +4. Streaming API (run_stream → run(stream=True)) +5. Event processing (unified WorkflowEvent model) +6. MagenticBuilder new constructor-based API +""" + +import asyncio +import json +import logging +import os +import sys +from typing import Any, Dict, List +from unittest.mock import AsyncMock, MagicMock, patch, PropertyMock + +import pytest + +# Ensure the agent packages are importable +sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'agentic_ai')) + + +# ============================================================================= +# Section 1: Import Compatibility Tests +# ============================================================================= + + +class TestImportCompatibility: + """Verify all imports resolve correctly against agent-framework 1.0.0rc1.""" + + def test_core_framework_imports(self): + """Core agent_framework symbols used across all agents.""" + from agent_framework import ( + Agent, + AgentSession, + ChatOptions, + MCPStreamableHTTPTool, + WorkflowCheckpoint, + WorkflowEvent, + CheckpointStorage, + ResponseStream, + WorkflowRunResult, + Role, + Message, + Content, + ) + assert Agent is not None + assert AgentSession is not None + assert ChatOptions is not None + + def test_azure_client_import(self): + """AzureOpenAIChatClient import.""" + from agent_framework.azure import AzureOpenAIChatClient + assert AzureOpenAIChatClient is not None + + def test_orchestration_imports(self): + """Orchestration symbols moved to agent_framework_orchestrations.""" + from agent_framework_orchestrations import ( + MagenticBuilder, + MagenticOrchestratorEvent, + MagenticOrchestratorEventType, + MagenticPlanReviewRequest, + MagenticPlanReviewResponse, + ) + assert MagenticBuilder is not None + + def test_single_agent_import(self): + """Single agent module loads cleanly.""" + from agents.agent_framework.single_agent import Agent + assert Agent is not None + + def test_handoff_agent_import(self): + """Handoff multi-domain agent module loads cleanly.""" + from agents.agent_framework.multi_agent.handoff_multi_domain_agent import Agent + assert Agent is not None + + def test_reflection_agent_import(self): + """Reflection agent module loads cleanly.""" + from agents.agent_framework.multi_agent.reflection_agent import Agent + assert Agent is not None + + def test_magentic_group_import(self): + """Magentic group agent module loads cleanly.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + assert Agent is not None + + def test_default_agent_export(self): + """Default agent_framework package re-exports single_agent.Agent.""" + from agents.agent_framework import Agent + from agents.agent_framework.single_agent import Agent as SingleAgent + assert Agent is SingleAgent + + def test_old_symbols_removed(self): + """Verify old symbols (ChatAgent, AgentThread, etc.) are NOT available.""" + import agent_framework + assert not hasattr(agent_framework, 'ChatAgent'), "ChatAgent should be renamed to Agent" + assert not hasattr(agent_framework, 'AgentThread'), "AgentThread should be renamed to AgentSession" + assert not hasattr(agent_framework, 'ChatMessage'), "ChatMessage should be renamed to Message" + + +# ============================================================================= +# Section 2: Agent Constructor Tests +# ============================================================================= + + +class TestAgentConstructors: + """Verify agent constructors use the new RC1 signatures.""" + + def test_framework_agent_constructor_signature(self): + """Agent(client, instructions, *, name, tools, default_options).""" + from agent_framework import Agent + import inspect + sig = inspect.signature(Agent.__init__) + params = list(sig.parameters.keys()) + # First positional after self should be 'client' + assert params[1] == 'client', f"First param should be 'client', got {params[1]}" + assert 'name' in params + assert 'tools' in params + assert 'default_options' in params + + def test_chat_options_model_id(self): + """ChatOptions accepts model_id for specifying the model.""" + from agent_framework import ChatOptions + opts = ChatOptions(model_id="gpt-4o") + assert opts["model_id"] == "gpt-4o" + + def test_azure_client_constructor(self): + """AzureOpenAIChatClient accepts deployment_name, endpoint, api_version.""" + from agent_framework.azure import AzureOpenAIChatClient + import inspect + sig = inspect.signature(AzureOpenAIChatClient.__init__) + params = list(sig.parameters.keys()) + assert 'api_key' in params + assert 'deployment_name' in params + assert 'endpoint' in params + assert 'api_version' in params + assert 'credential' in params + + +# ============================================================================= +# Section 3: Session Management Tests (AgentThread → AgentSession) +# ============================================================================= + + +class TestSessionManagement: + """Verify AgentSession works correctly as the replacement for AgentThread.""" + + def test_agent_session_creation(self): + """AgentSession can be created with default or custom session_id.""" + from agent_framework import AgentSession + session = AgentSession() + assert session.session_id is not None + + custom = AgentSession(session_id="test-123") + assert custom.session_id == "test-123" + + def test_agent_session_serialization(self): + """AgentSession supports to_dict() and from_dict().""" + from agent_framework import AgentSession + session = AgentSession(session_id="test-serialize") + data = session.to_dict() + assert isinstance(data, dict) + assert data.get("session_id") == "test-serialize" + + restored = AgentSession.from_dict(data) + assert restored.session_id == "test-serialize" + + def test_agent_has_create_session(self): + """Agent has create_session() method (replaces get_new_thread).""" + from agent_framework import Agent + assert hasattr(Agent, 'create_session') + assert callable(getattr(Agent, 'create_session')) + + def test_agent_no_old_thread_methods(self): + """Agent does NOT have old thread methods.""" + from agent_framework import Agent + assert not hasattr(Agent, 'get_new_thread') + assert not hasattr(Agent, 'deserialize_thread') + assert not hasattr(Agent, 'run_stream') + + +# ============================================================================= +# Section 4: Streaming API Tests +# ============================================================================= + + +class TestStreamingAPI: + """Verify run(stream=True) replaces the old run_stream() method.""" + + def test_agent_run_supports_stream_kwarg(self): + """Agent.run() accepts stream=True.""" + from agent_framework import Agent + import inspect + sig = inspect.signature(Agent.run) + assert 'stream' in sig.parameters + + def test_agent_run_supports_session_kwarg(self): + """Agent.run() accepts session=... (replaces thread=...).""" + from agent_framework import Agent + import inspect + sig = inspect.signature(Agent.run) + assert 'session' in sig.parameters + + def test_response_stream_has_updates(self): + """ResponseStream has .updates property for iteration.""" + from agent_framework import ResponseStream + assert hasattr(ResponseStream, 'updates') + + def test_agent_response_update_structure(self): + """AgentResponseUpdate has contents and text.""" + from agent_framework import AgentResponseUpdate + update = AgentResponseUpdate() + assert hasattr(update, 'contents') + assert hasattr(update, 'text') + + def test_content_types(self): + """Content factory methods exist for function_call and function_result.""" + from agent_framework import Content + assert hasattr(Content, 'from_function_call') + assert hasattr(Content, 'from_function_result') + assert hasattr(Content, 'from_text') + + +# ============================================================================= +# Section 5: Workflow Event Model Tests (Unified WorkflowEvent) +# ============================================================================= + + +class TestWorkflowEventModel: + """Verify the unified WorkflowEvent replaces old typed event classes.""" + + def test_workflow_event_output(self): + """WorkflowEvent.output() factory creates an output event.""" + from agent_framework import WorkflowEvent + event = WorkflowEvent.output("executor_1", "final answer") + assert event.type == "output" + assert event.executor_id == "executor_1" + assert event.data == "final answer" + + def test_workflow_event_executor_completed(self): + """WorkflowEvent.executor_completed() for completed agent responses.""" + from agent_framework import WorkflowEvent + event = WorkflowEvent.executor_completed("crm_billing", "response text") + assert event.type == "executor_completed" + assert event.executor_id == "crm_billing" + + def test_workflow_event_emit(self): + """WorkflowEvent.emit() for streaming data.""" + from agent_framework import WorkflowEvent + event = WorkflowEvent.emit("agent_1", "streaming chunk") + assert event.type == "data" + assert event.executor_id == "agent_1" + + def test_workflow_event_request_info(self): + """WorkflowEvent.request_info() for plan reviews.""" + from agent_framework import WorkflowEvent + event = WorkflowEvent.request_info( + "req-123", "magentic_manager", "plan data", response_type=str + ) + assert event.type == "request_info" + assert event.request_id == "req-123" + + def test_old_event_types_removed(self): + """Old event types no longer exist in agent_framework.""" + import agent_framework + for old_type in ['WorkflowOutputEvent', 'AgentRunEvent', 'AgentRunUpdateEvent', 'RequestInfoEvent']: + assert not hasattr(agent_framework, old_type), f"{old_type} should no longer exist" + + def test_magentic_orchestrator_event_type(self): + """MagenticOrchestratorEventType enum values.""" + from agent_framework_orchestrations import MagenticOrchestratorEventType + values = {e.value for e in MagenticOrchestratorEventType} + assert "plan_created" in values + assert "replanned" in values + assert "progress_ledger_updated" in values + + +# ============================================================================= +# Section 6: MagenticBuilder API Tests +# ============================================================================= + + +class TestMagenticBuilder: + """Verify MagenticBuilder uses the new constructor-based API.""" + + def test_magentic_builder_requires_participants(self): + """MagenticBuilder requires participants as a constructor kwarg.""" + from agent_framework_orchestrations import MagenticBuilder + with pytest.raises(TypeError, match="participants"): + MagenticBuilder() # Should fail without participants + + def test_magentic_builder_constructor_params(self): + """MagenticBuilder constructor accepts all needed params.""" + from agent_framework_orchestrations import MagenticBuilder + import inspect + sig = inspect.signature(MagenticBuilder.__init__) + params = set(sig.parameters.keys()) - {'self'} + assert 'participants' in params + assert 'manager_agent' in params + assert 'max_round_count' in params + assert 'max_stall_count' in params + assert 'max_reset_count' in params + assert 'progress_ledger_prompt' in params + assert 'checkpoint_storage' in params + assert 'enable_plan_review' in params + + def test_magentic_builder_has_build(self): + """MagenticBuilder has build() method.""" + from agent_framework_orchestrations import MagenticBuilder + assert hasattr(MagenticBuilder, 'build') + + def test_magentic_builder_no_old_methods(self): + """MagenticBuilder no longer has the old chained builder methods.""" + from agent_framework_orchestrations import MagenticBuilder + # Old API: .participants().with_manager().with_checkpointing() + # The .participants() was a chained method, now it's a constructor param + # with_manager() is gone, replaced by manager_agent constructor param + assert not hasattr(MagenticBuilder, 'with_manager'), \ + "with_manager() replaced by constructor params" + + +# ============================================================================= +# Section 7: Workflow.run() API Tests +# ============================================================================= + + +class TestWorkflowRunAPI: + """Verify Workflow.run() supports the new unified API.""" + + def test_workflow_run_signature(self): + """Workflow.run() accepts stream, responses, checkpoint_id, checkpoint_storage.""" + from agent_framework import Workflow + import inspect + sig = inspect.signature(Workflow.run) + params = set(sig.parameters.keys()) - {'self'} + assert 'stream' in params + assert 'responses' in params + assert 'checkpoint_id' in params + assert 'checkpoint_storage' in params + + def test_workflow_no_old_streaming_methods(self): + """Workflow no longer has run_stream/run_stream_from_checkpoint/send_responses_streaming.""" + from agent_framework import Workflow + assert not hasattr(Workflow, 'run_stream'), "run_stream replaced by run(stream=True)" + assert not hasattr(Workflow, 'run_stream_from_checkpoint'), \ + "run_stream_from_checkpoint replaced by run(checkpoint_id=..., stream=True)" + assert not hasattr(Workflow, 'send_responses_streaming'), \ + "send_responses_streaming replaced by run(responses=..., stream=True)" + + +# ============================================================================= +# Section 8: MCP Tool Tests +# ============================================================================= + + +class TestMCPTools: + """Verify MCPStreamableHTTPTool API compatibility.""" + + def test_mcp_tool_constructor(self): + """MCPStreamableHTTPTool accepts name, url, request_timeout + headers/timeout via kwargs.""" + from agent_framework import MCPStreamableHTTPTool + import inspect + sig = inspect.signature(MCPStreamableHTTPTool.__init__) + params = set(sig.parameters.keys()) - {'self'} + assert 'name' in params + assert 'url' in params + assert 'request_timeout' in params + # headers and timeout are now accepted via **kwargs + assert 'kwargs' in params, "MCPStreamableHTTPTool should accept **kwargs for headers/timeout" + + def test_mcp_tool_is_context_manager(self): + """MCPStreamableHTTPTool supports async context manager protocol.""" + from agent_framework import MCPStreamableHTTPTool + assert hasattr(MCPStreamableHTTPTool, '__aenter__') + assert hasattr(MCPStreamableHTTPTool, '__aexit__') + + +# ============================================================================= +# Section 9: Agent Module Integration Tests (mock-based) +# ============================================================================= + + +class TestSingleAgentIntegration: + """Test single_agent.Agent can be constructed and wired up correctly.""" + + def test_single_agent_init(self): + """Agent can be instantiated with state_store and session_id.""" + from agents.agent_framework.single_agent import Agent + store = {} + agent = Agent(state_store=store, session_id="test-session") + assert agent.session_id == "test-session" + assert agent._agent is None + assert agent._session is None + assert agent._initialized is False + + def test_single_agent_has_tool_tracking(self): + """Agent has tool tracking methods from mixin.""" + from agents.agent_framework.single_agent import Agent + agent = Agent(state_store={}, session_id="test") + assert hasattr(agent, 'get_tool_calls') + assert hasattr(agent, 'track_function_call_start') + assert hasattr(agent, 'finalize_tool_tracking') + + def test_single_agent_websocket_manager(self): + """set_websocket_manager injects the WS manager.""" + from agents.agent_framework.single_agent import Agent + agent = Agent(state_store={}, session_id="test") + mock_manager = MagicMock() + agent.set_websocket_manager(mock_manager) + assert agent._ws_manager is mock_manager + + +class TestHandoffAgentIntegration: + """Test handoff agent construction and domain configuration.""" + + def test_handoff_agent_init(self): + """Handoff agent initializes with domain tracking.""" + from agents.agent_framework.multi_agent.handoff_multi_domain_agent import Agent + store = {} + agent = Agent(state_store=store, session_id="handoff-test") + assert agent._current_domain is None + assert agent._domain_agents == {} + assert agent._domain_sessions == {} + + def test_handoff_agent_restores_domain(self): + """Handoff agent restores current domain from state_store.""" + from agents.agent_framework.multi_agent.handoff_multi_domain_agent import Agent + store = {"handoff-test_current_domain": "crm_billing"} + agent = Agent(state_store=store, session_id="handoff-test") + assert agent._current_domain == "crm_billing" + + def test_handoff_detects_handoff_phrases(self): + """Handoff detection logic works.""" + from agents.agent_framework.multi_agent.handoff_multi_domain_agent import Agent + agent = Agent(state_store={}, session_id="test") + assert agent._detect_handoff_request( + "This is outside my area. Let me connect you with the right specialist." + ) + assert not agent._detect_handoff_request( + "Your billing summary shows $50.00 due." + ) + + +class TestReflectionAgentIntegration: + """Test reflection agent construction.""" + + def test_reflection_agent_init(self): + """Reflection agent initializes with primary + reviewer agents.""" + from agents.agent_framework.multi_agent.reflection_agent import Agent + agent = Agent(state_store={}, session_id="reflect-test") + assert agent._primary_agent is None + assert agent._reviewer is None + assert agent._session is None + assert agent._max_refinements == 2 + + def test_reflection_approval_detection(self): + """Reviewer approval detection works.""" + from agents.agent_framework.multi_agent.reflection_agent import Agent + agent = Agent(state_store={}, session_id="test") + assert agent._is_approved("APPROVE - looks good") + assert not agent._is_approved("Needs improvement on point 3") + + +class TestMagenticGroupIntegration: + """Test magentic group agent construction.""" + + def test_magentic_agent_init(self): + """Magentic agent initializes with configuration.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + agent = Agent(state_store={}, session_id="magentic-test") + assert agent._max_round_count == 4 + assert agent._max_stall_count == 2 + + def test_magentic_checkpoint_storage(self): + """DictCheckpointStorage works for checkpointing.""" + from agents.agent_framework.multi_agent.magentic_group import DictCheckpointStorage + from agent_framework import WorkflowCheckpoint + + backing = {} + storage = DictCheckpointStorage(backing) + assert storage.latest_checkpoint_id is None + + def test_magentic_sanitize_final_answer(self): + """FINAL_ANSWER prefix is stripped from workflow output.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + agent = Agent(state_store={}, session_id="test") + assert agent._sanitize_final_answer("FINAL_ANSWER: Hello world") == "Hello world" + assert agent._sanitize_final_answer("Just a normal response") == "Just a normal response" + assert agent._sanitize_final_answer(None) is None + + def test_magentic_extract_text_from_event(self): + """Text extraction handles various WorkflowEvent data formats.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + from agent_framework import WorkflowEvent + + # Plain string data + event = WorkflowEvent.output("exec1", "Hello") + assert Agent._extract_text_from_event(event) == "Hello" + + # Object with .text attribute + mock_msg = MagicMock() + mock_msg.text = "From message" + event2 = WorkflowEvent.output("exec1", mock_msg) + assert Agent._extract_text_from_event(event2) == "From message" + + def test_magentic_process_output_event(self): + """_process_workflow_event handles output events correctly.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + from agent_framework import WorkflowEvent + + agent = Agent(state_store={}, session_id="test") + ws_manager = AsyncMock() + agent.set_websocket_manager(ws_manager) + + event = WorkflowEvent.output("exec1", "Final answer text") + asyncio.get_event_loop().run_until_complete( + agent._process_workflow_event(event) + ) + + ws_manager.broadcast.assert_called() + call_args = ws_manager.broadcast.call_args[0] + assert call_args[0] == "test" # session_id + msg = call_args[1] + assert msg["type"] == "final_result" + + def test_magentic_process_data_event(self): + """_process_workflow_event handles streaming data events.""" + from agents.agent_framework.multi_agent.magentic_group import Agent + from agent_framework import WorkflowEvent + + agent = Agent(state_store={}, session_id="test") + ws_manager = AsyncMock() + agent.set_websocket_manager(ws_manager) + + # Create a mock data event with text + mock_data = MagicMock() + mock_data.text = "streaming token" + event = WorkflowEvent.emit("crm_billing", mock_data) + + asyncio.get_event_loop().run_until_complete( + agent._process_workflow_event(event) + ) + + # Should broadcast agent_start and agent_token + assert ws_manager.broadcast.call_count >= 1 + + +# ============================================================================= +# Section 10: Framework Version Check +# ============================================================================= + + +class TestFrameworkVersion: + """Verify we're running against the expected RC1 version.""" + + def test_agent_framework_version(self): + """agent_framework is at 1.0.0rc1.""" + import agent_framework + assert agent_framework.__version__ == "1.0.0rc1", \ + f"Expected 1.0.0rc1, got {agent_framework.__version__}" + + def test_agent_framework_core_installed(self): + """agent-framework-core is installed as a dependency.""" + import importlib.metadata + version = importlib.metadata.version('agent-framework-core') + assert version == "1.0.0rc1", \ + f"Expected 1.0.0rc1, got {version}" From 640c3d305262d1d7f4241f0df282b5438ca650db Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 09:04:22 -0800 Subject: [PATCH 20/37] Fix: use GH_PAT for auto-merge to trigger downstream workflows Pushes made by GITHUB_TOKEN don't trigger other workflows (GitHub Actions limitation to prevent infinite loops). This meant the auto-merge into int-agentic never triggered promote-to-main.yml. Now uses secrets.GH_PAT with fallback to GITHUB_TOKEN so: 1. If GH_PAT is configured: merge triggers promote-to-main.yml 2. If not: merge still works, but promotion PR must be created manually --- .github/workflows/orchestrate.yml | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/.github/workflows/orchestrate.yml b/.github/workflows/orchestrate.yml index ec08ff610..62b25093f 100644 --- a/.github/workflows/orchestrate.yml +++ b/.github/workflows/orchestrate.yml @@ -285,6 +285,14 @@ jobs: # After a successful full deploy from a *-dev branch, automatically # merge the open PR from that branch into int-agentic. This triggers # the promote-to-main workflow which creates/updates a PR to main. + # + # IMPORTANT: Uses GH_PAT (a Personal Access Token or GitHub App token) + # instead of GITHUB_TOKEN so the push to int-agentic triggers + # downstream workflows (promote-to-main.yml). Pushes made by + # GITHUB_TOKEN do NOT trigger other workflows — this is a GitHub + # Actions limitation to prevent infinite loops. + # If GH_PAT is not configured, falls back to GITHUB_TOKEN (merge + # still works, but promote-to-main won't auto-trigger). # ──────────────────────────────────────────────────────────────────── auto-merge: needs: [pipeline-config, integration-tests, agent-evaluation] @@ -299,7 +307,7 @@ jobs: steps: - name: Merge dev PR into int-agentic env: - GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GH_TOKEN: ${{ secrets.GH_PAT || secrets.GITHUB_TOKEN }} run: | BRANCH="${{ github.ref_name }}" echo "🔍 Looking for open PR: ${BRANCH} → int-agentic" From c0811302d9978064724ceb4b14105635a8acb7d0 Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 09:26:47 -0800 Subject: [PATCH 21/37] Add workflow_dispatch trigger to promote-to-main Allows manual triggering when auto-trigger is missed (e.g. after GITHUB_TOKEN-based merges that don't fire downstream workflows). --- .github/workflows/promote-to-main.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/promote-to-main.yml b/.github/workflows/promote-to-main.yml index 5eef368a3..ea115f1a2 100644 --- a/.github/workflows/promote-to-main.yml +++ b/.github/workflows/promote-to-main.yml @@ -15,6 +15,7 @@ on: - '**/*.md' - 'docs/**' - 'LICENSE' + workflow_dispatch: # Allow manual trigger permissions: contents: read From 87a77db99c751a634b3326b38635155c42cbc789 Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 11:28:25 -0800 Subject: [PATCH 22/37] Fix: Cosmos DB auth + remove phantom reflection_workflow_agent 1. MCP Cosmos DB: Replace AzureCliCredential (not available in containers) with ManagedIdentityCredential (when AZURE_CLIENT_ID is set) or DefaultAzureCredential as fallback. This fixes the 'Azure CLI not found on path' error in production Container Apps. 2. Backend: Remove reflection_workflow_agent from DEFAULT_AVAILABLE_AGENTS and descriptions - the module file doesn't exist, causing it to show as a broken option in the UI agent selector. --- agentic_ai/applications/backend.py | 2 -- mcp/_backend_cosmos.py | 10 +++++++--- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/agentic_ai/applications/backend.py b/agentic_ai/applications/backend.py index 718177b0b..3c9bdd4ed 100644 --- a/agentic_ai/applications/backend.py +++ b/agentic_ai/applications/backend.py @@ -207,7 +207,6 @@ def verify_token(authorization: str | None = Header(None, alias="Authorization") "agents.agent_framework.multi_agent.handoff_multi_domain_agent", "agents.agent_framework.multi_agent.magentic_group", "agents.agent_framework.multi_agent.reflection_agent", - "agents.agent_framework.multi_agent.reflection_workflow_agent", ] def _load_available_agents() -> List[str]: @@ -403,7 +402,6 @@ async def list_agents(token: str = Depends(verify_token)): "handoff_multi_domain_agent": "Multi-agent system with domain-specific specialists and handoffs", "magentic_group": "MagenticOne-style orchestrator with specialist agents", "reflection_agent": "Agent with built-in reflection and self-critique", - "reflection_workflow_agent": "Workflow-based reflection with quality assurance gates", } description = descriptions.get(parts, "Agent module") diff --git a/mcp/_backend_cosmos.py b/mcp/_backend_cosmos.py index 999b0e51c..1c9458687 100644 --- a/mcp/_backend_cosmos.py +++ b/mcp/_backend_cosmos.py @@ -11,7 +11,7 @@ from datetime import datetime from dotenv import load_dotenv from azure.cosmos import CosmosClient, ContainerProxy, exceptions -from azure.identity import AzureCliCredential +from azure.identity import DefaultAzureCredential, ManagedIdentityCredential # Load environment variables load_dotenv() @@ -42,10 +42,14 @@ def get_cosmos_client() -> CosmosClient: - """Get or create Cosmos DB client using Azure CLI credentials.""" + """Get or create Cosmos DB client using managed identity or default credentials.""" global _cosmos_client if _cosmos_client is None: - credential = AzureCliCredential() + azure_client_id = os.getenv("AZURE_CLIENT_ID") + if azure_client_id: + credential = ManagedIdentityCredential(client_id=azure_client_id) + else: + credential = DefaultAzureCredential() _cosmos_client = CosmosClient(COSMOSDB_ENDPOINT, credential=credential) return _cosmos_client From cbadc7de3f9363c4dd2807c8eca944a619438eca Mon Sep 17 00:00:00 2001 From: James Nguyen Date: Mon, 23 Feb 2026 11:52:20 -0800 Subject: [PATCH 23/37] chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22321497108 --- .github/workflows/orchestrate.yml | 10 +++++++++- .github/workflows/promote-to-main.yml | 1 + agentic_ai/applications/backend.py | 2 -- mcp/_backend_cosmos.py | 10 +++++++--- 4 files changed, 17 insertions(+), 6 deletions(-) diff --git a/.github/workflows/orchestrate.yml b/.github/workflows/orchestrate.yml index ec08ff610..62b25093f 100644 --- a/.github/workflows/orchestrate.yml +++ b/.github/workflows/orchestrate.yml @@ -285,6 +285,14 @@ jobs: # After a successful full deploy from a *-dev branch, automatically # merge the open PR from that branch into int-agentic. This triggers # the promote-to-main workflow which creates/updates a PR to main. + # + # IMPORTANT: Uses GH_PAT (a Personal Access Token or GitHub App token) + # instead of GITHUB_TOKEN so the push to int-agentic triggers + # downstream workflows (promote-to-main.yml). Pushes made by + # GITHUB_TOKEN do NOT trigger other workflows — this is a GitHub + # Actions limitation to prevent infinite loops. + # If GH_PAT is not configured, falls back to GITHUB_TOKEN (merge + # still works, but promote-to-main won't auto-trigger). # ──────────────────────────────────────────────────────────────────── auto-merge: needs: [pipeline-config, integration-tests, agent-evaluation] @@ -299,7 +307,7 @@ jobs: steps: - name: Merge dev PR into int-agentic env: - GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GH_TOKEN: ${{ secrets.GH_PAT || secrets.GITHUB_TOKEN }} run: | BRANCH="${{ github.ref_name }}" echo "🔍 Looking for open PR: ${BRANCH} → int-agentic" diff --git a/.github/workflows/promote-to-main.yml b/.github/workflows/promote-to-main.yml index 5eef368a3..ea115f1a2 100644 --- a/.github/workflows/promote-to-main.yml +++ b/.github/workflows/promote-to-main.yml @@ -15,6 +15,7 @@ on: - '**/*.md' - 'docs/**' - 'LICENSE' + workflow_dispatch: # Allow manual trigger permissions: contents: read diff --git a/agentic_ai/applications/backend.py b/agentic_ai/applications/backend.py index 718177b0b..3c9bdd4ed 100644 --- a/agentic_ai/applications/backend.py +++ b/agentic_ai/applications/backend.py @@ -207,7 +207,6 @@ def verify_token(authorization: str | None = Header(None, alias="Authorization") "agents.agent_framework.multi_agent.handoff_multi_domain_agent", "agents.agent_framework.multi_agent.magentic_group", "agents.agent_framework.multi_agent.reflection_agent", - "agents.agent_framework.multi_agent.reflection_workflow_agent", ] def _load_available_agents() -> List[str]: @@ -403,7 +402,6 @@ async def list_agents(token: str = Depends(verify_token)): "handoff_multi_domain_agent": "Multi-agent system with domain-specific specialists and handoffs", "magentic_group": "MagenticOne-style orchestrator with specialist agents", "reflection_agent": "Agent with built-in reflection and self-critique", - "reflection_workflow_agent": "Workflow-based reflection with quality assurance gates", } description = descriptions.get(parts, "Agent module") diff --git a/mcp/_backend_cosmos.py b/mcp/_backend_cosmos.py index 999b0e51c..1c9458687 100644 --- a/mcp/_backend_cosmos.py +++ b/mcp/_backend_cosmos.py @@ -11,7 +11,7 @@ from datetime import datetime from dotenv import load_dotenv from azure.cosmos import CosmosClient, ContainerProxy, exceptions -from azure.identity import AzureCliCredential +from azure.identity import DefaultAzureCredential, ManagedIdentityCredential # Load environment variables load_dotenv() @@ -42,10 +42,14 @@ def get_cosmos_client() -> CosmosClient: - """Get or create Cosmos DB client using Azure CLI credentials.""" + """Get or create Cosmos DB client using managed identity or default credentials.""" global _cosmos_client if _cosmos_client is None: - credential = AzureCliCredential() + azure_client_id = os.getenv("AZURE_CLIENT_ID") + if azure_client_id: + credential = ManagedIdentityCredential(client_id=azure_client_id) + else: + credential = DefaultAzureCredential() _cosmos_client = CosmosClient(COSMOSDB_ENDPOINT, credential=credential) return _cosmos_client From d9ef29c8f5281c755f3882042aa352c7010fd828 Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 11:57:32 -0800 Subject: [PATCH 24/37] Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. --- .github/workflows/orchestrate.yml | 19 ++++++++++++++++--- .github/workflows/promote-to-main.yml | 2 +- 2 files changed, 17 insertions(+), 4 deletions(-) diff --git a/.github/workflows/orchestrate.yml b/.github/workflows/orchestrate.yml index 62b25093f..c995ec504 100644 --- a/.github/workflows/orchestrate.yml +++ b/.github/workflows/orchestrate.yml @@ -336,7 +336,20 @@ jobs: fi # ──────────────────────────────────────────────────────────────────── - # NOTE: Auto-destroy is disabled. All environments (integration-* and - # production) persist their infrastructure. To tear down an environment - # manually, use: workflow_dispatch → destroy.yml with the target env. + # Step 8: Auto-destroy dev environment after successful merge + # Integration environments from *-dev branches are ephemeral. + # After the code is merged to int-agentic, tear down the Azure + # resources to save costs. Production environments are retained. # ──────────────────────────────────────────────────────────────────── + auto-destroy: + needs: [pipeline-config, auto-merge] + if: >- + always() + && needs.auto-merge.result == 'success' + && needs.pipeline-config.outputs.full_deploy == 'true' + && github.event_name == 'push' + && endsWith(github.ref_name, '-dev') + uses: ./.github/workflows/destroy.yml + with: + environment: ${{ needs.pipeline-config.outputs.environment }} + secrets: inherit diff --git a/.github/workflows/promote-to-main.yml b/.github/workflows/promote-to-main.yml index ea115f1a2..40017c3e3 100644 --- a/.github/workflows/promote-to-main.yml +++ b/.github/workflows/promote-to-main.yml @@ -32,7 +32,7 @@ jobs: - name: Create or update PR to main env: - GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GH_TOKEN: ${{ secrets.GH_PAT || secrets.GITHUB_TOKEN }} run: | # Check if there's already an open PR from int-agentic → main EXISTING_PR=$(gh pr list \ From c7c92e6fd171188139066be9aec59079860ad1c3 Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 11:57:32 -0800 Subject: [PATCH 25/37] Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. --- .github/workflows/orchestrate.yml | 19 ++++++++++++++++--- .github/workflows/promote-to-main.yml | 2 +- 2 files changed, 17 insertions(+), 4 deletions(-) diff --git a/.github/workflows/orchestrate.yml b/.github/workflows/orchestrate.yml index 62b25093f..c995ec504 100644 --- a/.github/workflows/orchestrate.yml +++ b/.github/workflows/orchestrate.yml @@ -336,7 +336,20 @@ jobs: fi # ──────────────────────────────────────────────────────────────────── - # NOTE: Auto-destroy is disabled. All environments (integration-* and - # production) persist their infrastructure. To tear down an environment - # manually, use: workflow_dispatch → destroy.yml with the target env. + # Step 8: Auto-destroy dev environment after successful merge + # Integration environments from *-dev branches are ephemeral. + # After the code is merged to int-agentic, tear down the Azure + # resources to save costs. Production environments are retained. # ──────────────────────────────────────────────────────────────────── + auto-destroy: + needs: [pipeline-config, auto-merge] + if: >- + always() + && needs.auto-merge.result == 'success' + && needs.pipeline-config.outputs.full_deploy == 'true' + && github.event_name == 'push' + && endsWith(github.ref_name, '-dev') + uses: ./.github/workflows/destroy.yml + with: + environment: ${{ needs.pipeline-config.outputs.environment }} + secrets: inherit diff --git a/.github/workflows/promote-to-main.yml b/.github/workflows/promote-to-main.yml index ea115f1a2..40017c3e3 100644 --- a/.github/workflows/promote-to-main.yml +++ b/.github/workflows/promote-to-main.yml @@ -32,7 +32,7 @@ jobs: - name: Create or update PR to main env: - GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GH_TOKEN: ${{ secrets.GH_PAT || secrets.GITHUB_TOKEN }} run: | # Check if there's already an open PR from int-agentic → main EXISTING_PR=$(gh pr list \ From 50efa2193a370315c63454ebe920513fdd7aa012 Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 13:51:37 -0800 Subject: [PATCH 26/37] Trigger rebuild: deploy Cosmos DB auth fix and agent list cleanup From cc79a16e3ceb1b5ade06f25f1bd073f841407504 Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 14:34:23 -0800 Subject: [PATCH 27/37] Fix: iterate ResponseStream directly, not .updates ResponseStream is AsyncIterable - iterate with 'async for chunk in stream:' The .updates property returns a Sequence (list), not an async iterator. This caused TypeError: 'async for' requires __aiter__, got list. Not caught in local tests because unit tests mocked the streaming and the regression tests verified API signatures but didn't run live agents. --- .github/copilot-instructions.md | 50 +++++++++++++++++++ .../multi_agent/handoff_multi_domain_agent.py | 4 +- .../multi_agent/magentic_group.py | 2 +- .../multi_agent/reflection_agent.py | 4 +- .../agents/agent_framework/single_agent.py | 4 +- 5 files changed, 57 insertions(+), 7 deletions(-) create mode 100644 .github/copilot-instructions.md diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 000000000..956f2ae21 --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,50 @@ +# Workflow Orchestration + +## 1. Plan Node Default +- Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions) +- If something goes sideways, STOP and re-plan immediately – don’t keep pushing +- Use plan mode for verification steps, not just building +- Write detailed specs upfront to reduce ambiguity + +## 2. Subagent Strategy +- Use subagents liberally to keep main context window clean +- Offload research, exploration, and parallel analysis to subagents +- For complex problems, throw more compute at it via subagents +- One task per subagent for focused execution + +### 3. Self-Improvement Loop +- After ANY correction from the user: update `tasks/lessons.md` with the pattern +- Write rules for yourself that prevent the same mistake +- Ruthlessly iterate on these lessons until mistake rate drops +- Review lessons at session start for relevant project + +### 4. Verification Before Done +- Never mark a task complete without proving it works +- Diff behavior between main and your changes when relevant +- Ask yourself: “Would a staff engineer approve this?” +- Run tests, check logs, demonstrate correctness + +### 5. Demand Elegance (Balanced) +- For non-trivial changes: pause and ask “is there a more elegant way?” +- If a fix feels hacky: “Knowing everything I know now, implement the elegant solution” +- Skip this for simple, obvious fixes – don’t over-engineer +- Challenge your own work before presenting it + +### 6. Autonomous Bug Fixing +- When given a bug report: just fix it. Don’t ask for hand-holding +- Point at logs, errors, failing tests – then resolve them +- Zero context switching required from the user +- Go fix failing CI tests without being told how + +## Task Management +1. **Plan First**: Write plan to `tasks/todo.md` with checkable items +2. **Verify Plan**: Check in before starting implementation +3. **Track Progress**: Mark items complete as you go +4. **Explain Changes**: High-level summary at each step +5. **Document Results**: Add review section to `tasks/todo.md` +6. **Capture Lessons**: Update `tasks/lessons.md` after corrections + +## Core Principles +- **Simplicity First**: Make every change as simple as possible. Impact minimal code. +- **No Laziness**: Find root causes. No temporary fixes. Senior developer standards. +- **Minimal Impact**: Changes should only touch what’s necessary. Avoid introducing bugs. \ No newline at end of file diff --git a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py index 448f0a783..db37542d0 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py @@ -592,7 +592,7 @@ async def chat_async(self, prompt: str) -> str: try: response_stream = agent.run(actual_prompt, stream=True, session=session) - async for chunk in response_stream.updates: + async for chunk in response_stream: # Process contents in the chunk if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -702,7 +702,7 @@ async def chat_async(self, prompt: str) -> str: full_response_handoff = [] try: response_stream_handoff = new_agent.run(actual_prompt_handoff, stream=True, session=new_session) - async for chunk in response_stream_handoff.updates: + async for chunk in response_stream_handoff: if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: if content.type == "function_call": diff --git a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py index 0b3c9737b..cefeb3ca5 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py +++ b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py @@ -647,7 +647,7 @@ async def _run_workflow( pending_request: WorkflowEvent | None = None - async for event in response_stream.updates: + async for event in response_stream: # Stream events to WebSocket if available await self._process_workflow_event(event) diff --git a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py index 161f4d55c..363b2c64b 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py @@ -176,7 +176,7 @@ async def _run_agent_non_streaming( chunks: List[str] = [] response_stream = agent.run(prompt, stream=True, session=self._session) - async for chunk in response_stream.updates: + async for chunk in response_stream: # Track tool calls for evaluation if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -218,7 +218,7 @@ async def _run_agent_streaming( chunks: List[str] = [] response_stream = agent.run(prompt, stream=True, session=self._session) - async for chunk in response_stream.updates: + async for chunk in response_stream: # Handle tool calls with argument tracking if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: diff --git a/agentic_ai/agents/agent_framework/single_agent.py b/agentic_ai/agents/agent_framework/single_agent.py index 338995561..461a6b191 100644 --- a/agentic_ai/agents/agent_framework/single_agent.py +++ b/agentic_ai/agents/agent_framework/single_agent.py @@ -171,7 +171,7 @@ async def chat_async(self, prompt: str) -> str: # Non-streaming path - use run with stream=True to capture tool calls full_response = [] response_stream = self._agent.run(prompt, stream=True, session=self._session) - async for chunk in response_stream.updates: + async for chunk in response_stream: # Extract tool calls from contents if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: @@ -233,7 +233,7 @@ async def _chat_async_streaming(self, prompt: str) -> str: try: response_stream = self._agent.run(prompt, stream=True, session=self._session) - async for chunk in response_stream.updates: + async for chunk in response_stream: # Process contents in the chunk if hasattr(chunk, 'contents') and chunk.contents: for content in chunk.contents: From bd24124a6a9a578cc636cdd984f4988ad3428a4b Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 14:44:33 -0800 Subject: [PATCH 28/37] Fix: make integration test failures block the pipeline Removed continue-on-error: true from the pytest step so test failures actually fail the workflow. Previously, 5/7 tests could fail and the pipeline would still report success and proceed to auto-merge. --- .github/workflows/integration-tests.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 6c6544a9a..d3332a657 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -64,7 +64,6 @@ jobs: run: | cd tests pytest -v -m "integration" --tb=short - continue-on-error: true # Report results but don't fail the workflow env: BACKEND_API_ENDPOINT: ${{ inputs.backend_endpoint }} MCP_ENDPOINT: ${{ inputs.mcp_endpoint }} From e6e5e7ebe80c654c187684f37f4f6bfc0fe17535 Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 14:45:40 -0800 Subject: [PATCH 29/37] Fix: use SHA-tagged images for container deployment, not :latest Container Apps don't create a new revision when the image tag is unchanged. Using :latest meant the old container kept running even after a new image was pushed. Now passes github.sha as the image tag, which forces a new revision on every deploy. This was the root cause of the ResponseStream.updates bug reaching production - the integration tests ran against old containers. --- .github/workflows/orchestrate.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/orchestrate.yml b/.github/workflows/orchestrate.yml index c995ec504..61200e391 100644 --- a/.github/workflows/orchestrate.yml +++ b/.github/workflows/orchestrate.yml @@ -180,6 +180,8 @@ jobs: uses: ./.github/workflows/update-containers.yml with: environment: ${{ needs.pipeline-config.outputs.environment }} + mcp_image_tag: ${{ github.sha }} + backend_image_tag: ${{ github.sha }} secrets: inherit # ──────────────────────────────────────────────────────────────────── From d8d49efd5753dcea1d0c1408023c15d3f0bf314f Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 15:20:05 -0800 Subject: [PATCH 30/37] Skip MCP localhost check in CI mode In CI, the evaluation talks to the backend via HTTP which connects to the deployed MCP container internally. The localhost:8000 check is only useful for local dev and was producing a confusing warning in CI logs. --- agentic_ai/evaluations/run_agent_eval.py | 26 +++++++++++------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/agentic_ai/evaluations/run_agent_eval.py b/agentic_ai/evaluations/run_agent_eval.py index a1974e7f8..4b79cf0eb 100644 --- a/agentic_ai/evaluations/run_agent_eval.py +++ b/agentic_ai/evaluations/run_agent_eval.py @@ -651,20 +651,18 @@ async def main(): print(f" Make sure backend is running on {backend_url}") return - # 3. Check MCP server - mcp_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") - print(f"\n🔌 MCP Server: {mcp_uri}") - - try: - import requests - health_check = requests.get(mcp_uri.replace("/mcp", "/health"), timeout=2) - print(f"✓ MCP server is responding") - except: - print(f"⚠ WARNING: Could not connect to MCP server") - print(f" Make sure it's running: cd mcp && uv run python mcp_service.py") - if args.ci: - print(f" CI mode: continuing without MCP server") - else: + # 3. Check MCP server (skip in CI — backend connects to deployed MCP internally) + if not args.ci: + mcp_uri = os.getenv("MCP_SERVER_URI", "http://localhost:8000/mcp") + print(f"\n🔌 MCP Server: {mcp_uri}") + + try: + import requests + health_check = requests.get(mcp_uri.replace("/mcp", "/health"), timeout=2) + print(f"✓ MCP server is responding") + except: + print(f"⚠ WARNING: Could not connect to MCP server") + print(f" Make sure it's running: cd mcp && uv run python mcp_service.py") response = input("\nContinue anyway? (y/n): ") if response.lower() != 'y': return From 6b0abc434fab999fb3c8f8eb447205a4e6e3590a Mon Sep 17 00:00:00 2001 From: "James N." Date: Mon, 23 Feb 2026 15:37:00 -0800 Subject: [PATCH 31/37] Fix: auto-merge creates PR if none exists Previously auto-merge only looked for an existing open PR and skipped if none was found. Now it auto-creates a PR from the dev branch to int-agentic if one doesn't exist, then merges it. This ensures every successful pipeline run promotes code to int-agentic regardless of whether a PR was manually created beforehand. --- .github/workflows/orchestrate.yml | 31 +++++++++++++++++++------------ 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/.github/workflows/orchestrate.yml b/.github/workflows/orchestrate.yml index 61200e391..dd31093be 100644 --- a/.github/workflows/orchestrate.yml +++ b/.github/workflows/orchestrate.yml @@ -322,21 +322,28 @@ jobs: --json number \ --jq '.[0].number // empty') - if [ -n "$PR_NUMBER" ]; then - echo "✅ Found PR #${PR_NUMBER}" - echo "🔀 Merging ${BRANCH} → int-agentic..." - gh pr merge "$PR_NUMBER" \ + if [ -z "$PR_NUMBER" ]; then + echo "📝 No open PR found — creating one automatically" + PR_URL=$(gh pr create \ --repo "${{ github.repository }}" \ - --squash \ - --auto \ - --subject "chore: merge ${BRANCH} into int-agentic (auto)" \ - --body "Auto-merged after successful CI/CD pipeline run ${{ github.run_id }}" - echo "✅ PR #${PR_NUMBER} merge initiated" - else - echo "ℹ️ No open PR found from ${BRANCH} → int-agentic" - echo " Create one with: gh pr create --base int-agentic --head ${BRANCH}" + --base int-agentic \ + --head "$BRANCH" \ + --title "chore: merge ${BRANCH} into int-agentic" \ + --body "Auto-created by CI/CD pipeline run ${{ github.run_id }}") + echo "✅ Created PR: ${PR_URL}" + PR_NUMBER=$(echo "$PR_URL" | grep -oP '\d+$') fi + echo "✅ Found PR #${PR_NUMBER}" + echo "🔀 Merging ${BRANCH} → int-agentic..." + gh pr merge "$PR_NUMBER" \ + --repo "${{ github.repository }}" \ + --squash \ + --auto \ + --subject "chore: merge ${BRANCH} into int-agentic (auto)" \ + --body "Auto-merged after successful CI/CD pipeline run ${{ github.run_id }}" + echo "✅ PR #${PR_NUMBER} merge initiated" + # ──────────────────────────────────────────────────────────────────── # Step 8: Auto-destroy dev environment after successful merge # Integration environments from *-dev branches are ephemeral. From add88339518a396b7e4ecece95bb5a7a29ec17de Mon Sep 17 00:00:00 2001 From: James Nguyen Date: Mon, 23 Feb 2026 16:22:37 -0800 Subject: [PATCH 32/37] chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22330447642 --- .github/workflows/orchestrate.yml | 200 +++++++++++++++++------------- 1 file changed, 113 insertions(+), 87 deletions(-) diff --git a/.github/workflows/orchestrate.yml b/.github/workflows/orchestrate.yml index dd31093be..1a1f89b97 100644 --- a/.github/workflows/orchestrate.yml +++ b/.github/workflows/orchestrate.yml @@ -2,16 +2,22 @@ name: CI/CD Pipeline # ───────────────────────────────────────────────────────────────────── # Pipeline modes: -# PR → main ➜ tests-only (validate against existing env) -# Push → main ➜ full deploy (deploy to production) -# Push → *-dev ➜ full deploy (deploy to integration-) -# Manual dispatch ➜ full deploy (chosen environment) +# Push → *-dev ➜ create PR to int-agentic (lightweight) +# PR → int-agentic ➜ full deploy + test MERGE COMMIT → auto-merge +# PR → main ➜ tests-only (validate against existing env) +# Push → main ➜ full deploy to production +# Manual dispatch ➜ full deploy (chosen environment) # # Promotion flow: -# *-dev push → full pipeline → auto-merge PR to int-agentic +# *-dev push → creates PR to int-agentic +# PR to int-agentic → deploys MERGE COMMIT → tests → auto-merge if pass # int-agentic push → promote-to-main.yml → creates/updates PR to main # main PR → human review + tests-only → merge → full deploy to production # +# Key design: PRs to int-agentic build from the GitHub merge commit, +# which combines the dev branch with int-agentic. This ensures we test +# the MERGED result, not the dev branch in isolation. +# # Per-developer environments: # Each developer pushes to their own -dev branch. # The pipeline maps -dev → integration- environment, @@ -28,6 +34,7 @@ on: pull_request: branches: + - int-agentic - main paths-ignore: - '**/*.md' @@ -60,20 +67,33 @@ jobs: outputs: environment: ${{ steps.config.outputs.environment }} full_deploy: ${{ steps.config.outputs.full_deploy }} + create_pr_only: ${{ steps.config.outputs.create_pr_only }} steps: - name: Determine pipeline mode id: config run: | EVENT="${{ github.event_name }}" + CREATE_PR_ONLY="false" # ── Resolve target environment ── if [ "$EVENT" = "workflow_dispatch" ]; then ENV="${{ inputs.target_env }}" elif [ "$EVENT" = "pull_request" ]; then - # PRs: resolve from the target (base) branch case "${{ github.base_ref }}" in - main) ENV="production" ;; - *) ENV="integration" ;; + main) + ENV="production" ;; + int-agentic) + # PR to int-agentic: extract dev name from head branch + HEAD="${{ github.head_ref }}" + case "$HEAD" in + *-dev) + DEV_NAME="${HEAD%-dev}" + ENV="integration-${DEV_NAME}" ;; + *) + ENV="integration" ;; + esac ;; + *) + ENV="integration" ;; esac elif [ "$EVENT" = "push" ]; then BRANCH="${{ github.ref_name }}" @@ -81,9 +101,10 @@ jobs: main) ENV="production" ;; *-dev) - # Extract developer name: james-dev → integration-james + # Dev push: only create PR to int-agentic, no deploy DEV_NAME="${BRANCH%-dev}" - ENV="integration-${DEV_NAME}" ;; + ENV="integration-${DEV_NAME}" + CREATE_PR_ONLY="true" ;; *) ENV="integration" ;; esac @@ -92,22 +113,75 @@ jobs: fi # ── Resolve pipeline mode ── - # PRs only run integration tests against the existing environment. - # Pushes and manual dispatches do a full deploy + build + test. - if [ "$EVENT" = "pull_request" ]; then + if [ "$CREATE_PR_ONLY" = "true" ]; then + FULL_DEPLOY="false" + elif [ "$EVENT" = "pull_request" ] && [ "${{ github.base_ref }}" = "main" ]; then + # PRs to main: tests-only against existing environment FULL_DEPLOY="false" + elif [ "$EVENT" = "pull_request" ] && [ "${{ github.base_ref }}" = "int-agentic" ]; then + # PRs to int-agentic: FULL deploy from merge commit + FULL_DEPLOY="true" + elif [ "$EVENT" = "push" ]; then + FULL_DEPLOY="true" else FULL_DEPLOY="true" fi echo "environment=$ENV" >> $GITHUB_OUTPUT echo "full_deploy=$FULL_DEPLOY" >> $GITHUB_OUTPUT + echo "create_pr_only=$CREATE_PR_ONLY" >> $GITHUB_OUTPUT echo "──────────────────────────────────────" - echo "Event: $EVENT" - echo "Environment: $ENV" - echo "Full deploy: $FULL_DEPLOY" + echo "Event: $EVENT" + echo "Environment: $ENV" + echo "Full deploy: $FULL_DEPLOY" + echo "Create PR only: $CREATE_PR_ONLY" echo "──────────────────────────────────────" + # ──────────────────────────────────────────────────────────────────── + # Step 0.5: Create PR to int-agentic (*-dev push only) + # When a developer pushes to their *-dev branch, we just create + # (or update) a PR to int-agentic. The PR triggers the full pipeline + # which builds from the MERGE COMMIT, testing combined code. + # ──────────────────────────────────────────────────────────────────── + create-pr: + needs: pipeline-config + if: needs.pipeline-config.outputs.create_pr_only == 'true' + runs-on: ubuntu-latest + steps: + - name: Create or update PR to int-agentic + env: + GH_TOKEN: ${{ secrets.GH_PAT || secrets.GITHUB_TOKEN }} + run: | + BRANCH="${{ github.ref_name }}" + echo "🔍 Checking for existing PR: ${BRANCH} → int-agentic" + + EXISTING_PR=$(gh pr list \ + --repo "${{ github.repository }}" \ + --base int-agentic \ + --head "$BRANCH" \ + --state open \ + --json number \ + --jq '.[0].number // empty') + + if [ -n "$EXISTING_PR" ]; then + echo "✅ PR #${EXISTING_PR} already exists — push updated it" + echo " The PR-triggered pipeline will run with the updated merge commit" + else + echo "📝 Creating PR: ${BRANCH} → int-agentic" + gh pr create \ + --repo "${{ github.repository }}" \ + --base int-agentic \ + --head "$BRANCH" \ + --title "Merge ${BRANCH} into int-agentic" \ + --body "Auto-created by CI/CD pipeline. + + This PR will be automatically merged after the pipeline validates + the **merge commit** (${BRANCH} + int-agentic combined). + + Pipeline run: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}" + echo "✅ PR created — full pipeline will trigger on the new PR" + fi + # ──────────────────────────────────────────────────────────────────── # Step 1: Preflight – unlock Terraform state storage (full deploy only) # ──────────────────────────────────────────────────────────────────── @@ -125,18 +199,16 @@ jobs: subscription-id: ${{ vars.AZURE_SUBSCRIPTION_ID }} - run: | - echo "Full orchestrated run through. Should add unit testing and validation here later." - echo "MCAPS sub disables storage account networking, run a command to ensure the account is reachable." + echo "Preflight: ensuring Terraform state storage is accessible" if az group show --name "$TFSTATE_RG" > /dev/null 2>&1; then az storage account update --resource-group "$TFSTATE_RG" --name "$TFSTATE_ACCOUNT" --default-action Allow az storage account update --resource-group "$TFSTATE_RG" --name "$TFSTATE_ACCOUNT" --public-network-access Enabled else - echo "::warning::Terraform state resource group '${TFSTATE_RG}' not found - skipping storage account network unlock" + echo "::warning::Terraform state resource group '${TFSTATE_RG}' not found - skipping" fi env: TFSTATE_RG: ${{ vars.TFSTATE_RG }} TFSTATE_ACCOUNT: ${{ vars.TFSTATE_ACCOUNT }} - # ──────────────────────────────────────────────────────────────────── # Step 2: Deploy infrastructure (full deploy only) @@ -151,6 +223,8 @@ jobs: # ──────────────────────────────────────────────────────────────────── # Step 3: Build containers (full deploy only) + # For PRs to int-agentic, actions/checkout gets the MERGE COMMIT + # so Docker images contain dev changes + int-agentic combined. # ──────────────────────────────────────────────────────────────────── build-application-container: needs: [pipeline-config, deploy-infrastructure] @@ -170,6 +244,7 @@ jobs: # ──────────────────────────────────────────────────────────────────── # Step 4: Update Container Apps with new images (full deploy only) + # Uses SHA-tagged images to force new Container App revisions. # ──────────────────────────────────────────────────────────────────── update-containers: needs: [pipeline-config, build-application-container, build-mcp-container] @@ -185,12 +260,13 @@ jobs: secrets: inherit # ──────────────────────────────────────────────────────────────────── - # Resolve endpoints from existing environment (PR / tests-only path) - # Looks up Container App FQDNs via az cli instead of deploying infra. + # Resolve endpoints from existing environment (PR → main tests-only) # ──────────────────────────────────────────────────────────────────── resolve-endpoints: needs: pipeline-config - if: needs.pipeline-config.outputs.full_deploy == 'false' + if: >- + needs.pipeline-config.outputs.full_deploy == 'false' + && needs.pipeline-config.outputs.create_pr_only == 'false' runs-on: ubuntu-latest environment: ${{ needs.pipeline-config.outputs.environment }} outputs: @@ -213,8 +289,6 @@ jobs: ITERATION="${{ vars.ITERATION || '002' }}" RG="rg-${PROJECT}-${ENV}-${ITERATION}" - echo "Looking up endpoints in resource group: $RG" - BE_FQDN=$(az containerapp show \ --name "ca-be-${ITERATION}" \ --resource-group "$RG" \ @@ -228,26 +302,15 @@ jobs: if [ -n "$BE_FQDN" ]; then echo "backend_endpoint=https://${BE_FQDN}" >> $GITHUB_OUTPUT echo "deployed=true" >> $GITHUB_OUTPUT - echo "✅ Backend: https://${BE_FQDN}" else - echo "::warning::Backend Container App not found in $RG – environment not yet deployed. Skipping PR tests." + echo "::warning::Backend not found in $RG" echo "backend_endpoint=" >> $GITHUB_OUTPUT echo "deployed=false" >> $GITHUB_OUTPUT - exit 0 - fi - - if [ -n "$MCP_FQDN" ]; then - echo "mcp_endpoint=https://${MCP_FQDN}" >> $GITHUB_OUTPUT - echo "✅ MCP: https://${MCP_FQDN}" - else - echo "::warning::MCP Container App not found in $RG – MCP tests will be skipped" - echo "mcp_endpoint=" >> $GITHUB_OUTPUT fi + echo "mcp_endpoint=${MCP_FQDN:+https://$MCP_FQDN}" >> $GITHUB_OUTPUT # ──────────────────────────────────────────────────────────────────── # Step 5: Run integration tests - # Full deploy path → endpoints come from Terraform outputs - # Tests-only path → endpoints come from resolve-endpoints # ──────────────────────────────────────────────────────────────────── integration-tests: needs: [pipeline-config, deploy-infrastructure, update-containers, resolve-endpoints] @@ -266,8 +329,6 @@ jobs: # ──────────────────────────────────────────────────────────────────── # Step 6: Agent quality evaluation (full deploy only) - # Runs a subset of agent eval test cases against the deployed backend - # and logs results to an independent AI Foundry project for trending. # ──────────────────────────────────────────────────────────────────── agent-evaluation: needs: [pipeline-config, deploy-infrastructure, update-containers, integration-tests] @@ -283,18 +344,9 @@ jobs: secrets: inherit # ──────────────────────────────────────────────────────────────────── - # Step 7: Auto-merge dev branch PR into int-agentic - # After a successful full deploy from a *-dev branch, automatically - # merge the open PR from that branch into int-agentic. This triggers - # the promote-to-main workflow which creates/updates a PR to main. - # - # IMPORTANT: Uses GH_PAT (a Personal Access Token or GitHub App token) - # instead of GITHUB_TOKEN so the push to int-agentic triggers - # downstream workflows (promote-to-main.yml). Pushes made by - # GITHUB_TOKEN do NOT trigger other workflows — this is a GitHub - # Actions limitation to prevent infinite loops. - # If GH_PAT is not configured, falls back to GITHUB_TOKEN (merge - # still works, but promote-to-main won't auto-trigger). + # Step 7: Auto-merge PR into int-agentic + # After successful tests on the MERGE COMMIT, merge the PR. + # Uses GH_PAT so the push triggers promote-to-main.yml. # ──────────────────────────────────────────────────────────────────── auto-merge: needs: [pipeline-config, integration-tests, agent-evaluation] @@ -303,61 +355,35 @@ jobs: && needs.pipeline-config.outputs.full_deploy == 'true' && needs.integration-tests.result == 'success' && (needs.agent-evaluation.result == 'success' || needs.agent-evaluation.result == 'skipped') - && github.event_name == 'push' - && endsWith(github.ref_name, '-dev') + && github.event_name == 'pull_request' + && github.base_ref == 'int-agentic' runs-on: ubuntu-latest steps: - - name: Merge dev PR into int-agentic + - name: Auto-merge PR into int-agentic env: GH_TOKEN: ${{ secrets.GH_PAT || secrets.GITHUB_TOKEN }} run: | - BRANCH="${{ github.ref_name }}" - echo "🔍 Looking for open PR: ${BRANCH} → int-agentic" - - PR_NUMBER=$(gh pr list \ - --repo "${{ github.repository }}" \ - --base int-agentic \ - --head "$BRANCH" \ - --state open \ - --json number \ - --jq '.[0].number // empty') - - if [ -z "$PR_NUMBER" ]; then - echo "📝 No open PR found — creating one automatically" - PR_URL=$(gh pr create \ - --repo "${{ github.repository }}" \ - --base int-agentic \ - --head "$BRANCH" \ - --title "chore: merge ${BRANCH} into int-agentic" \ - --body "Auto-created by CI/CD pipeline run ${{ github.run_id }}") - echo "✅ Created PR: ${PR_URL}" - PR_NUMBER=$(echo "$PR_URL" | grep -oP '\d+$') - fi - - echo "✅ Found PR #${PR_NUMBER}" - echo "🔀 Merging ${BRANCH} → int-agentic..." + PR_NUMBER="${{ github.event.pull_request.number }}" + echo "✅ Tests passed on merge commit — auto-merging PR #${PR_NUMBER}" gh pr merge "$PR_NUMBER" \ --repo "${{ github.repository }}" \ --squash \ --auto \ - --subject "chore: merge ${BRANCH} into int-agentic (auto)" \ + --subject "chore: merge ${{ github.head_ref }} into int-agentic (auto)" \ --body "Auto-merged after successful CI/CD pipeline run ${{ github.run_id }}" echo "✅ PR #${PR_NUMBER} merge initiated" # ──────────────────────────────────────────────────────────────────── # Step 8: Auto-destroy dev environment after successful merge - # Integration environments from *-dev branches are ephemeral. - # After the code is merged to int-agentic, tear down the Azure - # resources to save costs. Production environments are retained. + # Dev environments are ephemeral — tear down after merge. # ──────────────────────────────────────────────────────────────────── auto-destroy: needs: [pipeline-config, auto-merge] if: >- always() && needs.auto-merge.result == 'success' - && needs.pipeline-config.outputs.full_deploy == 'true' - && github.event_name == 'push' - && endsWith(github.ref_name, '-dev') + && github.event_name == 'pull_request' + && github.base_ref == 'int-agentic' uses: ./.github/workflows/destroy.yml with: environment: ${{ needs.pipeline-config.outputs.environment }} From ee028093a2b44e8730fb1dc55a87a87d43dcdd4b Mon Sep 17 00:00:00 2001 From: James Nguyen Date: Mon, 23 Feb 2026 18:45:14 -0800 Subject: [PATCH 33/37] chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22334055353 --- mcp/mcp_service_agentic.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/mcp/mcp_service_agentic.py b/mcp/mcp_service_agentic.py index f027aff1d..4b6eacb3f 100644 --- a/mcp/mcp_service_agentic.py +++ b/mcp/mcp_service_agentic.py @@ -6,8 +6,7 @@ from dataclasses import dataclass, field from typing import Any, Optional -from fastmcp import FastMCP -from fastmcp.server import Context +from fastmcp import FastMCP, Context from fastmcp.server.middleware.error_handling import ErrorHandlingMiddleware from fastmcp.server.middleware.logging import LoggingMiddleware from fastmcp.server.middleware.timing import TimingMiddleware @@ -608,9 +607,6 @@ async def ask_account_expert(question: str, ctx: Context | None = None) -> dict: async def ask_product_expert(question: str, ctx: Context | None = None) -> dict: assert ctx is not None return await _run_domain_tool(ctx=ctx, domain=DOMAIN_PRODUCT, input=question) -from fastmcp.server import Context -import asyncio - # --- Entrypoint --- if __name__ == "__main__": From 8dc4983301fdd2e643827db04dad3ab6041ffa0f Mon Sep 17 00:00:00 2001 From: Copilot <198982749+Copilot@users.noreply.github.com> Date: Tue, 28 Apr 2026 10:16:24 -0700 Subject: [PATCH 34/37] Migrate workshop to agent-framework==1.2.1 (incl. native HandoffBuilder) (#419) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Promote: int-agentic → main (production) (#414) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * Updating Durable Agent Implementation (#404) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme --------- Co-authored-by: James N. * chore: reduce anomaly probability to 1% for controlled demo pace * Reduce anomaly probability to 1% for controlled demo pace (#406) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * chore: reduce anomaly probability to 1% for controlled demo pace --------- Co-authored-by: James N. * add mcp_agent_demo * Add hybrid MCP server (strict-schema + natural-language tools) and typed-contract workflow Scripts 6-8 for the MCP agent demo: - workflow_typed_contracts.py: 4-agent IT security incident response pipeline with Pydantic-enforced contracts at every boundary (SecurityAlert, ThreatAssessment, ImpactAnalysis, IncidentResponse) - mcp_server_hybrid.py: Single MCP endpoint exposing BOTH tool types: * Strict-schema: triage_alert, assess_threat, create_response (Pydantic) * Natural-language: ask_security_advisor, explain_for_customer (prose) * Shared session state across all tools - mcp_client_hybrid.py: 5-step incident flow using both tool types with explicit context passing between steps - README.md: Updated from 6 to 8 capabilities with Hybrid Mode architecture diagram and Quick Start sections 7-8 * Replace workflow_local_remote.py with simplified proxy agent (Script 3) - Delete workflow_local_remote.py (old Script 3 with extra LLM call) - Add workflow_proxy_agent.py: MCPProxyAgent (BaseAgent, no LLM) calls MCP tools directly via call_tool() zero local LLM overhead - Simplify MCPProxyAgent: extract common _call() method, module-level _last_text() helper, remove verbose static methods - Fix workflow_typed_contracts.py: add async with context managers - Update README: merge Script 9 into Script 3 position, update architecture diagram, Quick Start, How It Works sections * Clean up MCP demos: remove comparison commentary, drop typed-contracts - workflow_proxy_agent.py: remove 'traditional vs proxy' framing MCPProxyAgent is the standard approach for remote agent integration - Delete workflow_typed_contracts.py: not relevant to MCP integration - README: 7 scripts, renumber hybrid 6-7, remove typed-contract sections * Add LangGraph + MAF GroupChat cross-framework demo (Scripts 8-9) - mcp_server_langgraph.py: LangGraph ReAct agent exposed as MCP server on port 8003 with architecture tools (pattern eval, migration estimate, tech stack recommendation) - workflow_group_chat.py: MAF GroupChatBuilder orchestration with LLM planner routing between local BusinessStrategist (MAF) and remote TechnicalArchitect (LangGraph via MCP) - pyproject.toml: add langgraph, langchain-openai, langchain-core deps - README.md: update to 9 scripts, add cross-framework architecture diagram, Quick Start sections, and dependency table entries * MCP agent demo: rewrite README as MCP-vs-A2A thesis, delete proxy agent, finalize group chat with inline Planner * README: Mermaid diagrams, professional tone, remove LinkedIn teasing language * README: reframe around multi-framework interop problem, two design patterns (Agent-as-Tool + Agent Adapter), add conceptual architecture * README: fix Mermaid diagrams - use br tags instead of \n for line breaks * README: fix A2A comparison table - elicitation supported, structured schemas not * Upgrade agent-framework to 1.0.0rc1 and fastmcp to 3.0.2 Breaking changes migrated: - agent-framework 1.0.0rc1: ChatAgent->Agent, AgentThread->AgentSession, run_stream->run(stream=True), model->default_options(ChatOptions), WorkflowOutputEvent/AgentRunEvent/RequestInfoEvent->unified WorkflowEvent, MagenticBuilder now uses constructor kwargs, orchestrations moved to agent_framework_orchestrations package - fastmcp 3.0.2: removed mcp version pin (now pulled by fastmcp), no code changes needed (fully compatible) Files updated: - agentic_ai/agents/agent_framework/single_agent.py - agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py - agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py - agentic_ai/agents/agent_framework/multi_agent/magentic_group.py - agentic_ai/applications/pyproject.toml (agent-framework==1.0.0rc1) - agentic_ai/workflow/fraud_detection_durable/pyproject.toml (agent-framework==1.0.0rc1) - mcp/pyproject.toml (fastmcp==3.0.2) - tests/test_agent_framework_rc1_regression.py (51 regression tests, all passing) * Fix CI: regenerate requirements.txt with agent-framework-core==1.0.0rc1 The Docker build uses requirements.txt (not pyproject.toml/uv.lock), and it still had agent-framework-core==1.0.0b260130 pinned, which conflicts with agent-framework==1.0.0rc1 requiring agent-framework-core==1.0.0rc1. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22315210980 * Fix: use GH_PAT for auto-merge to trigger downstream workflows Pushes made by GITHUB_TOKEN don't trigger other workflows (GitHub Actions limitation to prevent infinite loops). This meant the auto-merge into int-agentic never triggered promote-to-main.yml. Now uses secrets.GH_PAT with fallback to GITHUB_TOKEN so: 1. If GH_PAT is configured: merge triggers promote-to-main.yml 2. If not: merge still works, but promotion PR must be created manually * Add workflow_dispatch trigger to promote-to-main Allows manual triggering when auto-trigger is missed (e.g. after GITHUB_TOKEN-based merges that don't fire downstream workflows). * Fix: Cosmos DB auth + remove phantom reflection_workflow_agent 1. MCP Cosmos DB: Replace AzureCliCredential (not available in containers) with ManagedIdentityCredential (when AZURE_CLIENT_ID is set) or DefaultAzureCredential as fallback. This fixes the 'Azure CLI not found on path' error in production Container Apps. 2. Backend: Remove reflection_workflow_agent from DEFAULT_AVAILABLE_AGENTS and descriptions - the module file doesn't exist, causing it to show as a broken option in the UI agent selector. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22321497108 * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Trigger rebuild: deploy Cosmos DB auth fix and agent list cleanup * Fix: iterate ResponseStream directly, not .updates ResponseStream is AsyncIterable - iterate with 'async for chunk in stream:' The .updates property returns a Sequence (list), not an async iterator. This caused TypeError: 'async for' requires __aiter__, got list. Not caught in local tests because unit tests mocked the streaming and the regression tests verified API signatures but didn't run live agents. * Fix: make integration test failures block the pipeline Removed continue-on-error: true from the pytest step so test failures actually fail the workflow. Previously, 5/7 tests could fail and the pipeline would still report success and proceed to auto-merge. * Fix: use SHA-tagged images for container deployment, not :latest Container Apps don't create a new revision when the image tag is unchanged. Using :latest meant the old container kept running even after a new image was pushed. Now passes github.sha as the image tag, which forces a new revision on every deploy. This was the root cause of the ResponseStream.updates bug reaching production - the integration tests ran against old containers. * Skip MCP localhost check in CI mode In CI, the evaluation talks to the backend via HTTP which connects to the deployed MCP container internally. The localhost:8000 check is only useful for local dev and was producing a confusing warning in CI logs. * Fix: auto-merge creates PR if none exists Previously auto-merge only looked for an existing open PR and skipped if none was found. Now it auto-creates a PR from the dev branch to int-agentic if one doesn't exist, then merges it. This ensures every successful pipeline run promotes code to int-agentic regardless of whether a PR was manually created beforehand. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22330447642 --------- Co-authored-by: James N. * Promote: int-agentic → main (production) (#416) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * Updating Durable Agent Implementation (#404) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme --------- Co-authored-by: James N. * chore: reduce anomaly probability to 1% for controlled demo pace * Reduce anomaly probability to 1% for controlled demo pace (#406) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * chore: reduce anomaly probability to 1% for controlled demo pace --------- Co-authored-by: James N. * add mcp_agent_demo * Add hybrid MCP server (strict-schema + natural-language tools) and typed-contract workflow Scripts 6-8 for the MCP agent demo: - workflow_typed_contracts.py: 4-agent IT security incident response pipeline with Pydantic-enforced contracts at every boundary (SecurityAlert, ThreatAssessment, ImpactAnalysis, IncidentResponse) - mcp_server_hybrid.py: Single MCP endpoint exposing BOTH tool types: * Strict-schema: triage_alert, assess_threat, create_response (Pydantic) * Natural-language: ask_security_advisor, explain_for_customer (prose) * Shared session state across all tools - mcp_client_hybrid.py: 5-step incident flow using both tool types with explicit context passing between steps - README.md: Updated from 6 to 8 capabilities with Hybrid Mode architecture diagram and Quick Start sections 7-8 * Replace workflow_local_remote.py with simplified proxy agent (Script 3) - Delete workflow_local_remote.py (old Script 3 with extra LLM call) - Add workflow_proxy_agent.py: MCPProxyAgent (BaseAgent, no LLM) calls MCP tools directly via call_tool() zero local LLM overhead - Simplify MCPProxyAgent: extract common _call() method, module-level _last_text() helper, remove verbose static methods - Fix workflow_typed_contracts.py: add async with context managers - Update README: merge Script 9 into Script 3 position, update architecture diagram, Quick Start, How It Works sections * Clean up MCP demos: remove comparison commentary, drop typed-contracts - workflow_proxy_agent.py: remove 'traditional vs proxy' framing MCPProxyAgent is the standard approach for remote agent integration - Delete workflow_typed_contracts.py: not relevant to MCP integration - README: 7 scripts, renumber hybrid 6-7, remove typed-contract sections * Add LangGraph + MAF GroupChat cross-framework demo (Scripts 8-9) - mcp_server_langgraph.py: LangGraph ReAct agent exposed as MCP server on port 8003 with architecture tools (pattern eval, migration estimate, tech stack recommendation) - workflow_group_chat.py: MAF GroupChatBuilder orchestration with LLM planner routing between local BusinessStrategist (MAF) and remote TechnicalArchitect (LangGraph via MCP) - pyproject.toml: add langgraph, langchain-openai, langchain-core deps - README.md: update to 9 scripts, add cross-framework architecture diagram, Quick Start sections, and dependency table entries * MCP agent demo: rewrite README as MCP-vs-A2A thesis, delete proxy agent, finalize group chat with inline Planner * README: Mermaid diagrams, professional tone, remove LinkedIn teasing language * README: reframe around multi-framework interop problem, two design patterns (Agent-as-Tool + Agent Adapter), add conceptual architecture * README: fix Mermaid diagrams - use br tags instead of \n for line breaks * README: fix A2A comparison table - elicitation supported, structured schemas not * Upgrade agent-framework to 1.0.0rc1 and fastmcp to 3.0.2 Breaking changes migrated: - agent-framework 1.0.0rc1: ChatAgent->Agent, AgentThread->AgentSession, run_stream->run(stream=True), model->default_options(ChatOptions), WorkflowOutputEvent/AgentRunEvent/RequestInfoEvent->unified WorkflowEvent, MagenticBuilder now uses constructor kwargs, orchestrations moved to agent_framework_orchestrations package - fastmcp 3.0.2: removed mcp version pin (now pulled by fastmcp), no code changes needed (fully compatible) Files updated: - agentic_ai/agents/agent_framework/single_agent.py - agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py - agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py - agentic_ai/agents/agent_framework/multi_agent/magentic_group.py - agentic_ai/applications/pyproject.toml (agent-framework==1.0.0rc1) - agentic_ai/workflow/fraud_detection_durable/pyproject.toml (agent-framework==1.0.0rc1) - mcp/pyproject.toml (fastmcp==3.0.2) - tests/test_agent_framework_rc1_regression.py (51 regression tests, all passing) * Fix CI: regenerate requirements.txt with agent-framework-core==1.0.0rc1 The Docker build uses requirements.txt (not pyproject.toml/uv.lock), and it still had agent-framework-core==1.0.0b260130 pinned, which conflicts with agent-framework==1.0.0rc1 requiring agent-framework-core==1.0.0rc1. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22315210980 * Fix: use GH_PAT for auto-merge to trigger downstream workflows Pushes made by GITHUB_TOKEN don't trigger other workflows (GitHub Actions limitation to prevent infinite loops). This meant the auto-merge into int-agentic never triggered promote-to-main.yml. Now uses secrets.GH_PAT with fallback to GITHUB_TOKEN so: 1. If GH_PAT is configured: merge triggers promote-to-main.yml 2. If not: merge still works, but promotion PR must be created manually * Add workflow_dispatch trigger to promote-to-main Allows manual triggering when auto-trigger is missed (e.g. after GITHUB_TOKEN-based merges that don't fire downstream workflows). * Fix: Cosmos DB auth + remove phantom reflection_workflow_agent 1. MCP Cosmos DB: Replace AzureCliCredential (not available in containers) with ManagedIdentityCredential (when AZURE_CLIENT_ID is set) or DefaultAzureCredential as fallback. This fixes the 'Azure CLI not found on path' error in production Container Apps. 2. Backend: Remove reflection_workflow_agent from DEFAULT_AVAILABLE_AGENTS and descriptions - the module file doesn't exist, causing it to show as a broken option in the UI agent selector. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22321497108 * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Trigger rebuild: deploy Cosmos DB auth fix and agent list cleanup * Fix: iterate ResponseStream directly, not .updates ResponseStream is AsyncIterable - iterate with 'async for chunk in stream:' The .updates property returns a Sequence (list), not an async iterator. This caused TypeError: 'async for' requires __aiter__, got list. Not caught in local tests because unit tests mocked the streaming and the regression tests verified API signatures but didn't run live agents. * Fix: make integration test failures block the pipeline Removed continue-on-error: true from the pytest step so test failures actually fail the workflow. Previously, 5/7 tests could fail and the pipeline would still report success and proceed to auto-merge. * Fix: use SHA-tagged images for container deployment, not :latest Container Apps don't create a new revision when the image tag is unchanged. Using :latest meant the old container kept running even after a new image was pushed. Now passes github.sha as the image tag, which forces a new revision on every deploy. This was the root cause of the ResponseStream.updates bug reaching production - the integration tests ran against old containers. * Skip MCP localhost check in CI mode In CI, the evaluation talks to the backend via HTTP which connects to the deployed MCP container internally. The localhost:8000 check is only useful for local dev and was producing a confusing warning in CI logs. * Fix: auto-merge creates PR if none exists Previously auto-merge only looked for an existing open PR and skipped if none was found. Now it auto-creates a PR from the dev branch to int-agentic if one doesn't exist, then merges it. This ensures every successful pipeline run promotes code to int-agentic regardless of whether a PR was manually created beforehand. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22330447642 * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22334055353 --------- Co-authored-by: James N. * Bump agent-framework to 1.2.1 and migrate AzureOpenAIChatClient → OpenAIChatClient Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/b1edc1aa-9c20-46c5-afdc-0256050a03de Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Rewrite handoff agent on native HandoffBuilder; update regression tests for 1.2.1 Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/b1edc1aa-9c20-46c5-afdc-0256050a03de Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Update HANDOFF_README and dependency table for native HandoffBuilder migration Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/b1edc1aa-9c20-46c5-afdc-0256050a03de Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Address GPT-5.5 review: fix CheckpointStorage protocol, reflection_agent kwargs, observability sample Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/b1edc1aa-9c20-46c5-afdc-0256050a03de Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Fix PEP 8 spacing in mcp_agent_demo files (code review nits) Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/b1edc1aa-9c20-46c5-afdc-0256050a03de Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Regenerate uv.lock files; pass workflow_name to 1.2.x checkpoint listing Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/91307ce7-2444-4b5f-b75f-2b736bc022c5 Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> --------- Co-authored-by: James Nguyen Co-authored-by: James N. Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> --- .../multi_agent/HANDOFF_README.md | 313 +- .../multi_agent/handoff_multi_domain_agent.py | 1008 ++--- .../multi_agent/magentic_group.py | 119 +- .../multi_agent/reflection_agent.py | 8 +- .../agents/agent_framework/single_agent.py | 14 +- agentic_ai/agents/mcp_agent_demo/README.md | 3 +- .../agents/mcp_agent_demo/mcp_client_agent.py | 8 +- .../mcp_agent_demo/mcp_client_hybrid.py | 8 +- .../mcp_agent_demo/mcp_client_stateful.py | 8 +- .../agents/mcp_agent_demo/mcp_server.py | 8 +- .../mcp_agent_demo/mcp_server_hybrid.py | 8 +- .../mcp_agent_demo/mcp_server_stateful.py | 8 +- .../agents/mcp_agent_demo/pyproject.toml | 3 +- agentic_ai/agents/mcp_agent_demo/uv.lock | 4015 ++++++++++++----- .../mcp_agent_demo/workflow_group_chat.py | 8 +- agentic_ai/applications/pyproject.toml | 2 +- agentic_ai/applications/requirements.txt | 61 +- agentic_ai/applications/uv.lock | 3638 +++++++-------- .../sample_agent_with_tracing.py | 20 +- .../fraud_analysis_workflow.py | 16 +- .../fraud_detection_durable/pyproject.toml | 4 +- .../workflow/fraud_detection_durable/uv.lock | 3070 ++++++------- .../fraud_detection_durable/worker.py | 6 +- ... test_agent_framework_1_2_1_regression.py} | 255 +- 24 files changed, 7294 insertions(+), 5317 deletions(-) rename tests/{test_agent_framework_rc1_regression.py => test_agent_framework_1_2_1_regression.py} (65%) diff --git a/agentic_ai/agents/agent_framework/multi_agent/HANDOFF_README.md b/agentic_ai/agents/agent_framework/multi_agent/HANDOFF_README.md index 79caa5509..c096c3775 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/HANDOFF_README.md +++ b/agentic_ai/agents/agent_framework/multi_agent/HANDOFF_README.md @@ -2,79 +2,100 @@ ## Overview -A scalable domain-based routing pattern for customer support that enables **direct specialist-to-user communication** with intelligent handoff detection. Specialists handle requests within their domain autonomously, and routing only occurs when a request falls outside their expertise. - -## Core Concept: Lazy Intent Classification - -**The key innovation:** Lightweight, lazy intent classification triggered only when a specialist signals a request is outside their domain. - -**The key advantage:** Specialist agents interact directly with users without third-party monitoring. Intent classification is triggered **only when needed**, making the system efficient and scalable. +A scalable domain-based routing pattern for customer support that uses the +**native `HandoffBuilder`** from `agent-framework-orchestrations` (1.2.x). +Each domain specialist talks to the user directly, and any specialist can +transfer the conversation to another specialist by calling an +auto-generated `handoff_to_` tool. The framework intercepts those +calls and routes control accordingly — no hand-rolled intent classifier +or regex-based handoff detection is needed. + +## Core Concept: Tool-Based Handoff + +The key change from earlier workshop versions is that handoffs are now +**modeled as tool calls** that the agents themselves decide to invoke. The +`HandoffBuilder` does three things automatically: + +1. **Synthesizes one handoff tool per allowed target** for every + participant agent (e.g., `handoff_to_product_promotions`). +2. **Injects middleware** that intercepts those tool calls — they never + actually execute, they just signal "route control to this target". +3. **Broadcasts the cleaned conversation** to the target agent so the new + specialist sees the full conversation when it takes over (no manual + context transfer required). ### How It Works -1. **Direct Communication** - Most of the time, the specialist agent communicates directly with the user -2. **Out-of-Domain Detection** - When a user asks something outside the agent's domain, the agent responds with a predefined phrase: *"This is outside my area. Let me connect you with the right specialist."* -3. **Pattern Matching** - The system detects this phrase using simple regex patterns (no LLM needed) -4. **Lazy Classification** - Only when detected, the intent classifier runs to determine the correct specialist -5. **Seamless Handoff** - The conversation transfers to the appropriate domain specialist with context +1. **Direct Communication** — The active specialist receives the user + message, runs its own tool calls (filtered MCP tools for its domain), + and replies. +2. **Self-Initiated Routing** — When a request is outside its domain, the + specialist calls the appropriate `handoff_to_` tool. This is + declared in its instructions ("you MUST hand off to the appropriate + specialist by calling the corresponding handoff tool"). +3. **Workflow Routing** — `HandoffBuilder`'s middleware intercepts the + tool call, emits a `handoff_sent` workflow event, and dispatches an + `AgentExecutorRequest` to the target specialist with the full + conversation history. +4. **Target Specialist Responds** — The new specialist sees the prior + conversation and answers the user's request. ### Why This Design? -- **Efficiency**: No intent classification overhead on every message -- **Scalability**: Specialists don't need to know about other agents' capabilities -- **Autonomy**: Each specialist operates independently within their domain -- **Cost-effective**: Minimal LLM calls compared to continuous monitoring - -### Classification Modes - -**Lazy Mode (Default)** - `HANDOFF_LAZY_CLASSIFICATION=true` -- Intent classifier runs only when: - - First message (initial routing) - - Handoff phrase detected in specialist response -- Agent communicates directly with user during normal conversation - -**Always-On Mode** - `HANDOFF_LAZY_CLASSIFICATION=false` -- Intent classifier runs on every user message -- Useful for complex routing scenarios or testing +- **First-class framework support** — handoffs are part of the + orchestration layer, not an application-level workaround. +- **No regex / classifier overhead** — the LLM decides when to hand off. +- **Conversation is shared automatically** — every specialist sees the + full cleaned conversation, no manual `_build_context_prefix` step. +- **Less code to maintain** — the agent file went from 782 lines (with a + hand-rolled `_classify_intent`, `_detect_handoff_request`, and + `_build_context_prefix`) down to ~510 lines. ## Architecture ```mermaid graph TD - A[User Message] --> B{First Message?} - B -->|Yes| C[Intent Classifier] - B -->|No| D{Lazy Mode?} - - D -->|Yes| E[Route to Current Specialist] - D -->|No| C - - C --> F[Assign Domain Specialist] - F --> E - - E --> G[Specialist Responds Directly] - - G --> H{Handoff Phrase Detected?} - H -->|No| I[User Receives Response] - H -->|Yes| J[Run Intent Classifier] - - J --> K[Switch to New Specialist] - K --> L[New Specialist Responds] - L --> I - + A[User Message] --> B{Resume from checkpoint?} + B -->|Yes| C[Workflow.run responses=...,checkpoint_id=...] + B -->|No| D[Workflow.run prompt] + + C --> E[Active Specialist] + D --> E + + E --> F{Tool call?} + F -->|Domain tool| G[Execute MCP tool] --> E + F -->|handoff_to_X| H[Middleware intercepts] + F -->|None / text| I[Stream tokens to user] + + H --> J[handoff_sent event] --> K[Target Specialist] + K --> F + + I --> L[request_info: HandoffAgentUserRequest] + L --> M[Save checkpoint] + M --> N[Return response to user] + style E fill:#90EE90 - style G fill:#90EE90 - style C fill:#FFD700 - style J fill:#FFD700 - style H fill:#87CEEB + style K fill:#90EE90 + style H fill:#FFD700 ``` ### Flow Description -1. **Initial Routing**: First message triggers intent classification to select starting specialist -2. **Direct Communication**: Specialist agent handles user messages directly using domain-specific tools -3. **Handoff Detection**: System monitors specialist response for out-of-domain phrase -4. **Lazy Classification**: Only when handoff phrase detected, intent classifier runs to find new domain -5. **Context Transfer**: Conversation history transfers to new specialist for continuity +1. **First Turn**: `chat_async` calls `workflow.run(prompt, stream=True)`. + The configured start agent (`HANDOFF_DEFAULT_DOMAIN`) handles the + message. +2. **Streaming**: `AgentResponseUpdate` events are forwarded to the + WebSocket as `agent_token`/`tool_called` messages. `handoff_sent` + events become `handoff_announcement` + a follow-up `agent_start`. +3. **Pause for User**: After the agent finishes (without handing off), + the workflow emits a `request_info` event carrying a + `HandoffAgentUserRequest`. We save the request ID alongside the + checkpoint. +4. **Subsequent Turns**: `chat_async` resumes via + `workflow.run(responses={request_id: HandoffAgentUserRequest.create_response(prompt)}, checkpoint_id=..., stream=True)`. + The current specialist sees the new user message; if the topic + shifts, it calls a `handoff_to_` tool and routing happens + automatically. ## Domain Specialists @@ -84,143 +105,115 @@ graph TD | **Product & Promotions** | Product catalog, promotions, eligibility, orders | `get_products`, `get_promotions`, `get_eligible_promotions`, `get_customer_orders` + 2 more | | **Security & Authentication** | Account security, lockouts, authentication, incidents | `get_security_logs`, `unlock_account`, `get_support_tickets`, `create_support_ticket` + 1 more | -**Key Instruction**: Each specialist is instructed to respond with *"This is outside my area. Let me connect you with the right specialist."* when requests fall outside their domain. - -## Handoff Detection & Classification - -### Pattern-Based Handoff Detection - -When a specialist responds with out-of-domain phrases, the system detects them using regex patterns: - -**Detection Strategies:** -1. **Exact Template**: `"outside my area.*connect you with.*specialist"` -2. **Domain Boundaries**: `"outside my (domain|expertise|area)"` -3. **Transfer Language**: `"let me (transfer|route|connect) you"` -4. **Keyword Proximity**: Multiple keyword groups within 100 characters - -**Example:** -``` -User: "Can you unlock my account?" -Billing Agent: "This is outside my area. Let me connect you with the right specialist." -System: ✓ Handoff detected via pattern match → Trigger classification -``` - -### Intent Classification (Structured Output) - -Uses Pydantic model with OpenAI's `beta.chat.completions.parse()` for reliable JSON: - -```python -class IntentClassification(BaseModel): - domain: str # Target domain - is_domain_change: bool # Whether domain changed - confidence: float # 0.0 - 1.0 - reasoning: str # Brief explanation -``` - -**When it runs:** -- First message (initial routing) -- When handoff phrase detected (lazy mode) -- Every message (always-on mode) - -**Error handling:** If classification fails, randomly selects a different domain to avoid getting stuck. +The agent **`description`** for each specialist is what `HandoffBuilder` +embeds into the auto-generated handoff tool description, so descriptions +should clearly state when control should transfer to that specialist. ## Key Implementation Details ### Tool Filtering -Each specialist has filtered access to MCP tools. The system: -1. Connects to MCP server once and loads all tools -2. Filters tool list per domain using `create_filtered_tool_list()` -3. Passes filtered tools to each specialist agent +Each specialist receives a filtered subset of MCP tools via +`create_filtered_tool_list()`. The MCP server is connected once and +shared across all specialists. -**Benefits:** Security, focus, reduced hallucination, efficient resource sharing +**Benefits:** Security, focus, reduced hallucination, efficient resource +sharing. -### Thread Isolation +### Cross-Request Continuity (Checkpointing) -Each domain specialist maintains its own conversation thread: -- Threads are serialized and persisted in state store -- When switching domains, the new specialist's thread is restored -- Allows specialists to maintain domain-specific context +The workshop runs each chat turn as a separate HTTP request, so the +workflow needs to persist its state between calls. We use +`HandoffBuilder.with_checkpointing(storage)` with a dictionary-backed +`CheckpointStorage` (`_DictCheckpointStorage`) keyed off the per-session +state store. -### Context Transfer +- After every turn the workflow saves a checkpoint and pauses on a + `request_info` event waiting for the next user message. +- The next call passes `checkpoint_id` and `responses={pending_id: ...}` + to resume the same conversation. -On handoff, conversation history can transfer to the new specialist: +The pending request ID and current speaking domain are also persisted in +the state store so a process restart can re-create the workflow with the +correct start agent. -**Configuration:** `HANDOFF_CONTEXT_TRANSFER_TURNS` -- `-1`: Transfer all history (default, best UX) -- `0`: No context transfer (domain isolation) -- `N`: Transfer last N turns only +### Conversation Sharing -**Example:** -``` -User: "My customer ID is 251. What's my bill?" -Billing: "Your bill is $150..." -User: "Am I eligible for promotions?" -[Context transfers → Product specialist sees customer ID 251] -Product: "For customer 251, you're eligible for..." -``` +`HandoffBuilder` automatically broadcasts each agent's cleaned response +(without internal tool-call/result content) to all other agents in the +group. There is no longer a need for the per-domain "context prefix" the +previous implementation manually built. -## Example: Multi-Turn Conversation +### Handoff Tool Filtering in the UI -**Lazy Mode (Default):** +When streaming `function_call` content from the workflow, the agent +filters out any tool whose name starts with `handoff_to_` — these are +synthetic framework signals, not real tool invocations, and are already +surfaced as the higher-level `handoff_announcement` WebSocket event. + +## Example: Multi-Turn Conversation ``` -Turn 1: Initial Routing +Turn 1 User: "What's my bill?" -→ First message → Intent classifier runs → Route to Billing -Billing: "Your bill is $45.99" - -Turn 2: Within Domain -User: "Can I see the details?" -→ Current domain = Billing → Skip classification → Direct to Billing -Billing: "Here are the line items: Basic plan $30, Data $15.99..." -→ No handoff marker → Done - -Turn 3: Out-of-Domain Request -User: "What about promotions?" -→ Current domain = Billing → Skip classification → Direct to Billing -Billing: "This is outside my area. Let me connect you with the right specialist." -→ Handoff marker detected! → Intent classifier runs → Route to Promotions -Promotions: "We have 3 active promotions available..." +→ Start agent (crm_billing) handles the message. +crm_billing: "Your bill is $45.99…" +→ Workflow pauses on request_info; checkpoint saved. + +Turn 2 (within domain) +User: "Can I see the line items?" +→ Resume from checkpoint with responses={pending_id: ...} +→ crm_billing answers directly. +crm_billing: "Basic plan $30, Data $15.99…" + +Turn 3 (out-of-domain) +User: "What promotions am I eligible for?" +→ Resume from checkpoint. +→ crm_billing's instructions tell it to call handoff_to_product_promotions. +→ HandoffBuilder middleware intercepts and emits handoff_sent. +→ product_promotions sees the full conversation and responds. +product_promotions: "You qualify for 3 active promotions…" ``` -**Performance:** Only 3 LLM calls total (Turn 1 classify, Turn 2 Billing, Turn 3 Billing → classify → Promotions) - -**Always-On Mode:** - -Every turn runs intent classification before routing (less efficient but useful for complex scenarios). - ## Configuration | Environment Variable | Default | Description | |---------------------|---------|-------------| -| `HANDOFF_LAZY_CLASSIFICATION` | `true` | Enable lazy mode (only classify on first message or handoff detection) | -| `HANDOFF_DEFAULT_DOMAIN` | `crm_billing` | Starting domain for first message (`crm_billing` \| `product_promotions` \| `security_authentication`) | -| `HANDOFF_CONTEXT_TRANSFER_TURNS` | `-1` | Context to transfer on handoff (`-1`=all, `0`=none, `N`=last N turns) | -| `AZURE_OPENAI_API_KEY` | (required) | Azure OpenAI API key | +| `HANDOFF_DEFAULT_DOMAIN` | `crm_billing` | Starting domain for the first turn (`crm_billing` \| `product_promotions` \| `security_authentication`) | +| `AZURE_OPENAI_API_KEY` | (required for key auth) | Azure OpenAI API key | | `AZURE_OPENAI_ENDPOINT` | (required) | Azure OpenAI endpoint URL | -| `AZURE_OPENAI_CHAT_DEPLOYMENT` | (required) | Deployment name (e.g., `gpt-4`) | +| `AZURE_OPENAI_CHAT_DEPLOYMENT` | (required) | Azure deployment name (passed to `OpenAIChatClient` as `model=`) | +| `AZURE_OPENAI_API_VERSION` | (required) | Azure OpenAI API version | | `MCP_SERVER_URI` | (optional) | MCP server endpoint for domain tools | -| `AGENT_MODULE` | - | `agents.agent_framework.multi_agent.handoff_multi_domain_agent` | +| `AGENT_MODULE` | – | `agents.agent_framework.multi_agent.handoff_multi_domain_agent` | + +> The legacy `HANDOFF_LAZY_CLASSIFICATION` and +> `HANDOFF_CONTEXT_TRANSFER_TURNS` settings are no longer used: native +> handoffs are always agent-driven and the workflow shares conversation +> state between specialists automatically. ## Advantages -✅ **Efficiency** - Minimal LLM calls, no orchestrator overhead -✅ **Scalability** - Add new specialists without coordination complexity -✅ **Autonomy** - Specialists don't need to know about other agents -✅ **Natural UX** - Direct agent-to-user communication -✅ **Cost-effective** - Classification only when needed -✅ **Maintainable** - Clean domain separation and tool filtering +✅ **Native framework support** — handoffs are part of the orchestration +layer, not an application workaround. +✅ **Less code to maintain** — no hand-rolled classifier, regex +detection, or context prefix builder. +✅ **Agent-decided routing** — the LLM decides when to hand off, with +clear handoff-tool descriptions to guide it. +✅ **Automatic shared conversation** — every specialist sees the cleaned +conversation when it takes over. +✅ **Resumable conversations** — workflow checkpointing persists state +across HTTP requests. ## When to Use **Good fit:** - Support scenarios with clear domain boundaries -- Conversations typically stay within one domain - Want to minimize latency and cost - Need to scale to many specialist agents **Not ideal:** -- Tasks requiring simultaneous multi-agent collaboration -- Complex workflows with dependencies between agents -- Need centralized orchestration and planning +- Tasks requiring simultaneous multi-agent collaboration → use + `MagenticBuilder` instead. +- Complex workflows with explicit dependencies between agents → use + `WorkflowBuilder` with custom edges. diff --git a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py index db37542d0..320d634de 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py @@ -1,29 +1,50 @@ -""" -Optimized handoff pattern for domain-based multi-agent routing. +"""Multi-domain handoff agent built on the native ``HandoffBuilder``. + +This module migrates the previous hand-rolled handoff implementation +(intent classification + regex-based handoff detection + manual context +transfer) to the native handoff orchestration introduced in +``agent-framework`` 1.2.x. Architecture: -1. User chats directly with the assigned specialist agent (no middleman) -2. Lightweight intent classifier checks for domain changes -3. Only re-routes when user switches topics or requests help -4. Full streaming visibility via WebSocket - -Key improvements over v2: -- No heavy Magentic orchestrator -- Direct agent-to-user communication -- Efficient intent detection using vanilla LLM calls -- Simple state management for current agent tracking + +1. Each domain specialist is a regular ``Agent`` configured with a + filtered MCP tool set. +2. ``HandoffBuilder`` constructs a workflow where every agent can hand off + to every other agent (mesh topology). The framework auto-injects + synthetic ``handoff_to_`` tools into each agent and intercepts + them via middleware to route control — no more manual handoff + detection. +3. Cross-request continuity is handled via the workflow's checkpointing + (``with_checkpointing``). The first turn starts a fresh run; subsequent + turns resume by responding to the pending ``request_info`` event with + the new user prompt. +4. Streaming agent updates and ``handoff_sent`` events are forwarded to + the WebSocket layer to preserve the existing UI behaviour + (``agent_start``, ``agent_token``, ``tool_called``, + ``handoff_announcement``, ``final_result``). """ -import json +from __future__ import annotations + +import asyncio import logging import os -import random -import re +from threading import Lock as ThreadLock from typing import Any, Dict, List, Optional -from pydantic import BaseModel, Field -from agent_framework import Agent as FrameworkAgent, AgentSession, ChatOptions, Message, Role, MCPStreamableHTTPTool -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework import ( + Agent as FrameworkAgent, + ChatOptions, + CheckpointStorage, + MCPStreamableHTTPTool, + WorkflowCheckpoint, +) +from agent_framework.openai import OpenAIChatClient +from agent_framework_orchestrations import ( + HandoffAgentUserRequest, + HandoffBuilder, + HandoffSentEvent, +) from agents.base_agent import BaseAgent, ToolCallTrackingMixin from agents.agent_framework.utils import create_filtered_tool_list @@ -31,30 +52,18 @@ logger = logging.getLogger(__name__) -# Pydantic model for structured intent classification output -class IntentClassification(BaseModel): - """Structured output for intent classification.""" - domain: str = Field( - description="Target domain: crm_billing, product_promotions, or security_authentication" - ) - is_domain_change: bool = Field( - description="Whether this represents a change from the current domain" - ) - confidence: float = Field( - ge=0.0, - le=1.0, - description="Confidence score between 0.0 and 1.0" - ) - reasoning: str = Field( - description="Brief explanation of the classification decision" - ) - - -# Domain definitions -DOMAINS = { +# Domain definitions for the handoff workflow. +# +# ``description`` is consumed by ``HandoffBuilder`` to populate the +# auto-generated handoff tool description, so it must clearly state when +# control should transfer to that specialist. +DOMAINS: Dict[str, Dict[str, Any]] = { "crm_billing": { "name": "CRM & Billing Specialist", - "description": "Handles subscriptions, billing, invoices, payments, and account adjustments", + "description": ( + "Handles customer accounts, subscriptions, billing, invoices, " + "payments, data usage and account adjustments." + ), "tools": [ "get_all_customers", "get_customer_detail", @@ -74,14 +83,18 @@ class IntentClassification(BaseModel): "**Critical rules:**\n" "- ALWAYS use your tools to retrieve factual data. NEVER guess or hallucinate.\n" "- If customer info is needed but not provided, ask the user directly for it.\n" - "- If the user asks about products, promotions, or security issues, you MUST respond with this EXACT phrase: " - "'This is outside my area. Let me connect you with the right specialist.'\n" + "- If the user asks about products, promotions, or security/authentication issues, " + "you MUST hand off to the appropriate specialist by calling the corresponding " + "handoff tool. Do not attempt to answer outside your domain.\n" "- Be concise and professional. Provide specific details from tool responses.\n" ), }, "product_promotions": { "name": "Product & Promotions Specialist", - "description": "Handles product inquiries, plan changes, promotions, and eligibility", + "description": ( + "Handles product catalog inquiries, plan changes, promotions, " + "eligibility checks and customer orders." + ), "tools": [ "get_products", "get_product_detail", @@ -98,14 +111,18 @@ class IntentClassification(BaseModel): "- Customer orders and product recommendations\n\n" "**Critical rules:**\n" "- ALWAYS use your tools to retrieve factual data. NEVER guess or hallucinate.\n" - "- If the user asks about billing or security issues, you MUST respond with this EXACT phrase: " - "'This is outside my area. Let me connect you with the right specialist.'\n" + "- If the user asks about billing or security/authentication issues, you MUST " + "hand off to the appropriate specialist by calling the corresponding handoff " + "tool. Do not attempt to answer outside your domain.\n" "- Be enthusiastic and helpful. Highlight benefits and savings opportunities.\n" ), }, "security_authentication": { "name": "Security & Authentication Specialist", - "description": "Handles authentication failures, lockouts, security incidents, and remediation", + "description": ( + "Handles authentication failures, account lockouts, security " + "incidents and remediation." + ), "tools": [ "get_security_logs", "unlock_account", @@ -121,157 +138,159 @@ class IntentClassification(BaseModel): "- Support ticket management for security issues\n\n" "**Critical rules:**\n" "- ALWAYS use your tools to retrieve factual data. NEVER guess or hallucinate.\n" - "- If the user asks about billing or products, you MUST respond with this EXACT phrase: " - "'This is outside my area. Let me connect you with the right specialist.'\n" + "- If the user asks about billing or products/promotions, you MUST hand off " + "to the appropriate specialist by calling the corresponding handoff tool. " + "Do not attempt to answer outside your domain.\n" "- Take security seriously. Verify user identity and flag suspicious activity.\n" ), }, } -# Intent classification prompt -INTENT_CLASSIFIER_PROMPT = """You are an intent classifier for Contoso customer support. - -Available domains: -1. crm_billing: subscriptions, billing, invoices, payments, account adjustments -2. product_promotions: products, plans, promotions, eligibility, orders -3. security_authentication: security issues, lockouts, authentication failures - -Analyze the user's message and determine: -1. Which domain it belongs to -2. Whether it's a domain change from the current context - -Current domain: {current_domain} -User message: {user_message} - -Respond with JSON: -{{ - "domain": "crm_billing|product_promotions|security_authentication", - "is_domain_change": true|false, - "confidence": 0.0-1.0, - "reasoning": "brief explanation" -}} - -Rules: -- If uncertain, default to current domain with low confidence -- Detect explicit requests to "talk to someone else" or "get help with X" as domain changes -- Consider context: billing questions stay in billing unless user explicitly changes topic -""" +class _DictCheckpointStorage(CheckpointStorage): + """Dictionary-backed ``CheckpointStorage`` shared via the session state store. -class Agent(ToolCallTrackingMixin, BaseAgent): - """ - Optimized handoff pattern using vanilla workflow and direct agent communication. - - Flow: - 1. Intent classifier determines which domain specialist to route to - 2. User communicates directly with assigned specialist - 3. On domain change detection, seamlessly transfer to new specialist - 4. Specialists have filtered tool access and clear boundaries + Survives across BaseAgent instances within the same session so that the + HandoffBuilder workflow can resume mid-conversation on subsequent + requests. """ - def __init__(self, state_store: Dict[str, Any], session_id: str, access_token: str | None = None) -> None: + _RETENTION = 5 + + def __init__(self, backing: Dict[str, Any]) -> None: + self._backing = backing + self._checkpoints: Dict[str, Dict[str, Any]] = backing.setdefault("checkpoints", {}) + self._async_lock = asyncio.Lock() + self._sync_lock = ThreadLock() + + async def save(self, checkpoint: WorkflowCheckpoint) -> str: + async with self._async_lock: + self._checkpoints[checkpoint.checkpoint_id] = checkpoint.to_dict() + self._backing["latest_checkpoint"] = checkpoint.checkpoint_id + self._backing["workflow_name"] = checkpoint.workflow_name + + if len(self._checkpoints) > self._RETENTION: + sorted_ids = sorted( + self._checkpoints.items(), + key=lambda item: (item[1].get("timestamp", ""), item[1].get("iteration_count", 0)), + ) + for cid, _ in sorted_ids[: -self._RETENTION]: + self._checkpoints.pop(cid, None) + return checkpoint.checkpoint_id + + async def load(self, checkpoint_id: str) -> WorkflowCheckpoint | None: + async with self._async_lock: + data = self._checkpoints.get(checkpoint_id) + if not data: + return None + return WorkflowCheckpoint.from_dict(data) + + async def list_checkpoint_ids(self, *, workflow_name: str) -> List[str]: + async with self._async_lock: + return [cid for cid, d in self._checkpoints.items() if d.get("workflow_name") == workflow_name] + + async def list_checkpoints(self, *, workflow_name: str) -> List[WorkflowCheckpoint]: + async with self._async_lock: + ids = [cid for cid, d in self._checkpoints.items() if d.get("workflow_name") == workflow_name] + return [WorkflowCheckpoint.from_dict(self._checkpoints[cid]) for cid in ids] + + async def delete(self, checkpoint_id: str) -> bool: + async with self._async_lock: + removed = self._checkpoints.pop(checkpoint_id, None) + if removed and self._backing.get("latest_checkpoint") == checkpoint_id: + self._backing.pop("latest_checkpoint", None) + return removed is not None + + async def get_latest(self, *, workflow_name: str) -> WorkflowCheckpoint | None: + async with self._async_lock: + latest_id = self._backing.get("latest_checkpoint") + if not latest_id: + return None + data = self._checkpoints.get(latest_id) + if not data or data.get("workflow_name") != workflow_name: + return None + return WorkflowCheckpoint.from_dict(data) + + @property + def latest_checkpoint_id(self) -> str | None: + with self._sync_lock: + return self._backing.get("latest_checkpoint") + + +class Agent(ToolCallTrackingMixin, BaseAgent): + """Multi-domain handoff agent backed by the native ``HandoffBuilder``.""" + + def __init__( + self, + state_store: Dict[str, Any], + session_id: str, + access_token: str | None = None, + ) -> None: super().__init__(state_store, session_id) self._access_token = access_token self._ws_manager = None - - # Track current agent and conversation history per domain - self._current_domain = state_store.get(f"{session_id}_current_domain", None) - self._domain_agents: Dict[str, FrameworkAgent] = {} - self._domain_sessions: Dict[str, AgentSession] = {} + + self._workflow: Any = None self._initialized = False - + self._domain_agents: Dict[str, FrameworkAgent] = {} + self._mcp_tool: Optional[MCPStreamableHTTPTool] = None + + # Checkpoint storage is backed by the per-session state_store so the + # workflow's conversation state survives across HTTP requests. + self._handoff_state_key = f"{session_id}_handoff_state" + backing = state_store.setdefault(self._handoff_state_key, {}) + self._checkpoint_storage = _DictCheckpointStorage(backing) + + # Track the pending ``request_info`` ID so the next turn can resume. + self._pending_request_id_key = f"{session_id}_handoff_pending_req" + self._pending_request_id: Optional[str] = state_store.get(self._pending_request_id_key) + + # Current speaking domain (used for UI hints + start-agent selection + # on resume after a process restart). + self._current_domain_key = f"{session_id}_current_domain" + self._current_domain: Optional[str] = state_store.get(self._current_domain_key) + # Turn tracking for tool grouping self._turn_key = f"{session_id}_handoff_turn" self._current_turn = state_store.get(self._turn_key, 0) - - # Initialize tool tracking from mixin + self.init_tool_tracking() - - # Context transfer configuration: -1 = all history, 0 = none, N = last N turns - self._context_transfer_turns = int(os.getenv("HANDOFF_CONTEXT_TRANSFER_TURNS", "-1")) - - # Lazy classification configuration - self._lazy_classification = os.getenv("HANDOFF_LAZY_CLASSIFICATION", "true").lower() == "true" + self._default_domain = os.getenv("HANDOFF_DEFAULT_DOMAIN", "crm_billing") - + if self._default_domain not in DOMAINS: + logger.warning( + "[HANDOFF] HANDOFF_DEFAULT_DOMAIN=%s is not a known domain; falling back to crm_billing", + self._default_domain, + ) + self._default_domain = "crm_billing" + logger.info( - f"[HANDOFF] Configuration: lazy_classification={self._lazy_classification}, " - f"default_domain={self._default_domain}, context_transfer_turns={self._context_transfer_turns}" + "[HANDOFF] Configuration: default_domain=%s current_domain=%s pending_req=%s", + self._default_domain, + self._current_domain, + self._pending_request_id, ) - def _detect_handoff_request(self, response_text: str) -> bool: - """ - Detect if the agent response contains a handoff request using pattern matching. - - This avoids the need for LLM-based detection by looking for key phrases that - agents are instructed to use when a request is outside their domain. - - Args: - response_text: The agent's response text - - Returns: - True if handoff is requested, False otherwise - """ - # Normalize text for matching - normalized = response_text.lower() - - # Define handoff patterns (in order of specificity) - handoff_patterns = [ - r"outside my area.*connect you with.*specialist", # Exact template match - r"outside my (domain|expertise|area)", # Domain boundary indication - r"connect you with.*specialist", # Explicit handoff language - r"let me (transfer|route|connect) you", # Transfer language - r"not my (specialty|expertise|domain)", # Boundary indication - r"better suited to help", # Redirection language - ] - - # Check each pattern - for pattern in handoff_patterns: - if re.search(pattern, normalized): - logger.info(f"[HANDOFF] Detected handoff request with pattern: {pattern}") - - return True - - # Additional keyword-based detection with proximity check - keywords_group1 = ["outside", "not my"] - keywords_group2 = ["area", "domain", "expertise", "specialty"] - keywords_group3 = ["connect", "transfer", "specialist", "help"] - - # Check if keywords from different groups appear within reasonable distance (100 chars) - for kw1 in keywords_group1: - if kw1 in normalized: - start_pos = normalized.find(kw1) - window = normalized[start_pos:start_pos + 100] - - has_group2 = any(kw2 in window for kw2 in keywords_group2) - has_group3 = any(kw3 in window for kw3 in keywords_group3) - - if has_group2 and has_group3: - logger.info(f"[HANDOFF] Detected handoff via keyword proximity: {kw1} + groups 2&3") - - return True - return False - def set_websocket_manager(self, manager: Any) -> None: - """Allow backend to inject WebSocket manager for streaming events.""" + """Allow backend to inject a WebSocket manager for streaming events.""" self._ws_manager = manager - logger.info(f"[HANDOFF] WebSocket manager set for handoff agent, session_id={self.session_id}") + logger.info("[HANDOFF] WebSocket manager set, session_id=%s", self.session_id) - async def _setup_agents(self) -> None: - """Initialize all domain specialist agents with filtered MCP tools.""" + # ------------------------------------------------------------------ + # Setup + # ------------------------------------------------------------------ + async def _setup(self) -> None: if self._initialized: return - # Check for either API key OR credential-based authentication has_api_key = bool(self.azure_openai_key) has_credential = bool(self.azure_credential) - + if not all([self.azure_deployment, self.azure_openai_endpoint, self.api_version]): raise RuntimeError( "Azure OpenAI configuration is incomplete. Ensure " "AZURE_OPENAI_CHAT_DEPLOYMENT, AZURE_OPENAI_ENDPOINT, and AZURE_OPENAI_API_VERSION are set." ) - if not has_api_key and not has_credential: raise RuntimeError( "Azure OpenAI authentication is not configured. Either set AZURE_OPENAI_API_KEY " @@ -279,504 +298,285 @@ async def _setup_agents(self) -> None: ) headers = self._build_headers() - base_mcp_tool = await self._create_mcp_tool(headers) - - # Connect to MCP server once to load all available tools - if base_mcp_tool: - await base_mcp_tool.__aenter__() - logger.info(f"[HANDOFF] Connected to MCP server, loaded {len(base_mcp_tool.functions)} tools") + self._mcp_tool = await self._create_mcp_tool(headers) + if self._mcp_tool is not None: + await self._mcp_tool.__aenter__() + logger.info( + "[HANDOFF] Connected to MCP server, loaded %d tools", + len(self._mcp_tool.functions), + ) - # Use API key if available, otherwise use credential-based authentication if has_api_key: - chat_client = AzureOpenAIChatClient( + chat_client = OpenAIChatClient( api_key=self.azure_openai_key, - deployment_name=self.azure_deployment, - endpoint=self.azure_openai_endpoint, + model=self.azure_deployment, + azure_endpoint=self.azure_openai_endpoint, api_version=self.api_version, ) logger.info("[HANDOFF] Using API key authentication for Azure OpenAI") else: - chat_client = AzureOpenAIChatClient( + chat_client = OpenAIChatClient( credential=self.azure_credential, - deployment_name=self.azure_deployment, - endpoint=self.azure_openai_endpoint, + model=self.azure_deployment, + azure_endpoint=self.azure_openai_endpoint, api_version=self.api_version, ) logger.info("[HANDOFF] Using managed identity authentication for Azure OpenAI") - # Create all domain specialist agents with filtered tools - for domain_id, domain_config in DOMAINS.items(): - # Create filtered tool list for this domain using common utility + # Build one Agent per domain. ``require_per_service_call_history_persistence`` + # is required by HandoffBuilder because the framework injects middleware + # that short-circuits handoff tool calls (MiddlewareTermination); without + # this flag local history providers would persist tool results the + # service never observed. + for domain_id, cfg in DOMAINS.items(): domain_tools = create_filtered_tool_list( - base_mcp_tool=base_mcp_tool, - allowed_tool_names=domain_config["tools"], - agent_name=domain_id + base_mcp_tool=self._mcp_tool, + allowed_tool_names=cfg["tools"], + agent_name=domain_id, ) - agent = FrameworkAgent( client=chat_client, name=domain_id, - instructions=domain_config["instructions"], - tools=domain_tools, # Pass list of filtered AIFunction objects + description=cfg["description"], + instructions=cfg["instructions"], + tools=domain_tools, default_options=ChatOptions(model_id=self.openai_model_name), + require_per_service_call_history_persistence=True, ) - - # Enter agent context await agent.__aenter__() self._domain_agents[domain_id] = agent - - # Create or restore session for this domain - session_state_key = f"{self.session_id}_session_{domain_id}" - session_state = self.state_store.get(session_state_key) - - if session_state: - self._domain_sessions[domain_id] = AgentSession.from_dict(session_state) - else: - self._domain_sessions[domain_id] = agent.create_session() + + # Choose start agent: prefer the domain that handled the prior turn + # (so a fresh process can route the next message to the same specialist), + # otherwise fall back to the configured default. Note: ``with_start_agent`` + # only matters when there is no checkpoint to resume from. + start_id = ( + self._current_domain + if self._current_domain in self._domain_agents + else self._default_domain + ) + + # Default mesh topology (no add_handoff calls = every agent can hand + # off to every other agent), which matches the previous "any specialist + # can route anywhere" behaviour. + self._workflow = ( + HandoffBuilder( + name=f"handoff-{self.session_id}", + participants=list(self._domain_agents.values()), + ) + .with_start_agent(self._domain_agents[start_id]) + .with_checkpointing(self._checkpoint_storage) + .build() + ) self._initialized = True - logger.info(f"[HANDOFF] Initialized {len(self._domain_agents)} domain specialists with filtered tools") + logger.info( + "[HANDOFF] Initialized %d domain specialists with native HandoffBuilder; start=%s", + len(self._domain_agents), + start_id, + ) def _build_headers(self) -> Dict[str, str]: - headers: Dict[str, str] = {"Content-Type": "application/json"} + h: Dict[str, str] = {"Content-Type": "application/json"} if self._access_token: - headers["Authorization"] = f"Bearer {self._access_token}" - return headers + h["Authorization"] = f"Bearer {self._access_token}" + return h async def _create_mcp_tool(self, headers: Dict[str, str]) -> MCPStreamableHTTPTool | None: - """ - Create the base MCP tool that will be shared across all domain specialists. - - The tool will be connected once and then filtered for each domain. - - Returns: - MCPStreamableHTTPTool instance or None if MCP server is not configured - """ if not self.mcp_server_uri: logger.warning("MCP_SERVER_URI is not configured; agents will run without MCP tools.") return None - - tool = MCPStreamableHTTPTool( + return MCPStreamableHTTPTool( name="mcp-streamable", url=self.mcp_server_uri, headers=headers, timeout=30, request_timeout=30, ) - - return tool - - async def _build_context_prefix(self, from_domain: str, to_domain: str) -> str | None: - """ - Build a context prefix to prepend to the user's prompt on handoff. - - Args: - from_domain: Previous domain specialist - to_domain: New domain specialist - - Returns: - Context prefix string or None if no context to transfer - """ - if self._context_transfer_turns == 0: - logger.info(f"[HANDOFF] Context transfer disabled (HANDOFF_CONTEXT_TRANSFER_TURNS=0)") - return None - - # Get chat history - history = self.chat_history - if not history: - logger.info(f"[HANDOFF] No chat history to transfer") - return None - - # Determine how much history to transfer - if self._context_transfer_turns == -1: - # Transfer all history - context_messages = history - logger.info(f"[HANDOFF] Transferring all {len(history)} messages to {to_domain}") - else: - # Transfer last N turns (each turn = user + assistant pair) - turns_to_transfer = self._context_transfer_turns * 2 # Each turn has 2 messages - context_messages = history[-turns_to_transfer:] if len(history) > turns_to_transfer else history - logger.info(f"[HANDOFF] Transferring last {self._context_transfer_turns} turns ({len(context_messages)} messages) to {to_domain}") - - # Build context summary - context_parts = [] - for msg in context_messages: - role = msg.get("role", "unknown") - content = msg.get("content", "") - if role == "user": - context_parts.append(f"User: {content}") - elif role == "assistant": - context_parts.append(f"Previous Specialist: {content}") - - context_summary = "\n".join(context_parts) - - # Build context prefix - context_prefix = ( - f"[CONTEXT FROM PREVIOUS CONVERSATION]\n" - f"The user was previously speaking with the {DOMAINS[from_domain]['name']}.\n" - f"Here is the recent conversation history for your reference:\n\n" - f"{context_summary}\n\n" - f"[END OF CONTEXT]\n" - f"Now, please address their current request:" - ) - - logger.info(f"[HANDOFF] Built context prefix with {len(context_messages)} messages for {to_domain}") - return context_prefix - - async def _classify_intent(self, user_message: str, current_domain: str | None) -> Dict[str, Any]: - """ - Use structured output with Pydantic to classify user intent and detect domain changes. - - This uses the beta.chat.completions.parse() API with response_format to ensure - robust JSON parsing without validation errors. - - Args: - user_message: The user's message to classify - current_domain: Current active domain (or None for first message) - - Returns: - Dictionary with keys: domain, is_domain_change, confidence, reasoning - """ - # If no current domain, route to default domain - if not current_domain: - return { - "domain": self._default_domain, - "is_domain_change": True, - "confidence": 1.0, - "reasoning": f"First message, routing to {self._default_domain}" - } - - # Build classification prompt - prompt = INTENT_CLASSIFIER_PROMPT.format( - current_domain=current_domain, - user_message=user_message - ) - - try: - # Use OpenAI client directly for structured output support - from openai import AsyncAzureOpenAI - - client = AsyncAzureOpenAI( - api_key=self.azure_openai_key, - api_version=self.api_version, - azure_endpoint=self.azure_openai_endpoint, - ) - - # Use beta API with structured output - completion = await client.beta.chat.completions.parse( - model=self.azure_deployment, - messages=[ - {"role": "user", "content": prompt} - ], - response_format=IntentClassification, - ) - - # Extract structured result - intent = completion.choices[0].message.parsed - - result = { - "domain": intent.domain, - "is_domain_change": intent.is_domain_change, - "confidence": intent.confidence, - "reasoning": intent.reasoning, - } - - logger.info(f"[HANDOFF] Intent classification: {result}") - return result - - except Exception as exc: - logger.error(f"[HANDOFF] Intent classification failed: {exc}", exc_info=True) - - # Fallback: randomly select a different domain (not current) - available_domains = [d for d in DOMAINS.keys() if d != current_domain] - fallback_domain = random.choice(available_domains) if available_domains else current_domain - - logger.warning(f"[HANDOFF] Falling back to random domain: {fallback_domain}") - - return { - "domain": fallback_domain, - "is_domain_change": fallback_domain != current_domain, - "confidence": 0.3, - "reasoning": f"Classification error, randomly selected {fallback_domain}" - } + # ------------------------------------------------------------------ + # Chat entry point + # ------------------------------------------------------------------ async def chat_async(self, prompt: str) -> str: - """ - Main chat entry point with intelligent routing and lazy classification. - - Flow: - 1. If first message OR lazy classification is disabled: classify intent upfront - 2. If lazy classification enabled: route to current agent first - 3. Check agent response for handoff markers - 4. If handoff detected: run intent classification and re-route - 5. Stream response back to user - """ - await self._setup_agents() - - # Clear tool calls from previous request (from mixin) + await self._setup() self.clear_tool_calls() - # Increment turn counter self._current_turn += 1 self.state_store[self._turn_key] = self._current_turn - # Determine if we need upfront classification - is_first_message = self._current_domain is None - needs_upfront_classification = is_first_message or not self._lazy_classification - - target_domain = None - is_domain_change = False - - if needs_upfront_classification: - # Run intent classification before routing - logger.info(f"[HANDOFF] Running upfront classification (first_message={is_first_message}, lazy={self._lazy_classification})") - intent = await self._classify_intent(prompt, self._current_domain) - target_domain = intent["domain"] - is_domain_change = intent["is_domain_change"] - - # Announce handoff if domain changed - if is_domain_change and self._current_domain: - handoff_message = ( - f"I'll connect you with our {DOMAINS[target_domain]['name']} " - f"who can better assist with that." - ) - - if self._ws_manager: - await self._ws_manager.broadcast( - self.session_id, - { - "type": "handoff_announcement", - "from_domain": self._current_domain, - "to_domain": target_domain, - "message": handoff_message, - }, - ) - - logger.info(f"[HANDOFF] Domain change: {self._current_domain} -> {target_domain}") - else: - # Lazy mode: use current domain (will check response for handoff markers) - target_domain = self._current_domain - logger.info(f"[HANDOFF] Using lazy classification, routing to current domain: {target_domain}") - - # Update current domain - previous_domain = self._current_domain - self._current_domain = target_domain - self.state_store[f"{self.session_id}_current_domain"] = target_domain - - # Get the specialist agent and session - agent = self._domain_agents[target_domain] - session = self._domain_sessions[target_domain] - - # Prepare the prompt with context if this is a handoff - actual_prompt = prompt - if is_domain_change and previous_domain and previous_domain != target_domain: - context_prefix = await self._build_context_prefix(previous_domain, target_domain) - if context_prefix: - actual_prompt = f"{context_prefix}\n\n{prompt}" - logger.info(f"[HANDOFF] Added context prefix to prompt for {target_domain}") - - # Notify UI that agent started - if self._ws_manager: - await self._ws_manager.broadcast( - self.session_id, - { - "type": "agent_start", - "agent_id": target_domain, - "agent_name": DOMAINS[target_domain]["name"], - "show_message_in_internal_process": is_domain_change, # Show handoff in left panel - }, + latest_checkpoint = self._checkpoint_storage.latest_checkpoint_id + + # Resume an in-flight workflow (typical path after the first turn) by + # responding to the pending HandoffAgentUserRequest with the new user + # message. Otherwise start a fresh run. + if latest_checkpoint and self._pending_request_id: + user_msgs = HandoffAgentUserRequest.create_response(prompt) + stream = self._workflow.run( + responses={self._pending_request_id: user_msgs}, + checkpoint_id=latest_checkpoint, + stream=True, + ) + logger.info( + "[HANDOFF] Resuming workflow from checkpoint=%s with pending request=%s", + latest_checkpoint, + self._pending_request_id, ) + else: + stream = self._workflow.run(prompt, stream=True) + logger.info("[HANDOFF] Starting fresh handoff workflow run") + + active_agent_id: Optional[str] = self._current_domain or self._default_domain + per_agent_text: Dict[str, List[str]] = {} + new_pending_request_id: Optional[str] = None + + # Announce the initial speaking agent so the UI shows activity even + # before the first token arrives. + await self._notify_agent_start(active_agent_id, is_handoff=False) - # Stream response from specialist agent - full_response = [] - try: - response_stream = agent.run(actual_prompt, stream=True, session=session) - async for chunk in response_stream: - # Process contents in the chunk - if hasattr(chunk, 'contents') and chunk.contents: - for content in chunk.contents: - # Check for tool/function calls - track with arguments - if content.type == "function_call": - if content.name: - # New function call - finalize previous and start new - self.track_function_call_start(content.name) - - if self._ws_manager: - await self._ws_manager.broadcast( - self.session_id, - { - "type": "tool_called", - "agent_id": target_domain, - "tool_name": content.name, - "turn": self._current_turn, - }, - ) - - # Accumulate arguments - args_chunk = getattr(content, 'arguments', '') - if args_chunk: - self.track_function_call_arguments(args_chunk) - - elif content.type == "function_result": - # Function completed - finalize - self.finalize_tool_tracking() - - # Extract text from chunk - if hasattr(chunk, 'text') and chunk.text: - full_response.append(chunk.text) - - # Broadcast token to WebSocket - if self._ws_manager: - await self._ws_manager.broadcast( - self.session_id, - { - "type": "agent_token", - "agent_id": target_domain, - "content": chunk.text, - }, - ) - + async for event in stream: + etype = event.type + + if etype == "handoff_sent": + data = event.data # HandoffSentEvent + src_id = getattr(data, "source", None) or active_agent_id or "" + tgt_id = getattr(data, "target", None) or "" + logger.info("[HANDOFF] handoff_sent: %s -> %s", src_id, tgt_id) + await self._notify_handoff(src_id, tgt_id) + if tgt_id: + active_agent_id = tgt_id + await self._notify_agent_start(tgt_id, is_handoff=True) + continue + + if etype == "output" and event.executor_id in self._domain_agents: + update = event.data # AgentResponseUpdate + if update is None: + continue + await self._handle_agent_update(event.executor_id, update, per_agent_text) + continue + + if etype == "request_info": + new_pending_request_id = event.request_id + continue except Exception as exc: - logger.error(f"[HANDOFF] Error during agent streaming: {exc}", exc_info=True) + logger.error("[HANDOFF] Workflow error: %s", exc, exc_info=True) raise - - # Finalize any remaining function call + + # Finalize any in-flight tool tracking self.finalize_tool_tracking() - assistant_response = ''.join(full_response) - - # Check if lazy classification is enabled and handoff was requested - if self._lazy_classification and self._detect_handoff_request(assistant_response): - logger.info(f"[HANDOFF] Handoff marker detected in response, running intent classification") - - # Run intent classification to determine new domain - intent = await self._classify_intent(prompt, target_domain) - new_target_domain = intent["domain"] - - if new_target_domain != target_domain: - logger.info(f"[HANDOFF] Re-routing from {target_domain} to {new_target_domain}") - - # Update domain - self._current_domain = new_target_domain - self.state_store[f"{self.session_id}_current_domain"] = new_target_domain - - # Announce handoff - handoff_message = ( - f"I'll connect you with our {DOMAINS[new_target_domain]['name']} " - f"who can better assist with that." - ) - - if self._ws_manager: - await self._ws_manager.broadcast( - self.session_id, - { - "type": "handoff_announcement", - "from_domain": target_domain, - "to_domain": new_target_domain, - "message": handoff_message, - }, - ) - - # Get new agent and session - new_agent = self._domain_agents[new_target_domain] - new_session = self._domain_sessions[new_target_domain] - - # Build context prefix - context_prefix = await self._build_context_prefix(target_domain, new_target_domain) - actual_prompt_handoff = f"{context_prefix}\n\n{prompt}" if context_prefix else prompt - - # Notify UI - if self._ws_manager: - await self._ws_manager.broadcast( - self.session_id, - { - "type": "agent_start", - "agent_id": new_target_domain, - "agent_name": DOMAINS[new_target_domain]["name"], - "show_message_in_internal_process": True, - }, - ) - - # Get response from new agent - full_response_handoff = [] - try: - response_stream_handoff = new_agent.run(actual_prompt_handoff, stream=True, session=new_session) - async for chunk in response_stream_handoff: - if hasattr(chunk, 'contents') and chunk.contents: - for content in chunk.contents: - if content.type == "function_call": - if content.name: - self.track_function_call_start(content.name) - - if self._ws_manager: - await self._ws_manager.broadcast( - self.session_id, - { - "type": "tool_called", - "agent_id": new_target_domain, - "tool_name": content.name, - "turn": self._current_turn, - }, - ) - - args_chunk = getattr(content, 'arguments', '') - if args_chunk: - self.track_function_call_arguments(args_chunk) - - elif content.type == "function_result": - self.finalize_tool_tracking() - - if hasattr(chunk, 'text') and chunk.text: - full_response_handoff.append(chunk.text) - - if self._ws_manager: - await self._ws_manager.broadcast( - self.session_id, - { - "type": "agent_token", - "agent_id": new_target_domain, - "content": chunk.text, - }, - ) - except Exception as exc: - logger.error(f"[HANDOFF] Error during handoff agent streaming: {exc}", exc_info=True) - raise - - # Finalize any remaining function call - self.finalize_tool_tracking() - - # Use handoff response - assistant_response = ''.join(full_response_handoff) - target_domain = new_target_domain - session = new_session + # Pick the response that should be returned to the user — the last + # agent to speak (which is ``active_agent_id`` after any handoffs). + if active_agent_id and active_agent_id in per_agent_text: + assistant_response = "".join(per_agent_text[active_agent_id]) + else: + # Fallback: concatenate all speaker output in order + assistant_response = "".join(t for buf in per_agent_text.values() for t in buf) + + # Persist resumption state for the next turn + self._pending_request_id = new_pending_request_id + if new_pending_request_id is not None: + self.state_store[self._pending_request_id_key] = new_pending_request_id + else: + self.state_store.pop(self._pending_request_id_key, None) + + if active_agent_id: + self._current_domain = active_agent_id + self.state_store[self._current_domain_key] = active_agent_id - # Send final result if self._ws_manager: await self._ws_manager.broadcast( self.session_id, - { - "type": "final_result", - "content": assistant_response, - }, + {"type": "final_result", "content": assistant_response}, ) - # Update chat history - messages = [ - {"role": "user", "content": prompt}, - {"role": "assistant", "content": assistant_response}, - ] - self.append_to_chat_history(messages) + # Mirror the conversation in the BaseAgent chat history (used by the + # backend's history APIs and the magentic_group context-transfer logic). + self.append_to_chat_history( + [ + {"role": "user", "content": prompt}, + {"role": "assistant", "content": assistant_response}, + ] + ) + self._setstate({"mode": "handoff_multi_domain", "current_domain": self._current_domain}) + + return assistant_response + + # ------------------------------------------------------------------ + # Streaming helpers + # ------------------------------------------------------------------ + async def _handle_agent_update( + self, + executor_id: str, + update: Any, + per_agent_text: Dict[str, List[str]], + ) -> None: + contents = getattr(update, "contents", None) or [] + for content in contents: + ctype = getattr(content, "type", None) + if ctype == "function_call": + name = getattr(content, "name", None) + if name and not name.startswith("handoff_to_"): + # Real domain tool — track for the UI. Synthetic handoff + # tools are filtered out because the framework already + # surfaces those as ``handoff_sent`` events. + self.track_function_call_start(name) + if self._ws_manager: + await self._ws_manager.broadcast( + self.session_id, + { + "type": "tool_called", + "agent_id": executor_id, + "tool_name": name, + "turn": self._current_turn, + }, + ) + args_chunk = getattr(content, "arguments", "") + if args_chunk: + self.track_function_call_arguments(args_chunk) + elif ctype == "function_result": + self.finalize_tool_tracking() - # Save session state for this domain - session_state_key = f"{self.session_id}_session_{target_domain}" - new_state = session.to_dict() - self.state_store[session_state_key] = new_state + text = getattr(update, "text", None) + if text: + per_agent_text.setdefault(executor_id, []).append(text) + if self._ws_manager: + await self._ws_manager.broadcast( + self.session_id, + { + "type": "agent_token", + "agent_id": executor_id, + "content": text, + }, + ) - # Save overall state - self._setstate({ - "mode": "handoff_multi_domain", - "current_domain": target_domain, - }) + async def _notify_agent_start(self, agent_id: Optional[str], *, is_handoff: bool) -> None: + if not self._ws_manager or not agent_id: + return + cfg = DOMAINS.get(agent_id, {}) + await self._ws_manager.broadcast( + self.session_id, + { + "type": "agent_start", + "agent_id": agent_id, + "agent_name": cfg.get("name", agent_id), + # Handoffs should appear in the UI's left "internal process" panel; + # the very first agent_start of a turn should not. + "show_message_in_internal_process": is_handoff, + }, + ) - return assistant_response + async def _notify_handoff(self, from_id: str, to_id: str) -> None: + if not self._ws_manager: + return + to_name = DOMAINS.get(to_id, {}).get("name", to_id) + msg = f"I'll connect you with our {to_name} who can better assist with that." + await self._ws_manager.broadcast( + self.session_id, + { + "type": "handoff_announcement", + "from_domain": from_id, + "to_domain": to_id, + "message": msg, + }, + ) diff --git a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py index cefeb3ca5..33e2c679d 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py +++ b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py @@ -23,7 +23,7 @@ MagenticPlanReviewRequest, MagenticPlanReviewResponse, ) -from agent_framework.azure import AzureOpenAIChatClient # type: ignore[import] +from agent_framework.openai import OpenAIChatClient from agents.base_agent import BaseAgent, ToolCallTrackingMixin from agents.agent_framework.utils import create_filtered_tool_list @@ -42,11 +42,11 @@ def __init__(self, backing_store: Dict[str, Any]) -> None: self._async_lock = asyncio.Lock() self._sync_lock = ThreadLock() - async def save_checkpoint(self, checkpoint: WorkflowCheckpoint) -> str: + async def save(self, checkpoint: WorkflowCheckpoint) -> str: async with self._async_lock: self._checkpoints[checkpoint.checkpoint_id] = checkpoint.to_dict() self._backing["latest_checkpoint"] = checkpoint.checkpoint_id - self._backing["workflow_id"] = checkpoint.workflow_id + self._backing["workflow_name"] = checkpoint.workflow_name if len(self._checkpoints) > self._RETENTION: sorted_ids = sorted( @@ -57,34 +57,39 @@ async def save_checkpoint(self, checkpoint: WorkflowCheckpoint) -> str: self._checkpoints.pop(checkpoint_id, None) return checkpoint.checkpoint_id - async def load_checkpoint(self, checkpoint_id: str) -> WorkflowCheckpoint | None: + async def load(self, checkpoint_id: str) -> WorkflowCheckpoint | None: async with self._async_lock: data = self._checkpoints.get(checkpoint_id) if not data: return None return WorkflowCheckpoint.from_dict(data) - async def list_checkpoint_ids(self, workflow_id: str | None = None) -> List[str]: + async def list_checkpoint_ids(self, *, workflow_name: str) -> List[str]: async with self._async_lock: - if workflow_id is None: - return list(self._checkpoints.keys()) - return [cid for cid, data in self._checkpoints.items() if data.get("workflow_id") == workflow_id] + return [cid for cid, data in self._checkpoints.items() if data.get("workflow_name") == workflow_name] - async def list_checkpoints(self, workflow_id: str | None = None) -> List[WorkflowCheckpoint]: + async def list_checkpoints(self, *, workflow_name: str) -> List[WorkflowCheckpoint]: async with self._async_lock: - if workflow_id is None: - ids = list(self._checkpoints.keys()) - else: - ids = [cid for cid, data in self._checkpoints.items() if data.get("workflow_id") == workflow_id] + ids = [cid for cid, data in self._checkpoints.items() if data.get("workflow_name") == workflow_name] return [WorkflowCheckpoint.from_dict(self._checkpoints[cid]) for cid in ids] - async def delete_checkpoint(self, checkpoint_id: str) -> bool: + async def delete(self, checkpoint_id: str) -> bool: async with self._async_lock: removed = self._checkpoints.pop(checkpoint_id, None) if removed and self._backing.get("latest_checkpoint") == checkpoint_id: self._backing.pop("latest_checkpoint", None) return removed is not None + async def get_latest(self, *, workflow_name: str) -> WorkflowCheckpoint | None: + async with self._async_lock: + latest_id = self._backing.get("latest_checkpoint") + if not latest_id: + return None + data = self._checkpoints.get(latest_id) + if not data or data.get("workflow_name") != workflow_name: + return None + return WorkflowCheckpoint.from_dict(data) + @property def latest_checkpoint_id(self) -> str | None: with self._sync_lock: @@ -214,8 +219,8 @@ def __init__( logger.warning( "[AgentFramework-Magentic] Ignoring checkpoint storage override because it does not implement CheckpointStorage." ) - self._participant_client: Optional[AzureOpenAIChatClient] = None - self._manager_client: Optional[AzureOpenAIChatClient] = None + self._participant_client: Optional[OpenAIChatClient] = None + self._manager_client: Optional[OpenAIChatClient] = None self._workflow_event_logging_enabled = bool(self._config.get("log_workflow_events", False)) self._enable_plan_review = bool(self._config.get("enable_plan_review", False)) self._manager_instructions = self._config.get( @@ -353,32 +358,32 @@ async def _maybe_create_tools(self, headers: Dict[str, str]) -> List[MCPStreamab return None - def _get_participant_client(self) -> AzureOpenAIChatClient: + def _get_participant_client(self) -> OpenAIChatClient: if self._participant_client is None: self._participant_client = self._build_chat_client() return self._participant_client - def _get_manager_client(self) -> AzureOpenAIChatClient: + def _get_manager_client(self) -> OpenAIChatClient: if self._manager_client is None: self._manager_client = self._build_chat_client() return self._manager_client - def _build_chat_client(self) -> AzureOpenAIChatClient: + def _build_chat_client(self) -> OpenAIChatClient: # Use API key if available, otherwise use credential-based authentication if self.azure_openai_key: logger.info("[AgentFramework-Magentic] Using API key authentication for Azure OpenAI") - return AzureOpenAIChatClient( + return OpenAIChatClient( api_key=self.azure_openai_key, - deployment_name=self.azure_deployment, - endpoint=self.azure_openai_endpoint, + model=self.azure_deployment, + azure_endpoint=self.azure_openai_endpoint, api_version=self.api_version, ) elif self.azure_credential: logger.info("[AgentFramework-Magentic] Using managed identity authentication for Azure OpenAI") - return AzureOpenAIChatClient( + return OpenAIChatClient( credential=self.azure_credential, - deployment_name=self.azure_deployment, - endpoint=self.azure_openai_endpoint, + model=self.azure_deployment, + azure_endpoint=self.azure_openai_endpoint, api_version=self.api_version, ) else: @@ -436,8 +441,8 @@ async def _resume_previous_run( async def _build_workflow( self, - participant_client: AzureOpenAIChatClient, - manager_client: AzureOpenAIChatClient, + participant_client: OpenAIChatClient, + manager_client: OpenAIChatClient, tools: List[MCPStreamableHTTPTool] | None, checkpoint_storage: CheckpointStorage, ) -> Any: @@ -471,7 +476,7 @@ async def _build_workflow( async def _create_participants( self, - participant_client: AzureOpenAIChatClient, + participant_client: OpenAIChatClient, tools: Iterable[MCPStreamableHTTPTool] | None, ) -> Dict[str, FrameworkAgent]: # Get base MCP tool (connect once, filter per agent) @@ -695,7 +700,7 @@ async def _run_workflow( async def _process_workflow_event(self, event: WorkflowEvent) -> None: """Process workflow events and stream to WebSocket clients. - In 1.0.0rc1, all events are WorkflowEvent with a .type field: + In agent-framework 1.2.x, all events are WorkflowEvent with a .type field: - 'magentic_orchestrator': plan, replan, progress ledger updates - 'request_info': plan review requests - 'data': streaming tokens from participant agents @@ -913,8 +918,8 @@ def _coerce_checkpoint_storage(self, candidate: Any) -> Optional[CheckpointStora return None required_methods = [ - "save_checkpoint", - "load_checkpoint", + "save", + "load", ] for method_name in required_methods: @@ -1070,14 +1075,16 @@ async def _purge_checkpoint_storage(self, storage: CheckpointStorage) -> None: except Exception as exc: logger.debug("clear_all failed: %s", exc) - # Fallback: list and delete individually + # Fallback: list and delete individually using the 1.2.x CheckpointStorage protocol. + # ``list_checkpoint_ids`` now requires a keyword-only ``workflow_name``. list_fn = getattr(storage, "list_checkpoint_ids", None) - delete_fn = getattr(storage, "delete_checkpoint", None) + delete_fn = getattr(storage, "delete", None) if not (callable(list_fn) and callable(delete_fn)): return try: - checkpoint_ids = await self._call_maybe_async(list_fn) + workflow_name = self._workflow_name_for_storage(storage) + checkpoint_ids = await self._call_maybe_async(list_fn, workflow_name=workflow_name) if workflow_name else [] if checkpoint_ids: for checkpoint_id in checkpoint_ids: try: @@ -1087,9 +1094,22 @@ async def _purge_checkpoint_storage(self, storage: CheckpointStorage) -> None: except Exception as exc: logger.debug("Unable to enumerate checkpoints: %s", exc) + @staticmethod + def _workflow_name_for_storage(storage: CheckpointStorage) -> str | None: + """Best-effort lookup of the active workflow name for a storage instance. + + The DictCheckpointStorage shipped with this module records the workflow + name on every ``save()``; for other storages we cannot infer it. + """ + backing = getattr(storage, "_backing", None) + if isinstance(backing, dict): + return backing.get("workflow_name") + return None + async def _get_latest_checkpoint_id(self, storage: CheckpointStorage) -> Optional[str]: """Get the most recent checkpoint ID from storage.""" - # Try latest_checkpoint_id property/method first + # Try latest_checkpoint_id property/method first (nonstandard convenience + # exposed by the in-process DictCheckpointStorage in this module). latest_id_attr = getattr(storage, "latest_checkpoint_id", None) if callable(latest_id_attr): try: @@ -1101,11 +1121,28 @@ async def _get_latest_checkpoint_id(self, storage: CheckpointStorage) -> Optiona elif isinstance(latest_id_attr, str): return latest_id_attr - # Try list_checkpoints and get latest + # Best-effort: the 1.2.x ``CheckpointStorage`` protocol requires a + # keyword-only ``workflow_name`` on ``get_latest`` / ``list_checkpoints`` + # / ``list_checkpoint_ids``. Without one we cannot call those methods. + workflow_name = self._workflow_name_for_storage(storage) + + # Try the 1.2.x ``get_latest`` shortcut. + get_latest_fn = getattr(storage, "get_latest", None) + if callable(get_latest_fn) and workflow_name: + try: + latest = await self._call_maybe_async(get_latest_fn, workflow_name=workflow_name) + if latest is not None: + checkpoint_id = getattr(latest, "checkpoint_id", None) + if isinstance(checkpoint_id, str): + return checkpoint_id + except Exception: + pass + + # Try list_checkpoints and pick the most recent entry. list_checkpoints_fn = getattr(storage, "list_checkpoints", None) - if callable(list_checkpoints_fn): + if callable(list_checkpoints_fn) and workflow_name: try: - checkpoints = await self._call_maybe_async(list_checkpoints_fn) + checkpoints = await self._call_maybe_async(list_checkpoints_fn, workflow_name=workflow_name) if checkpoints: latest = max(checkpoints, key=lambda cp: ( getattr(cp, "timestamp", ""), @@ -1115,11 +1152,11 @@ async def _get_latest_checkpoint_id(self, storage: CheckpointStorage) -> Optiona except Exception: pass - # Fallback: list checkpoint IDs and return last + # Fallback: list checkpoint IDs and return last. list_ids_fn = getattr(storage, "list_checkpoint_ids", None) - if callable(list_ids_fn): + if callable(list_ids_fn) and workflow_name: try: - checkpoint_ids = await self._call_maybe_async(list_ids_fn) + checkpoint_ids = await self._call_maybe_async(list_ids_fn, workflow_name=workflow_name) if checkpoint_ids: return checkpoint_ids[-1] except Exception: diff --git a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py index 363b2c64b..394e199db 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py @@ -11,7 +11,7 @@ from typing import Any, Dict, List from agent_framework import Agent as FrameworkAgent, AgentSession, ChatOptions, MCPStreamableHTTPTool -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.openai import OpenAIChatClient from agents.base_agent import BaseAgent, ToolCallTrackingMixin @@ -88,8 +88,8 @@ async def _setup_agents(self) -> None: # Create chat client client_kwargs = { - "deployment_name": self.azure_deployment, - "endpoint": self.azure_openai_endpoint, + "model": self.azure_deployment, + "azure_endpoint": self.azure_openai_endpoint, "api_version": self.api_version, } if self.azure_openai_key: @@ -97,7 +97,7 @@ async def _setup_agents(self) -> None: else: client_kwargs["credential"] = self.azure_credential - chat_client = AzureOpenAIChatClient(**client_kwargs) + chat_client = OpenAIChatClient(**client_kwargs) # Create MCP tools tools = await self._create_mcp_tools() diff --git a/agentic_ai/agents/agent_framework/single_agent.py b/agentic_ai/agents/agent_framework/single_agent.py index 461a6b191..a9917754c 100644 --- a/agentic_ai/agents/agent_framework/single_agent.py +++ b/agentic_ai/agents/agent_framework/single_agent.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List from agent_framework import Agent as FrameworkAgent, AgentSession, ChatOptions, MCPStreamableHTTPTool -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.openai import OpenAIChatClient from agents.base_agent import BaseAgent, ToolCallTrackingMixin @@ -55,18 +55,18 @@ async def _setup_single_agent(self) -> None: # Use API key if available, otherwise use credential-based authentication if has_api_key: - chat_client = AzureOpenAIChatClient( + chat_client = OpenAIChatClient( api_key=self.azure_openai_key, - deployment_name=self.azure_deployment, - endpoint=self.azure_openai_endpoint, + model=self.azure_deployment, + azure_endpoint=self.azure_openai_endpoint, api_version=self.api_version, ) logger.info("[AgentFramework] Using API key authentication for Azure OpenAI") else: - chat_client = AzureOpenAIChatClient( + chat_client = OpenAIChatClient( credential=self.azure_credential, - deployment_name=self.azure_deployment, - endpoint=self.azure_openai_endpoint, + model=self.azure_deployment, + azure_endpoint=self.azure_openai_endpoint, api_version=self.api_version, ) logger.info("[AgentFramework] Using managed identity authentication for Azure OpenAI") diff --git a/agentic_ai/agents/mcp_agent_demo/README.md b/agentic_ai/agents/mcp_agent_demo/README.md index 5ff167b8c..1f8ad8401 100644 --- a/agentic_ai/agents/mcp_agent_demo/README.md +++ b/agentic_ai/agents/mcp_agent_demo/README.md @@ -422,8 +422,7 @@ uv run python workflow_group_chat.py | Package | Purpose | |---------|---------| -| [agent-framework-core](https://github.com/microsoft/agent-framework) | Microsoft Agent Framework — agents, tools, MCP client | -| [agent-framework-orchestrations](https://github.com/microsoft/agent-framework) | GroupChatBuilder for multi-agent workflows | +| [agent-framework](https://github.com/microsoft/agent-framework) | Microsoft Agent Framework 1.2.1 — unified umbrella package (agents, tools, MCP client, orchestrations including the native `HandoffBuilder`, `GroupChatBuilder`, etc.) | | [fastmcp](https://github.com/jlowin/fastmcp) | PrefectHQ FastMCP v3 — stateful MCP server with session support | | [langgraph](https://github.com/langchain-ai/langgraph) | Stateful agent graphs with MemorySaver | | [langchain-openai](https://github.com/langchain-ai/langchain) | Azure OpenAI integration for LangGraph | diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py b/agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py index 8bd4fb6f9..10b3eebc0 100644 --- a/agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py +++ b/agentic_ai/agents/mcp_agent_demo/mcp_client_agent.py @@ -24,7 +24,7 @@ load_dotenv(env_path) from agent_framework import Agent, MCPStreamableHTTPTool -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.openai import OpenAIChatClient async def main() -> None: @@ -49,10 +49,10 @@ async def main() -> None: print(f"✅ Connected! Tools available from MCP server: {[t.name for t in mcp_tool.functions]}") # 2) Create a local Coordinator agent that wraps the MCP tool - client = AzureOpenAIChatClient( + client = OpenAIChatClient( api_key=api_key, - endpoint=endpoint, - deployment_name=deployment, + azure_endpoint=endpoint, + model=deployment, api_version=api_version, ) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py b/agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py index a9d2b121d..ca6f10d0e 100644 --- a/agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py +++ b/agentic_ai/agents/mcp_agent_demo/mcp_client_hybrid.py @@ -36,7 +36,7 @@ load_dotenv(env_path) from agent_framework import Agent, MCPStreamableHTTPTool -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.openai import OpenAIChatClient # ── The same raw EDR alert used in the typed-contracts demo ───────────────── @@ -118,10 +118,10 @@ async def main() -> None: print(f" ✅ Connected — {len(tool_names)} tools: {', '.join(tool_names)}") print() - client = AzureOpenAIChatClient( + client = OpenAIChatClient( api_key=api_key, - endpoint=endpoint, - deployment_name=deployment, + azure_endpoint=endpoint, + model=deployment, api_version=api_version, ) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py b/agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py index 88ec20689..f186608a7 100644 --- a/agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py +++ b/agentic_ai/agents/mcp_agent_demo/mcp_client_stateful.py @@ -28,7 +28,7 @@ load_dotenv(env_path) from agent_framework import Agent, MCPStreamableHTTPTool -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.openai import OpenAIChatClient async def main() -> None: @@ -61,10 +61,10 @@ async def main() -> None: print(f"✅ Connected! Tools available: {tool_names}") # ── Create the local coordinator agent ────────────────────────── - client = AzureOpenAIChatClient( + client = OpenAIChatClient( api_key=api_key, - endpoint=endpoint, - deployment_name=deployment, + azure_endpoint=endpoint, + model=deployment, api_version=api_version, ) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server.py b/agentic_ai/agents/mcp_agent_demo/mcp_server.py index 1fda8ceea..4f562c761 100644 --- a/agentic_ai/agents/mcp_agent_demo/mcp_server.py +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server.py @@ -25,7 +25,7 @@ load_dotenv(env_path) from agent_framework import Agent, tool -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.openai import OpenAIChatClient from mcp.server.fastmcp import FastMCP # ── Domain tools the agent can use ────────────────────────────────────────── @@ -89,10 +89,10 @@ def main() -> None: print(f"🔧 Deployment : {deployment}") # 1) Create the agent-framework Agent - client = AzureOpenAIChatClient( + client = OpenAIChatClient( api_key=api_key, - endpoint=endpoint, - deployment_name=deployment, + azure_endpoint=endpoint, + model=deployment, api_version=api_version, ) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py b/agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py index 8e691c76b..406c0595b 100644 --- a/agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server_hybrid.py @@ -69,7 +69,7 @@ from agent_framework import Agent, AgentResponse from agent_framework._sessions import AgentSession -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.openai import OpenAIChatClient from fastmcp import FastMCP from fastmcp.server.context import Context @@ -239,10 +239,10 @@ def main() -> None: print(f"🔧 Azure OpenAI endpoint : {endpoint}") print(f"🔧 Deployment : {deployment}") - llm_client = AzureOpenAIChatClient( + llm_client = OpenAIChatClient( api_key=api_key, - endpoint=endpoint, - deployment_name=deployment, + azure_endpoint=endpoint, + model=deployment, api_version=api_version, ) diff --git a/agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py b/agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py index fc96263fc..e5551748c 100644 --- a/agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py +++ b/agentic_ai/agents/mcp_agent_demo/mcp_server_stateful.py @@ -39,7 +39,7 @@ from agent_framework import Agent, tool from agent_framework._sessions import AgentSession -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.openai import OpenAIChatClient from fastmcp import FastMCP from fastmcp.server.context import Context @@ -108,10 +108,10 @@ def main() -> None: print(f"🔧 Deployment : {deployment}") # 1) Create the agent-framework Agent - llm_client = AzureOpenAIChatClient( + llm_client = OpenAIChatClient( api_key=api_key, - endpoint=endpoint, - deployment_name=deployment, + azure_endpoint=endpoint, + model=deployment, api_version=api_version, ) diff --git a/agentic_ai/agents/mcp_agent_demo/pyproject.toml b/agentic_ai/agents/mcp_agent_demo/pyproject.toml index 92ffae34f..25ff7a346 100644 --- a/agentic_ai/agents/mcp_agent_demo/pyproject.toml +++ b/agentic_ai/agents/mcp_agent_demo/pyproject.toml @@ -5,8 +5,7 @@ description = "Demo: Expose agent-framework agent as MCP service, consume it fro readme = "README.md" requires-python = ">=3.12" dependencies = [ - "agent-framework-core>=0.2.0", - "agent-framework-orchestrations>=0.1.0", + "agent-framework==1.2.1", "azure-identity>=1.19.0", "fastmcp>=3.0.0", "langchain-core>=0.3.0", diff --git a/agentic_ai/agents/mcp_agent_demo/uv.lock b/agentic_ai/agents/mcp_agent_demo/uv.lock index f779dbd60..9ae8f3f2c 100644 --- a/agentic_ai/agents/mcp_agent_demo/uv.lock +++ b/agentic_ai/agents/mcp_agent_demo/uv.lock @@ -1,26 +1,362 @@ version = 1 -revision = 1 +revision = 3 requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= 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b/agentic_ai/agents/mcp_agent_demo/workflow_group_chat.py @@ -54,7 +54,7 @@ MCPStreamableHTTPTool, Message, ) -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.openai import OpenAIChatClient from agent_framework.orchestrations import GroupChatBuilder @@ -289,10 +289,10 @@ async def main() -> None: print(f" Session: stateful (server maintains conversation history)") print() - client = AzureOpenAIChatClient( + client = OpenAIChatClient( api_key=api_key, - endpoint=endpoint, - deployment_name=deployment, + azure_endpoint=endpoint, + model=deployment, api_version=api_version, ) diff --git a/agentic_ai/applications/pyproject.toml b/agentic_ai/applications/pyproject.toml index cc51fc58f..2051eec2a 100644 --- a/agentic_ai/applications/pyproject.toml +++ b/agentic_ai/applications/pyproject.toml @@ -5,7 +5,7 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.12" dependencies = [ - "agent-framework==1.0.0rc1", + 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agent-framework-azurefunctions==1.0.0b260130 # via agent-framework-core agent-framework-bedrock==1.0.0b260219 @@ -26,14 +26,14 @@ agent-framework-claude==1.0.0b260219 # via agent-framework-core agent-framework-copilotstudio==1.0.0b251114 # via agent-framework-core -agent-framework-core==1.0.0rc1 +agent-framework-core==1.2.1 # via # agent-framework # agent-framework-a2a # agent-framework-ag-ui # agent-framework-anthropic - # agent-framework-azure-ai # agent-framework-azure-ai-search + # agent-framework-azure-cosmos # agent-framework-azurefunctions # agent-framework-bedrock # agent-framework-chatkit @@ -42,11 +42,13 @@ agent-framework-core==1.0.0rc1 # agent-framework-declarative # agent-framework-devui # agent-framework-durabletask + # agent-framework-foundry # agent-framework-foundry-local # agent-framework-github-copilot # agent-framework-lab # agent-framework-mem0 # agent-framework-ollama + # agent-framework-openai # agent-framework-orchestrations # agent-framework-purview # 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routes to Azure OpenAI) + chat_client = OpenAIChatClient( + azure_endpoint=azure_endpoint, + model=deployment_name, credential=DefaultAzureCredential(), ) # Create agent - agent = ChatAgent( - chat_client=chat_client, + agent = Agent( + client=chat_client, tools=[mcp_tool], name="CustomerServiceAgent", instructions="""You are a helpful customer service agent for Contoso Wireless. @@ -130,7 +130,7 @@ async def run_agent_with_tracing(): "Show me the data usage for subscription 1 from 2025-01-01 to 2025-01-15", ] - thread = agent.get_new_thread() + session = agent.create_session() for query in queries: print(f"\n👤 User: {query}") @@ -143,7 +143,7 @@ async def run_agent_with_tracing(): ) as query_span: query_span.set_attribute("user.query", query) - async for update in agent.run_stream(query, thread=thread): + async for update in agent.run(query, session=session, stream=True): if update.text: print(update.text, end="") print("\n" + "=" * 60) diff --git a/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py b/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py index 475c4a66f..899f2cfc6 100644 --- a/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py +++ b/agentic_ai/workflow/fraud_detection_durable/fraud_analysis_workflow.py @@ -25,7 +25,7 @@ WorkflowContext, MCPStreamableHTTPTool, ) -from agent_framework.azure import AzureOpenAIChatClient +from agent_framework.openai import OpenAIChatClient logger = logging.getLogger(__name__) @@ -190,7 +190,7 @@ async def handle_alert( class UsagePatternExecutor(Executor): """Analyzes data usage patterns using MCP tools.""" - def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient): + def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: OpenAIChatClient): super().__init__(id="usage_pattern_executor") # Filter MCP tools for usage analysis @@ -263,7 +263,7 @@ async def handle_alert( class LocationAnalysisExecutor(Executor): """Analyzes geolocation data for anomalies.""" - def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient): + def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: OpenAIChatClient): super().__init__(id="location_analysis_executor") # Filter MCP tools for location analysis @@ -335,7 +335,7 @@ async def handle_alert( class BillingChargeExecutor(Executor): """Analyzes billing and charge patterns.""" - def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: AzureOpenAIChatClient): + def __init__(self, mcp_tool: MCPStreamableHTTPTool, chat_client: OpenAIChatClient): super().__init__(id="billing_charge_executor") # Filter MCP tools for billing analysis @@ -415,7 +415,7 @@ class FraudRiskAggregatorExecutor(Executor): Receives a list of results from fan-in edges (all 3 specialists at once). """ - def __init__(self, chat_client: AzureOpenAIChatClient): + def __init__(self, chat_client: OpenAIChatClient): super().__init__(id="fraud_risk_aggregator") self._chat_client = chat_client @@ -560,7 +560,7 @@ def safe_tool_calls(tool_calls_list): def create_fraud_analysis_workflow( mcp_tool: MCPStreamableHTTPTool, - chat_client: AzureOpenAIChatClient, + chat_client: OpenAIChatClient, ) -> Any: """ Create the inner fraud analysis workflow. @@ -622,9 +622,9 @@ async def main(): async with mcp_tool: # Initialize chat client - chat_client = AzureOpenAIChatClient( + chat_client = OpenAIChatClient( credential=AzureCliCredential(), - deployment_name=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4o"), + model=os.getenv("AZURE_OPENAI_CHAT_DEPLOYMENT", "gpt-4o"), ) # Create workflow diff --git a/agentic_ai/workflow/fraud_detection_durable/pyproject.toml b/agentic_ai/workflow/fraud_detection_durable/pyproject.toml index febf6b265..1be5e6585 100644 --- 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a/tests/test_agent_framework_rc1_regression.py b/tests/test_agent_framework_1_2_1_regression.py similarity index 65% rename from tests/test_agent_framework_rc1_regression.py rename to tests/test_agent_framework_1_2_1_regression.py index 5fadc597b..2b1af5111 100644 --- a/tests/test_agent_framework_rc1_regression.py +++ b/tests/test_agent_framework_1_2_1_regression.py @@ -1,13 +1,14 @@ """ -Regression tests for agent-framework 1.0.0rc1 upgrade. +Regression tests for agent-framework 1.2.1 upgrade. Tests all agent types (single, handoff, reflection, magentic) to verify: -1. Import compatibility with the new RC1 API +1. Import compatibility with the 1.2.1 API 2. Constructor signatures work correctly -3. Session management (AgentThread → AgentSession migration) -4. Streaming API (run_stream → run(stream=True)) +3. Session management (AgentSession) +4. Streaming API (run(stream=True)) 5. Event processing (unified WorkflowEvent model) -6. MagenticBuilder new constructor-based API +6. MagenticBuilder constructor-based API +7. Native HandoffBuilder integration in the handoff agent """ import asyncio @@ -30,7 +31,7 @@ class TestImportCompatibility: - """Verify all imports resolve correctly against agent-framework 1.0.0rc1.""" + """Verify all imports resolve correctly against agent-framework 1.2.1.""" def test_core_framework_imports(self): """Core agent_framework symbols used across all agents.""" @@ -52,10 +53,18 @@ def test_core_framework_imports(self): assert AgentSession is not None assert ChatOptions is not None - def test_azure_client_import(self): - """AzureOpenAIChatClient import.""" - from agent_framework.azure import AzureOpenAIChatClient - assert AzureOpenAIChatClient is not None + def test_openai_client_import(self): + """OpenAIChatClient is the unified chat client (replaces AzureOpenAIChatClient).""" + from agent_framework.openai import OpenAIChatClient + assert OpenAIChatClient is not None + + def test_legacy_azure_client_removed(self): + """The 1.0.0rc1 AzureOpenAIChatClient is no longer exported by agent_framework.azure.""" + import agent_framework.azure as az + assert not hasattr(az, "AzureOpenAIChatClient"), ( + "AzureOpenAIChatClient was removed in 1.2.x; use " + "agent_framework.openai.OpenAIChatClient with azure_endpoint=... instead." + ) def test_orchestration_imports(self): """Orchestration symbols moved to agent_framework_orchestrations.""" @@ -68,6 +77,20 @@ def test_orchestration_imports(self): ) assert MagenticBuilder is not None + def test_native_handoff_imports(self): + """Native handoff orchestration is available in 1.2.x.""" + from agent_framework_orchestrations import ( + HandoffBuilder, + HandoffAgentUserRequest, + HandoffSentEvent, + HandoffConfiguration, + ) + assert HandoffBuilder is not None + assert HandoffSentEvent is not None + assert HandoffConfiguration is not None + # HandoffAgentUserRequest provides helpers used by chat_async resumption + assert hasattr(HandoffAgentUserRequest, "create_response") + def test_single_agent_import(self): """Single agent module loads cleanly.""" from agents.agent_framework.single_agent import Agent @@ -108,7 +131,7 @@ def test_old_symbols_removed(self): class TestAgentConstructors: - """Verify agent constructors use the new RC1 signatures.""" + """Verify agent constructors use the 1.2.1 signatures.""" def test_framework_agent_constructor_signature(self): """Agent(client, instructions, *, name, tools, default_options).""" @@ -121,6 +144,10 @@ def test_framework_agent_constructor_signature(self): assert 'name' in params assert 'tools' in params assert 'default_options' in params + # 1.2.x adds first-class support for handoff workflows via this flag + assert 'require_per_service_call_history_persistence' in params + # description was wired through in 1.2.x and is consumed by HandoffBuilder + assert 'description' in params def test_chat_options_model_id(self): """ChatOptions accepts model_id for specifying the model.""" @@ -128,17 +155,32 @@ def test_chat_options_model_id(self): opts = ChatOptions(model_id="gpt-4o") assert opts["model_id"] == "gpt-4o" - def test_azure_client_constructor(self): - """AzureOpenAIChatClient accepts deployment_name, endpoint, api_version.""" - from agent_framework.azure import AzureOpenAIChatClient + def test_openai_client_constructor_supports_azure_kwargs(self): + """OpenAIChatClient accepts model, api_key, credential, azure_endpoint, api_version.""" + from agent_framework.openai import OpenAIChatClient import inspect - sig = inspect.signature(AzureOpenAIChatClient.__init__) - params = list(sig.parameters.keys()) + sig = inspect.signature(OpenAIChatClient.__init__) + params = set(sig.parameters.keys()) - {'self'} + # Unified client uses `model` (not `deployment_name`) and `azure_endpoint` (not `endpoint`) + assert 'model' in params assert 'api_key' in params - assert 'deployment_name' in params - assert 'endpoint' in params - assert 'api_version' in params assert 'credential' in params + assert 'azure_endpoint' in params + assert 'api_version' in params + # The legacy 1.0.0rc1 names should not be present anymore + assert 'deployment_name' not in params, "deployment_name was renamed to model in 1.2.x" + + def test_openai_client_routes_to_azure(self): + """Passing azure_endpoint routes the OpenAIChatClient to the Azure backend.""" + from agent_framework.openai import OpenAIChatClient + client = OpenAIChatClient( + model="gpt-4o", + api_key="dummy-key", + azure_endpoint="https://example.openai.azure.com/", + api_version="2024-08-01-preview", + ) + assert client.azure_endpoint == "https://example.openai.azure.com/" + assert client.api_version == "2024-08-01-preview" # ============================================================================= @@ -415,16 +457,22 @@ def test_single_agent_websocket_manager(self): class TestHandoffAgentIntegration: - """Test handoff agent construction and domain configuration.""" + """Test handoff agent construction and native HandoffBuilder integration. + + The handoff agent was rewritten in 1.2.x to use the native + ``HandoffBuilder`` orchestration (no more hand-rolled intent classifier + or regex-based handoff detection). + """ def test_handoff_agent_init(self): - """Handoff agent initializes with domain tracking.""" + """Handoff agent initializes with empty domain agent map and no current domain.""" from agents.agent_framework.multi_agent.handoff_multi_domain_agent import Agent store = {} agent = Agent(state_store=store, session_id="handoff-test") assert agent._current_domain is None assert agent._domain_agents == {} - assert agent._domain_sessions == {} + assert agent._workflow is None + assert agent._initialized is False def test_handoff_agent_restores_domain(self): """Handoff agent restores current domain from state_store.""" @@ -433,17 +481,42 @@ def test_handoff_agent_restores_domain(self): agent = Agent(state_store=store, session_id="handoff-test") assert agent._current_domain == "crm_billing" - def test_handoff_detects_handoff_phrases(self): - """Handoff detection logic works.""" + def test_handoff_uses_native_handoff_builder(self): + """The handoff agent module imports HandoffBuilder from agent_framework_orchestrations.""" + from agents.agent_framework.multi_agent import handoff_multi_domain_agent as mod + from agent_framework_orchestrations import HandoffBuilder, HandoffAgentUserRequest + # The rewritten module should reference the native HandoffBuilder API + assert mod.HandoffBuilder is HandoffBuilder + assert mod.HandoffAgentUserRequest is HandoffAgentUserRequest + + def test_handoff_no_legacy_classifier_attributes(self): + """The 1.0.0rc1 hand-rolled classifier helpers were removed in 1.2.x.""" from agents.agent_framework.multi_agent.handoff_multi_domain_agent import Agent - agent = Agent(state_store={}, session_id="test") - assert agent._detect_handoff_request( - "This is outside my area. Let me connect you with the right specialist." + agent = Agent(state_store={}, session_id="handoff-test") + # These were the old hand-rolled helpers; native HandoffBuilder replaces them + assert not hasattr(agent, "_detect_handoff_request"), ( + "Regex-based handoff detection was removed; HandoffBuilder injects " + "synthetic handoff tools that are intercepted via middleware." + ) + assert not hasattr(agent, "_classify_intent"), ( + "Hand-rolled intent classifier was removed; agents now decide " + "handoffs themselves by calling the auto-generated handoff tools." ) - assert not agent._detect_handoff_request( - "Your billing summary shows $50.00 due." + assert not hasattr(agent, "_domain_sessions"), ( + "Per-domain AgentSessions were removed; the workflow's checkpoint " + "storage manages conversation state across handoffs." ) + def test_handoff_domains_have_descriptions(self): + """Each domain config must have a description (used by HandoffBuilder for tool docs).""" + from agents.agent_framework.multi_agent.handoff_multi_domain_agent import DOMAINS + assert set(DOMAINS) == {"crm_billing", "product_promotions", "security_authentication"} + for domain_id, cfg in DOMAINS.items(): + assert cfg.get("description"), ( + f"Domain '{domain_id}' must have a description; HandoffBuilder uses " + f"it to generate the auto-injected handoff tool description." + ) + class TestReflectionAgentIntegration: """Test reflection agent construction.""" @@ -464,6 +537,28 @@ def test_reflection_approval_detection(self): assert agent._is_approved("APPROVE - looks good") assert not agent._is_approved("Needs improvement on point 3") + def test_reflection_agent_uses_1_2_1_chat_client_kwargs(self): + """The reflection agent must use 1.2.x kwarg names (model, azure_endpoint). + + The chat client is constructed via ``OpenAIChatClient(**client_kwargs)``, + so the kwarg dict keys must match the new API names — this is a + common foot-gun when migrating from 1.0.0rc1. + """ + import inspect + from agents.agent_framework.multi_agent import reflection_agent as mod + src = inspect.getsource(getattr(mod.Agent, "_setup_agents")) + # Old-API key strings should not appear in the setup code path + assert '"deployment_name"' not in src, ( + "reflection_agent passes 'deployment_name' to OpenAIChatClient; " + "rename to 'model' for the 1.2.x API." + ) + # Note: a literal `'endpoint':` could also match the unrelated HTTP endpoint + # field, so we look for the specific Azure key-rename pattern instead. + assert '"endpoint": self.azure_openai_endpoint' not in src, ( + "reflection_agent passes 'endpoint' to OpenAIChatClient; " + "rename to 'azure_endpoint' for the 1.2.x API." + ) + class TestMagenticGroupIntegration: """Test magentic group agent construction.""" @@ -484,6 +579,94 @@ def test_magentic_checkpoint_storage(self): storage = DictCheckpointStorage(backing) assert storage.latest_checkpoint_id is None + def test_magentic_checkpoint_storage_implements_1_2_1_protocol(self): + """DictCheckpointStorage implements the 1.2.x CheckpointStorage protocol. + + In 1.0.0rc1 the methods were named save_checkpoint/load_checkpoint/...; in + 1.2.x they are save/load/delete/get_latest with workflow_name kwargs. + """ + from agents.agent_framework.multi_agent.magentic_group import DictCheckpointStorage + import inspect + for name in ("save", "load", "delete", "get_latest", "list_checkpoints", "list_checkpoint_ids"): + assert callable(getattr(DictCheckpointStorage, name, None)), ( + f"DictCheckpointStorage must implement 1.2.x method {name!r}" + ) + # Old method names should NOT exist + assert not hasattr(DictCheckpointStorage, "save_checkpoint"), \ + "save_checkpoint was renamed to save in 1.2.x" + assert not hasattr(DictCheckpointStorage, "load_checkpoint"), \ + "load_checkpoint was renamed to load in 1.2.x" + # workflow_name keyword-only enforcement + for name in ("list_checkpoints", "list_checkpoint_ids", "get_latest"): + sig = inspect.signature(getattr(DictCheckpointStorage, name)) + assert "workflow_name" in sig.parameters, ( + f"{name} must accept workflow_name keyword in 1.2.x" + ) + + def test_handoff_checkpoint_storage_implements_1_2_1_protocol(self): + """The handoff agent's _DictCheckpointStorage matches the 1.2.x protocol.""" + from agents.agent_framework.multi_agent.handoff_multi_domain_agent import _DictCheckpointStorage + for name in ("save", "load", "delete", "get_latest", "list_checkpoints", "list_checkpoint_ids"): + assert callable(getattr(_DictCheckpointStorage, name, None)), ( + f"_DictCheckpointStorage must implement 1.2.x method {name!r}" + ) + + def test_workflow_checkpoint_uses_workflow_name(self): + """WorkflowCheckpoint uses workflow_name (was workflow_id in 1.0.0rc1).""" + from agent_framework import WorkflowCheckpoint + import inspect + sig = inspect.signature(WorkflowCheckpoint.__init__) + assert "workflow_name" in sig.parameters + assert "workflow_id" not in sig.parameters, \ + "workflow_id was renamed to workflow_name in 1.2.x" + + def test_dict_checkpoint_storage_save_load_roundtrip(self): + """save() persists a checkpoint that load()/get_latest() can recover.""" + from agents.agent_framework.multi_agent.magentic_group import DictCheckpointStorage + from agent_framework import WorkflowCheckpoint + + storage = DictCheckpointStorage({}) + cp = WorkflowCheckpoint(workflow_name="wf-A", graph_signature_hash="hash-1", checkpoint_id="cp-1") + + cid = asyncio.get_event_loop().run_until_complete(storage.save(cp)) + assert cid == "cp-1" + + loaded = asyncio.get_event_loop().run_until_complete(storage.load("cp-1")) + assert loaded is not None + assert loaded.workflow_name == "wf-A" + assert loaded.checkpoint_id == "cp-1" + + latest = asyncio.get_event_loop().run_until_complete(storage.get_latest(workflow_name="wf-A")) + assert latest is not None and latest.checkpoint_id == "cp-1" + + # get_latest with a non-matching workflow_name returns None + none = asyncio.get_event_loop().run_until_complete(storage.get_latest(workflow_name="other")) + assert none is None + + def test_get_latest_checkpoint_id_uses_workflow_name_kwarg(self): + """_get_latest_checkpoint_id resumes from a 1.2.x-protocol-only storage. + + Storages that follow the 1.2.x ``CheckpointStorage`` protocol require + the keyword-only ``workflow_name`` argument on ``get_latest`` / + ``list_checkpoints`` / ``list_checkpoint_ids`` and do not expose the + nonstandard ``latest_checkpoint_id`` convenience. The resume helper + must call those methods with ``workflow_name=`` so it can still find + the latest checkpoint. + """ + from agents.agent_framework.multi_agent.magentic_group import Agent, DictCheckpointStorage + from agent_framework import WorkflowCheckpoint + + # Back the storage with a dict that records the workflow_name so + # _workflow_name_for_storage can recover it (mirrors how save() works). + backing: dict = {"workflow_name": "wf-resume"} + storage = DictCheckpointStorage(backing) + cp = WorkflowCheckpoint(workflow_name="wf-resume", graph_signature_hash="h", checkpoint_id="cp-latest") + asyncio.get_event_loop().run_until_complete(storage.save(cp)) + + agent = Agent(state_store={}, session_id="test") + latest_id = asyncio.get_event_loop().run_until_complete(agent._get_latest_checkpoint_id(storage)) + assert latest_id == "cp-latest" + def test_magentic_sanitize_final_answer(self): """FINAL_ANSWER prefix is stripped from workflow output.""" from agents.agent_framework.multi_agent.magentic_group import Agent @@ -555,17 +738,17 @@ def test_magentic_process_data_event(self): class TestFrameworkVersion: - """Verify we're running against the expected RC1 version.""" + """Verify we're running against the expected 1.2.x version.""" def test_agent_framework_version(self): - """agent_framework is at 1.0.0rc1.""" + """agent_framework is at 1.2.1.""" import agent_framework - assert agent_framework.__version__ == "1.0.0rc1", \ - f"Expected 1.0.0rc1, got {agent_framework.__version__}" + assert agent_framework.__version__ == "1.2.1", \ + f"Expected 1.2.1, got {agent_framework.__version__}" def test_agent_framework_core_installed(self): - """agent-framework-core is installed as a dependency.""" + """agent-framework-core is installed at 1.2.1 as a dependency.""" import importlib.metadata version = importlib.metadata.version('agent-framework-core') - assert version == "1.0.0rc1", \ - f"Expected 1.0.0rc1, got {version}" + assert version == "1.2.1", \ + f"Expected 1.2.1, got {version}" From 931ca05acc9a7e30f4d5145f7a4320bb4234f958 Mon Sep 17 00:00:00 2001 From: Eric Hansen Date: Tue, 28 Apr 2026 12:19:33 -0500 Subject: [PATCH 35/37] docs: Remove misleading RequestInfoExecutor references (#418) doc update --- agentic_ai/agents/agent_framework/STATE_MANAGEMENT.md | 2 +- .../agents/agent_framework/multi_agent/MAGENTIC_README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/agentic_ai/agents/agent_framework/STATE_MANAGEMENT.md b/agentic_ai/agents/agent_framework/STATE_MANAGEMENT.md index f58c4be8b..c4b1d2923 100644 --- a/agentic_ai/agents/agent_framework/STATE_MANAGEMENT.md +++ b/agentic_ai/agents/agent_framework/STATE_MANAGEMENT.md @@ -408,7 +408,7 @@ class SessionMemoryCheckpointStorage(CheckpointStorage): 2. **Team agent** - `MagenticOrchestratorExecutor` automatically snapshots each participant’s `chat_history`. - - Checkpoints include outstanding tool requests (`RequestInfoExecutor` state). + - Checkpoints include outstanding tool requests and plan-review state managed by the orchestrator via `WorkflowEvent(type="request_info")`. - Ensure all custom executors implement `restore_state` to interpret snapshots after schema changes. 3. **Human-in-the-loop** diff --git a/agentic_ai/agents/agent_framework/multi_agent/MAGENTIC_README.md b/agentic_ai/agents/agent_framework/multi_agent/MAGENTIC_README.md index a6641d92c..5f997c725 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/MAGENTIC_README.md +++ b/agentic_ai/agents/agent_framework/multi_agent/MAGENTIC_README.md @@ -128,7 +128,7 @@ When stalling, the manager calls `replan()`, which uses [`ORCHESTRATOR_TASK_LEDG 1. Registers **participants** (`participants()`), each either a `BaseAgent` or another `Executor`. 2. Configures the **manager** (`with_standard_manager()`). 3. Optionally enables **plan review**, **checkpointing**, and **unified callbacks**. -4. Builds a [`Workflow`](reference/agent-framework/python/packages/main/agent_framework/_workflow/_workflow.py) graph: orchestrator (start node), optional `RequestInfoExecutor` for plan review, and bidirectional edges to each agent executor. +4. Builds a [`Workflow`](reference/agent-framework/python/packages/main/agent_framework/_workflow/_workflow.py) graph: orchestrator (start node) with optional plan review enabled via `enable_plan_review=True`, and bidirectional edges to each agent executor. Plan review is handled internally by the orchestrator, which emits `WorkflowEvent(type="request_info")` events containing `MagenticPlanReviewRequest` data. ### 4.2 Workflow Context & State From a3a503563355cbab1014ff51c7c7c9bd327884c4 Mon Sep 17 00:00:00 2001 From: Copilot <198982749+Copilot@users.noreply.github.com> Date: Tue, 28 Apr 2026 10:58:08 -0700 Subject: [PATCH 36/37] =?UTF-8?q?Fix=20backend=20500s=20by=20renaming=20Ch?= =?UTF-8?q?atOptions(model=5Fid=3D=E2=80=A6)=20to=20ChatOptions(model=3D?= =?UTF-8?q?=E2=80=A6)=20for=20agent-framework=201.2.1=20(#421)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Promote: int-agentic → main (production) (#414) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * Updating Durable Agent Implementation (#404) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme --------- Co-authored-by: James N. * chore: reduce anomaly probability to 1% for controlled demo pace * Reduce anomaly probability to 1% for controlled demo pace (#406) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * chore: reduce anomaly probability to 1% for controlled demo pace --------- Co-authored-by: James N. * add mcp_agent_demo * Add hybrid MCP server (strict-schema + natural-language tools) and typed-contract workflow Scripts 6-8 for the MCP agent demo: - workflow_typed_contracts.py: 4-agent IT security incident response pipeline with Pydantic-enforced contracts at every boundary (SecurityAlert, ThreatAssessment, ImpactAnalysis, IncidentResponse) - mcp_server_hybrid.py: Single MCP endpoint exposing BOTH tool types: * Strict-schema: triage_alert, assess_threat, create_response (Pydantic) * Natural-language: ask_security_advisor, explain_for_customer (prose) * Shared session state across all tools - mcp_client_hybrid.py: 5-step incident flow using both tool types with explicit context passing between steps - README.md: Updated from 6 to 8 capabilities with Hybrid Mode architecture diagram and Quick Start sections 7-8 * Replace workflow_local_remote.py with simplified proxy agent (Script 3) - Delete workflow_local_remote.py (old Script 3 with extra LLM call) - Add workflow_proxy_agent.py: MCPProxyAgent (BaseAgent, no LLM) calls MCP tools directly via call_tool() zero local LLM overhead - Simplify MCPProxyAgent: extract common _call() method, module-level _last_text() helper, remove verbose static methods - Fix workflow_typed_contracts.py: add async with context managers - Update README: merge Script 9 into Script 3 position, update architecture diagram, Quick Start, How It Works sections * Clean up MCP demos: remove comparison commentary, drop typed-contracts - workflow_proxy_agent.py: remove 'traditional vs proxy' framing MCPProxyAgent is the standard approach for remote agent integration - Delete workflow_typed_contracts.py: not relevant to MCP integration - README: 7 scripts, renumber hybrid 6-7, remove typed-contract sections * Add LangGraph + MAF GroupChat cross-framework demo (Scripts 8-9) - mcp_server_langgraph.py: LangGraph ReAct agent exposed as MCP server on port 8003 with architecture tools (pattern eval, migration estimate, tech stack recommendation) - workflow_group_chat.py: MAF GroupChatBuilder orchestration with LLM planner routing between local BusinessStrategist (MAF) and remote TechnicalArchitect (LangGraph via MCP) - pyproject.toml: add langgraph, langchain-openai, langchain-core deps - README.md: update to 9 scripts, add cross-framework architecture diagram, Quick Start sections, and dependency table entries * MCP agent demo: rewrite README as MCP-vs-A2A thesis, delete proxy agent, finalize group chat with inline Planner * README: Mermaid diagrams, professional tone, remove LinkedIn teasing language * README: reframe around multi-framework interop problem, two design patterns (Agent-as-Tool + Agent Adapter), add conceptual architecture * README: fix Mermaid diagrams - use br tags instead of \n for line breaks * README: fix A2A comparison table - elicitation supported, structured schemas not * Upgrade agent-framework to 1.0.0rc1 and fastmcp to 3.0.2 Breaking changes migrated: - agent-framework 1.0.0rc1: ChatAgent->Agent, AgentThread->AgentSession, run_stream->run(stream=True), model->default_options(ChatOptions), WorkflowOutputEvent/AgentRunEvent/RequestInfoEvent->unified WorkflowEvent, MagenticBuilder now uses constructor kwargs, orchestrations moved to agent_framework_orchestrations package - fastmcp 3.0.2: removed mcp version pin (now pulled by fastmcp), no code changes needed (fully compatible) Files updated: - agentic_ai/agents/agent_framework/single_agent.py - agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py - agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py - agentic_ai/agents/agent_framework/multi_agent/magentic_group.py - agentic_ai/applications/pyproject.toml (agent-framework==1.0.0rc1) - agentic_ai/workflow/fraud_detection_durable/pyproject.toml (agent-framework==1.0.0rc1) - mcp/pyproject.toml (fastmcp==3.0.2) - tests/test_agent_framework_rc1_regression.py (51 regression tests, all passing) * Fix CI: regenerate requirements.txt with agent-framework-core==1.0.0rc1 The Docker build uses requirements.txt (not pyproject.toml/uv.lock), and it still had agent-framework-core==1.0.0b260130 pinned, which conflicts with agent-framework==1.0.0rc1 requiring agent-framework-core==1.0.0rc1. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22315210980 * Fix: use GH_PAT for auto-merge to trigger downstream workflows Pushes made by GITHUB_TOKEN don't trigger other workflows (GitHub Actions limitation to prevent infinite loops). This meant the auto-merge into int-agentic never triggered promote-to-main.yml. Now uses secrets.GH_PAT with fallback to GITHUB_TOKEN so: 1. If GH_PAT is configured: merge triggers promote-to-main.yml 2. If not: merge still works, but promotion PR must be created manually * Add workflow_dispatch trigger to promote-to-main Allows manual triggering when auto-trigger is missed (e.g. after GITHUB_TOKEN-based merges that don't fire downstream workflows). * Fix: Cosmos DB auth + remove phantom reflection_workflow_agent 1. MCP Cosmos DB: Replace AzureCliCredential (not available in containers) with ManagedIdentityCredential (when AZURE_CLIENT_ID is set) or DefaultAzureCredential as fallback. This fixes the 'Azure CLI not found on path' error in production Container Apps. 2. Backend: Remove reflection_workflow_agent from DEFAULT_AVAILABLE_AGENTS and descriptions - the module file doesn't exist, causing it to show as a broken option in the UI agent selector. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22321497108 * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Trigger rebuild: deploy Cosmos DB auth fix and agent list cleanup * Fix: iterate ResponseStream directly, not .updates ResponseStream is AsyncIterable - iterate with 'async for chunk in stream:' The .updates property returns a Sequence (list), not an async iterator. This caused TypeError: 'async for' requires __aiter__, got list. Not caught in local tests because unit tests mocked the streaming and the regression tests verified API signatures but didn't run live agents. * Fix: make integration test failures block the pipeline Removed continue-on-error: true from the pytest step so test failures actually fail the workflow. Previously, 5/7 tests could fail and the pipeline would still report success and proceed to auto-merge. * Fix: use SHA-tagged images for container deployment, not :latest Container Apps don't create a new revision when the image tag is unchanged. Using :latest meant the old container kept running even after a new image was pushed. Now passes github.sha as the image tag, which forces a new revision on every deploy. This was the root cause of the ResponseStream.updates bug reaching production - the integration tests ran against old containers. * Skip MCP localhost check in CI mode In CI, the evaluation talks to the backend via HTTP which connects to the deployed MCP container internally. The localhost:8000 check is only useful for local dev and was producing a confusing warning in CI logs. * Fix: auto-merge creates PR if none exists Previously auto-merge only looked for an existing open PR and skipped if none was found. Now it auto-creates a PR from the dev branch to int-agentic if one doesn't exist, then merges it. This ensures every successful pipeline run promotes code to int-agentic regardless of whether a PR was manually created beforehand. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22330447642 --------- Co-authored-by: James N. * Promote: int-agentic → main (production) (#416) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * Updating Durable Agent Implementation (#404) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme --------- Co-authored-by: James N. * chore: reduce anomaly probability to 1% for controlled demo pace * Reduce anomaly probability to 1% for controlled demo pace (#406) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * chore: reduce anomaly probability to 1% for controlled demo pace --------- Co-authored-by: James N. * add mcp_agent_demo * Add hybrid MCP server (strict-schema + natural-language tools) and typed-contract workflow Scripts 6-8 for the MCP agent demo: - workflow_typed_contracts.py: 4-agent IT security incident response pipeline with Pydantic-enforced contracts at every boundary (SecurityAlert, ThreatAssessment, ImpactAnalysis, IncidentResponse) - mcp_server_hybrid.py: Single MCP endpoint exposing BOTH tool types: * Strict-schema: triage_alert, assess_threat, create_response (Pydantic) * Natural-language: ask_security_advisor, explain_for_customer (prose) * Shared session state across all tools - mcp_client_hybrid.py: 5-step incident flow using both tool types with explicit context passing between steps - README.md: Updated from 6 to 8 capabilities with Hybrid Mode architecture diagram and Quick Start sections 7-8 * Replace workflow_local_remote.py with simplified proxy agent (Script 3) - Delete workflow_local_remote.py (old Script 3 with extra LLM call) - Add workflow_proxy_agent.py: MCPProxyAgent (BaseAgent, no LLM) calls MCP tools directly via call_tool() zero local LLM overhead - Simplify MCPProxyAgent: extract common _call() method, module-level _last_text() helper, remove verbose static methods - Fix workflow_typed_contracts.py: add async with context managers - Update README: merge Script 9 into Script 3 position, update architecture diagram, Quick Start, How It Works sections * Clean up MCP demos: remove comparison commentary, drop typed-contracts - workflow_proxy_agent.py: remove 'traditional vs proxy' framing MCPProxyAgent is the standard approach for remote agent integration - Delete workflow_typed_contracts.py: not relevant to MCP integration - README: 7 scripts, renumber hybrid 6-7, remove typed-contract sections * Add LangGraph + MAF GroupChat cross-framework demo (Scripts 8-9) - mcp_server_langgraph.py: LangGraph ReAct agent exposed as MCP server on port 8003 with architecture tools (pattern eval, migration estimate, tech stack recommendation) - workflow_group_chat.py: MAF GroupChatBuilder orchestration with LLM planner routing between local BusinessStrategist (MAF) and remote TechnicalArchitect (LangGraph via MCP) - pyproject.toml: add langgraph, langchain-openai, langchain-core deps - README.md: update to 9 scripts, add cross-framework architecture diagram, Quick Start sections, and dependency table entries * MCP agent demo: rewrite README as MCP-vs-A2A thesis, delete proxy agent, finalize group chat with inline Planner * README: Mermaid diagrams, professional tone, remove LinkedIn teasing language * README: reframe around multi-framework interop problem, two design patterns (Agent-as-Tool + Agent Adapter), add conceptual architecture * README: fix Mermaid diagrams - use br tags instead of \n for line breaks * README: fix A2A comparison table - elicitation supported, structured schemas not * Upgrade agent-framework to 1.0.0rc1 and fastmcp to 3.0.2 Breaking changes migrated: - agent-framework 1.0.0rc1: ChatAgent->Agent, AgentThread->AgentSession, run_stream->run(stream=True), model->default_options(ChatOptions), WorkflowOutputEvent/AgentRunEvent/RequestInfoEvent->unified WorkflowEvent, MagenticBuilder now uses constructor kwargs, orchestrations moved to agent_framework_orchestrations package - fastmcp 3.0.2: removed mcp version pin (now pulled by fastmcp), no code changes needed (fully compatible) Files updated: - agentic_ai/agents/agent_framework/single_agent.py - agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py - agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py - agentic_ai/agents/agent_framework/multi_agent/magentic_group.py - agentic_ai/applications/pyproject.toml (agent-framework==1.0.0rc1) - agentic_ai/workflow/fraud_detection_durable/pyproject.toml (agent-framework==1.0.0rc1) - mcp/pyproject.toml (fastmcp==3.0.2) - tests/test_agent_framework_rc1_regression.py (51 regression tests, all passing) * Fix CI: regenerate requirements.txt with agent-framework-core==1.0.0rc1 The Docker build uses requirements.txt (not pyproject.toml/uv.lock), and it still had agent-framework-core==1.0.0b260130 pinned, which conflicts with agent-framework==1.0.0rc1 requiring agent-framework-core==1.0.0rc1. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22315210980 * Fix: use GH_PAT for auto-merge to trigger downstream workflows Pushes made by GITHUB_TOKEN don't trigger other workflows (GitHub Actions limitation to prevent infinite loops). This meant the auto-merge into int-agentic never triggered promote-to-main.yml. Now uses secrets.GH_PAT with fallback to GITHUB_TOKEN so: 1. If GH_PAT is configured: merge triggers promote-to-main.yml 2. If not: merge still works, but promotion PR must be created manually * Add workflow_dispatch trigger to promote-to-main Allows manual triggering when auto-trigger is missed (e.g. after GITHUB_TOKEN-based merges that don't fire downstream workflows). * Fix: Cosmos DB auth + remove phantom reflection_workflow_agent 1. MCP Cosmos DB: Replace AzureCliCredential (not available in containers) with ManagedIdentityCredential (when AZURE_CLIENT_ID is set) or DefaultAzureCredential as fallback. This fixes the 'Azure CLI not found on path' error in production Container Apps. 2. Backend: Remove reflection_workflow_agent from DEFAULT_AVAILABLE_AGENTS and descriptions - the module file doesn't exist, causing it to show as a broken option in the UI agent selector. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22321497108 * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Trigger rebuild: deploy Cosmos DB auth fix and agent list cleanup * Fix: iterate ResponseStream directly, not .updates ResponseStream is AsyncIterable - iterate with 'async for chunk in stream:' The .updates property returns a Sequence (list), not an async iterator. This caused TypeError: 'async for' requires __aiter__, got list. Not caught in local tests because unit tests mocked the streaming and the regression tests verified API signatures but didn't run live agents. * Fix: make integration test failures block the pipeline Removed continue-on-error: true from the pytest step so test failures actually fail the workflow. Previously, 5/7 tests could fail and the pipeline would still report success and proceed to auto-merge. * Fix: use SHA-tagged images for container deployment, not :latest Container Apps don't create a new revision when the image tag is unchanged. Using :latest meant the old container kept running even after a new image was pushed. Now passes github.sha as the image tag, which forces a new revision on every deploy. This was the root cause of the ResponseStream.updates bug reaching production - the integration tests ran against old containers. * Skip MCP localhost check in CI mode In CI, the evaluation talks to the backend via HTTP which connects to the deployed MCP container internally. The localhost:8000 check is only useful for local dev and was producing a confusing warning in CI logs. * Fix: auto-merge creates PR if none exists Previously auto-merge only looked for an existing open PR and skipped if none was found. Now it auto-creates a PR from the dev branch to int-agentic if one doesn't exist, then merges it. This ensures every successful pipeline run promotes code to int-agentic regardless of whether a PR was manually created beforehand. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22330447642 * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22334055353 --------- Co-authored-by: James N. * Promote: int-agentic → main (production) (#420) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * Updating Durable Agent Implementation (#404) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme --------- Co-authored-by: James N. * chore: reduce anomaly probability to 1% for controlled demo pace * Reduce anomaly probability to 1% for controlled demo pace (#406) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * chore: reduce anomaly probability to 1% for controlled demo pace --------- Co-authored-by: James N. * add mcp_agent_demo * Add hybrid MCP server (strict-schema + natural-language tools) and typed-contract workflow Scripts 6-8 for the MCP agent demo: - workflow_typed_contracts.py: 4-agent IT security incident response pipeline with Pydantic-enforced contracts at every boundary (SecurityAlert, ThreatAssessment, ImpactAnalysis, IncidentResponse) - mcp_server_hybrid.py: Single MCP endpoint exposing BOTH tool types: * Strict-schema: triage_alert, assess_threat, create_response (Pydantic) * Natural-language: ask_security_advisor, explain_for_customer (prose) * Shared session state across all tools - mcp_client_hybrid.py: 5-step incident flow using both tool types with explicit context passing between steps - README.md: Updated from 6 to 8 capabilities with Hybrid Mode architecture diagram and Quick Start sections 7-8 * Replace workflow_local_remote.py with simplified proxy agent (Script 3) - Delete workflow_local_remote.py (old Script 3 with extra LLM call) - Add workflow_proxy_agent.py: MCPProxyAgent (BaseAgent, no LLM) calls MCP tools directly via call_tool() zero local LLM overhead - Simplify MCPProxyAgent: extract common _call() method, module-level _last_text() helper, remove verbose static methods - Fix workflow_typed_contracts.py: add async with context managers - Update README: merge Script 9 into Script 3 position, update architecture diagram, Quick Start, How It Works sections * Clean up MCP demos: remove comparison commentary, drop typed-contracts - workflow_proxy_agent.py: remove 'traditional vs proxy' framing MCPProxyAgent is the standard approach for remote agent integration - Delete workflow_typed_contracts.py: not relevant to MCP integration - README: 7 scripts, renumber hybrid 6-7, remove typed-contract sections * Add LangGraph + MAF GroupChat cross-framework demo (Scripts 8-9) - mcp_server_langgraph.py: LangGraph ReAct agent exposed as MCP server on port 8003 with architecture tools (pattern eval, migration estimate, tech stack recommendation) - workflow_group_chat.py: MAF GroupChatBuilder orchestration with LLM planner routing between local BusinessStrategist (MAF) and remote TechnicalArchitect (LangGraph via MCP) - pyproject.toml: add langgraph, langchain-openai, langchain-core deps - README.md: update to 9 scripts, add cross-framework architecture diagram, Quick Start sections, and dependency table entries * MCP agent demo: rewrite README as MCP-vs-A2A thesis, delete proxy agent, finalize group chat with inline Planner * README: Mermaid diagrams, professional tone, remove LinkedIn teasing language * README: reframe around multi-framework interop problem, two design patterns (Agent-as-Tool + Agent Adapter), add conceptual architecture * README: fix Mermaid diagrams - use br tags instead of \n for line breaks * README: fix A2A comparison table - elicitation supported, structured schemas not * Upgrade agent-framework to 1.0.0rc1 and fastmcp to 3.0.2 Breaking changes migrated: - agent-framework 1.0.0rc1: ChatAgent->Agent, AgentThread->AgentSession, run_stream->run(stream=True), model->default_options(ChatOptions), WorkflowOutputEvent/AgentRunEvent/RequestInfoEvent->unified WorkflowEvent, MagenticBuilder now uses constructor kwargs, orchestrations moved to agent_framework_orchestrations package - fastmcp 3.0.2: removed mcp version pin (now pulled by fastmcp), no code changes needed (fully compatible) Files updated: - agentic_ai/agents/agent_framework/single_agent.py - agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py - agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py - agentic_ai/agents/agent_framework/multi_agent/magentic_group.py - agentic_ai/applications/pyproject.toml (agent-framework==1.0.0rc1) - agentic_ai/workflow/fraud_detection_durable/pyproject.toml (agent-framework==1.0.0rc1) - mcp/pyproject.toml (fastmcp==3.0.2) - tests/test_agent_framework_rc1_regression.py (51 regression tests, all passing) * Fix CI: regenerate requirements.txt with agent-framework-core==1.0.0rc1 The Docker build uses requirements.txt (not pyproject.toml/uv.lock), and it still had agent-framework-core==1.0.0b260130 pinned, which conflicts with agent-framework==1.0.0rc1 requiring agent-framework-core==1.0.0rc1. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22315210980 * Fix: use GH_PAT for auto-merge to trigger downstream workflows Pushes made by GITHUB_TOKEN don't trigger other workflows (GitHub Actions limitation to prevent infinite loops). This meant the auto-merge into int-agentic never triggered promote-to-main.yml. Now uses secrets.GH_PAT with fallback to GITHUB_TOKEN so: 1. If GH_PAT is configured: merge triggers promote-to-main.yml 2. If not: merge still works, but promotion PR must be created manually * Add workflow_dispatch trigger to promote-to-main Allows manual triggering when auto-trigger is missed (e.g. after GITHUB_TOKEN-based merges that don't fire downstream workflows). * Fix: Cosmos DB auth + remove phantom reflection_workflow_agent 1. MCP Cosmos DB: Replace AzureCliCredential (not available in containers) with ManagedIdentityCredential (when AZURE_CLIENT_ID is set) or DefaultAzureCredential as fallback. This fixes the 'Azure CLI not found on path' error in production Container Apps. 2. Backend: Remove reflection_workflow_agent from DEFAULT_AVAILABLE_AGENTS and descriptions - the module file doesn't exist, causing it to show as a broken option in the UI agent selector. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22321497108 * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Trigger rebuild: deploy Cosmos DB auth fix and agent list cleanup * Fix: iterate ResponseStream directly, not .updates ResponseStream is AsyncIterable - iterate with 'async for chunk in stream:' The .updates property returns a Sequence (list), not an async iterator. This caused TypeError: 'async for' requires __aiter__, got list. Not caught in local tests because unit tests mocked the streaming and the regression tests verified API signatures but didn't run live agents. * Fix: make integration test failures block the pipeline Removed continue-on-error: true from the pytest step so test failures actually fail the workflow. Previously, 5/7 tests could fail and the pipeline would still report success and proceed to auto-merge. * Fix: use SHA-tagged images for container deployment, not :latest Container Apps don't create a new revision when the image tag is unchanged. Using :latest meant the old container kept running even after a new image was pushed. Now passes github.sha as the image tag, which forces a new revision on every deploy. This was the root cause of the ResponseStream.updates bug reaching production - the integration tests ran against old containers. * Skip MCP localhost check in CI mode In CI, the evaluation talks to the backend via HTTP which connects to the deployed MCP container internally. The localhost:8000 check is only useful for local dev and was producing a confusing warning in CI logs. * Fix: auto-merge creates PR if none exists Previously auto-merge only looked for an existing open PR and skipped if none was found. Now it auto-creates a PR from the dev branch to int-agentic if one doesn't exist, then merges it. This ensures every successful pipeline run promotes code to int-agentic regardless of whether a PR was manually created beforehand. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22330447642 * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22334055353 * Migrate workshop to agent-framework==1.2.1 (incl. native HandoffBuilder) (#419) * Promote: int-agentic → main (production) (#414) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * Updating Durable Agent Implementation (#404) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme --------- Co-authored-by: James N. * chore: reduce anomaly probability to 1% for controlled demo pace * Reduce anomaly probability to 1% for controlled demo pace (#406) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * chore: reduce anomaly probability to 1% for controlled demo pace --------- Co-authored-by: James N. * add mcp_agent_demo * Add hybrid MCP server (strict-schema + natural-language tools) and typed-contract workflow Scripts 6-8 for the MCP agent demo: - workflow_typed_contracts.py: 4-agent IT security incident response pipeline with Pydantic-enforced contracts at every boundary (SecurityAlert, ThreatAssessment, ImpactAnalysis, IncidentResponse) - mcp_server_hybrid.py: Single MCP endpoint exposing BOTH tool types: * Strict-schema: triage_alert, assess_threat, create_response (Pydantic) * Natural-language: ask_security_advisor, explain_for_customer (prose) * Shared session state across all tools - mcp_client_hybrid.py: 5-step incident flow using both tool types with explicit context passing between steps - README.md: Updated from 6 to 8 capabilities with Hybrid Mode architecture diagram and Quick Start sections 7-8 * Replace workflow_local_remote.py with simplified proxy agent (Script 3) - Delete workflow_local_remote.py (old Script 3 with extra LLM call) - Add workflow_proxy_agent.py: MCPProxyAgent (BaseAgent, no LLM) calls MCP tools directly via call_tool() zero local LLM overhead - Simplify MCPProxyAgent: extract common _call() method, module-level _last_text() helper, remove verbose static methods - Fix workflow_typed_contracts.py: add async with context managers - Update README: merge Script 9 into Script 3 position, update architecture diagram, Quick Start, How It Works sections * Clean up MCP demos: remove comparison commentary, drop typed-contracts - workflow_proxy_agent.py: remove 'traditional vs proxy' framing MCPProxyAgent is the standard approach for remote agent integration - Delete workflow_typed_contracts.py: not relevant to MCP integration - README: 7 scripts, renumber hybrid 6-7, remove typed-contract sections * Add LangGraph + MAF GroupChat cross-framework demo (Scripts 8-9) - mcp_server_langgraph.py: LangGraph ReAct agent exposed as MCP server on port 8003 with architecture tools (pattern eval, migration estimate, tech stack recommendation) - workflow_group_chat.py: MAF GroupChatBuilder orchestration with LLM planner routing between local BusinessStrategist (MAF) and remote TechnicalArchitect (LangGraph via MCP) - pyproject.toml: add langgraph, langchain-openai, langchain-core deps - README.md: update to 9 scripts, add cross-framework architecture diagram, Quick Start sections, and dependency table entries * MCP agent demo: rewrite README as MCP-vs-A2A thesis, delete proxy agent, finalize group chat with inline Planner * README: Mermaid diagrams, professional tone, remove LinkedIn teasing language * README: reframe around multi-framework interop problem, two design patterns (Agent-as-Tool + Agent Adapter), add conceptual architecture * README: fix Mermaid diagrams - use br tags instead of \n for line breaks * README: fix A2A comparison table - elicitation supported, structured schemas not * Upgrade agent-framework to 1.0.0rc1 and fastmcp to 3.0.2 Breaking changes migrated: - agent-framework 1.0.0rc1: ChatAgent->Agent, AgentThread->AgentSession, run_stream->run(stream=True), model->default_options(ChatOptions), WorkflowOutputEvent/AgentRunEvent/RequestInfoEvent->unified WorkflowEvent, MagenticBuilder now uses constructor kwargs, orchestrations moved to agent_framework_orchestrations package - fastmcp 3.0.2: removed mcp version pin (now pulled by fastmcp), no code changes needed (fully compatible) Files updated: - agentic_ai/agents/agent_framework/single_agent.py - agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py - agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py - agentic_ai/agents/agent_framework/multi_agent/magentic_group.py - agentic_ai/applications/pyproject.toml (agent-framework==1.0.0rc1) - agentic_ai/workflow/fraud_detection_durable/pyproject.toml (agent-framework==1.0.0rc1) - mcp/pyproject.toml (fastmcp==3.0.2) - tests/test_agent_framework_rc1_regression.py (51 regression tests, all passing) * Fix CI: regenerate requirements.txt with agent-framework-core==1.0.0rc1 The Docker build uses requirements.txt (not pyproject.toml/uv.lock), and it still had agent-framework-core==1.0.0b260130 pinned, which conflicts with agent-framework==1.0.0rc1 requiring agent-framework-core==1.0.0rc1. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22315210980 * Fix: use GH_PAT for auto-merge to trigger downstream workflows Pushes made by GITHUB_TOKEN don't trigger other workflows (GitHub Actions limitation to prevent infinite loops). This meant the auto-merge into int-agentic never triggered promote-to-main.yml. Now uses secrets.GH_PAT with fallback to GITHUB_TOKEN so: 1. If GH_PAT is configured: merge triggers promote-to-main.yml 2. If not: merge still works, but promotion PR must be created manually * Add workflow_dispatch trigger to promote-to-main Allows manual triggering when auto-trigger is missed (e.g. after GITHUB_TOKEN-based merges that don't fire downstream workflows). * Fix: Cosmos DB auth + remove phantom reflection_workflow_agent 1. MCP Cosmos DB: Replace AzureCliCredential (not available in containers) with ManagedIdentityCredential (when AZURE_CLIENT_ID is set) or DefaultAzureCredential as fallback. This fixes the 'Azure CLI not found on path' error in production Container Apps. 2. Backend: Remove reflection_workflow_agent from DEFAULT_AVAILABLE_AGENTS and descriptions - the module file doesn't exist, causing it to show as a broken option in the UI agent selector. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22321497108 * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Trigger rebuild: deploy Cosmos DB auth fix and agent list cleanup * Fix: iterate ResponseStream directly, not .updates ResponseStream is AsyncIterable - iterate with 'async for chunk in stream:' The .updates property returns a Sequence (list), not an async iterator. This caused TypeError: 'async for' requires __aiter__, got list. Not caught in local tests because unit tests mocked the streaming and the regression tests verified API signatures but didn't run live agents. * Fix: make integration test failures block the pipeline Removed continue-on-error: true from the pytest step so test failures actually fail the workflow. Previously, 5/7 tests could fail and the pipeline would still report success and proceed to auto-merge. * Fix: use SHA-tagged images for container deployment, not :latest Container Apps don't create a new revision when the image tag is unchanged. Using :latest meant the old container kept running even after a new image was pushed. Now passes github.sha as the image tag, which forces a new revision on every deploy. This was the root cause of the ResponseStream.updates bug reaching production - the integration tests ran against old containers. * Skip MCP localhost check in CI mode In CI, the evaluation talks to the backend via HTTP which connects to the deployed MCP container internally. The localhost:8000 check is only useful for local dev and was producing a confusing warning in CI logs. * Fix: auto-merge creates PR if none exists Previously auto-merge only looked for an existing open PR and skipped if none was found. Now it auto-creates a PR from the dev branch to int-agentic if one doesn't exist, then merges it. This ensures every successful pipeline run promotes code to int-agentic regardless of whether a PR was manually created beforehand. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22330447642 --------- Co-authored-by: James N. * Promote: int-agentic → main (production) (#416) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * Updating Durable Agent Implementation (#404) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme --------- Co-authored-by: James N. * chore: reduce anomaly probability to 1% for controlled demo pace * Reduce anomaly probability to 1% for controlled demo pace (#406) * update fraud_detection_durable to feb 12 agent-framework * enhance fraud detection durable * update readme * chore: reduce anomaly probability to 1% for controlled demo pace --------- Co-authored-by: James N. * add mcp_agent_demo * Add hybrid MCP server (strict-schema + natural-language tools) and typed-contract workflow Scripts 6-8 for the MCP agent demo: - workflow_typed_contracts.py: 4-agent IT security incident response pipeline with Pydantic-enforced contracts at every boundary (SecurityAlert, ThreatAssessment, ImpactAnalysis, IncidentResponse) - mcp_server_hybrid.py: Single MCP endpoint exposing BOTH tool types: * Strict-schema: triage_alert, assess_threat, create_response (Pydantic) * Natural-language: ask_security_advisor, explain_for_customer (prose) * Shared session state across all tools - mcp_client_hybrid.py: 5-step incident flow using both tool types with explicit context passing between steps - README.md: Updated from 6 to 8 capabilities with Hybrid Mode architecture diagram and Quick Start sections 7-8 * Replace workflow_local_remote.py with simplified proxy agent (Script 3) - Delete workflow_local_remote.py (old Script 3 with extra LLM call) - Add workflow_proxy_agent.py: MCPProxyAgent (BaseAgent, no LLM) calls MCP tools directly via call_tool() zero local LLM overhead - Simplify MCPProxyAgent: extract common _call() method, module-level _last_text() helper, remove verbose static methods - Fix workflow_typed_contracts.py: add async with context managers - Update README: merge Script 9 into Script 3 position, update architecture diagram, Quick Start, How It Works sections * Clean up MCP demos: remove comparison commentary, drop typed-contracts - workflow_proxy_agent.py: remove 'traditional vs proxy' framing MCPProxyAgent is the standard approach for remote agent integration - Delete workflow_typed_contracts.py: not relevant to MCP integration - README: 7 scripts, renumber hybrid 6-7, remove typed-contract sections * Add LangGraph + MAF GroupChat cross-framework demo (Scripts 8-9) - mcp_server_langgraph.py: LangGraph ReAct agent exposed as MCP server on port 8003 with architecture tools (pattern eval, migration estimate, tech stack recommendation) - workflow_group_chat.py: MAF GroupChatBuilder orchestration with LLM planner routing between local BusinessStrategist (MAF) and remote TechnicalArchitect (LangGraph via MCP) - pyproject.toml: add langgraph, langchain-openai, langchain-core deps - README.md: update to 9 scripts, add cross-framework architecture diagram, Quick Start sections, and dependency table entries * MCP agent demo: rewrite README as MCP-vs-A2A thesis, delete proxy agent, finalize group chat with inline Planner * README: Mermaid diagrams, professional tone, remove LinkedIn teasing language * README: reframe around multi-framework interop problem, two design patterns (Agent-as-Tool + Agent Adapter), add conceptual architecture * README: fix Mermaid diagrams - use br tags instead of \n for line breaks * README: fix A2A comparison table - elicitation supported, structured schemas not * Upgrade agent-framework to 1.0.0rc1 and fastmcp to 3.0.2 Breaking changes migrated: - agent-framework 1.0.0rc1: ChatAgent->Agent, AgentThread->AgentSession, run_stream->run(stream=True), model->default_options(ChatOptions), WorkflowOutputEvent/AgentRunEvent/RequestInfoEvent->unified WorkflowEvent, MagenticBuilder now uses constructor kwargs, orchestrations moved to agent_framework_orchestrations package - fastmcp 3.0.2: removed mcp version pin (now pulled by fastmcp), no code changes needed (fully compatible) Files updated: - agentic_ai/agents/agent_framework/single_agent.py - agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py - agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py - agentic_ai/agents/agent_framework/multi_agent/magentic_group.py - agentic_ai/applications/pyproject.toml (agent-framework==1.0.0rc1) - agentic_ai/workflow/fraud_detection_durable/pyproject.toml (agent-framework==1.0.0rc1) - mcp/pyproject.toml (fastmcp==3.0.2) - tests/test_agent_framework_rc1_regression.py (51 regression tests, all passing) * Fix CI: regenerate requirements.txt with agent-framework-core==1.0.0rc1 The Docker build uses requirements.txt (not pyproject.toml/uv.lock), and it still had agent-framework-core==1.0.0b260130 pinned, which conflicts with agent-framework==1.0.0rc1 requiring agent-framework-core==1.0.0rc1. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22315210980 * Fix: use GH_PAT for auto-merge to trigger downstream workflows Pushes made by GITHUB_TOKEN don't trigger other workflows (GitHub Actions limitation to prevent infinite loops). This meant the auto-merge into int-agentic never triggered promote-to-main.yml. Now uses secrets.GH_PAT with fallback to GITHUB_TOKEN so: 1. If GH_PAT is configured: merge triggers promote-to-main.yml 2. If not: merge still works, but promotion PR must be created manually * Add workflow_dispatch trigger to promote-to-main Allows manual triggering when auto-trigger is missed (e.g. after GITHUB_TOKEN-based merges that don't fire downstream workflows). * Fix: Cosmos DB auth + remove phantom reflection_workflow_agent 1. MCP Cosmos DB: Replace AzureCliCredential (not available in containers) with ManagedIdentityCredential (when AZURE_CLIENT_ID is set) or DefaultAzureCredential as fallback. This fixes the 'Azure CLI not found on path' error in production Container Apps. 2. Backend: Remove reflection_workflow_agent from DEFAULT_AVAILABLE_AGENTS and descriptions - the module file doesn't exist, causing it to show as a broken option in the UI agent selector. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22321497108 * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Fix promote-to-main + auto-destroy dev environments 1. promote-to-main.yml: Use GH_PAT instead of GITHUB_TOKEN so the workflow can create PRs (GITHUB_TOKEN lacks permission for this in repos with branch protection). 2. orchestrate.yml: Add Step 8 auto-destroy that tears down the integration-* environment after successful auto-merge to int-agentic. Only dev environments are destroyed; production is retained. * Trigger rebuild: deploy Cosmos DB auth fix and agent list cleanup * Fix: iterate ResponseStream directly, not .updates ResponseStream is AsyncIterable - iterate with 'async for chunk in stream:' The .updates property returns a Sequence (list), not an async iterator. This caused TypeError: 'async for' requires __aiter__, got list. Not caught in local tests because unit tests mocked the streaming and the regression tests verified API signatures but didn't run live agents. * Fix: make integration test failures block the pipeline Removed continue-on-error: true from the pytest step so test failures actually fail the workflow. Previously, 5/7 tests could fail and the pipeline would still report success and proceed to auto-merge. * Fix: use SHA-tagged images for container deployment, not :latest Container Apps don't create a new revision when the image tag is unchanged. Using :latest meant the old container kept running even after a new image was pushed. Now passes github.sha as the image tag, which forces a new revision on every deploy. This was the root cause of the ResponseStream.updates bug reaching production - the integration tests ran against old containers. * Skip MCP localhost check in CI mode In CI, the evaluation talks to the backend via HTTP which connects to the deployed MCP container internally. The localhost:8000 check is only useful for local dev and was producing a confusing warning in CI logs. * Fix: auto-merge creates PR if none exists Previously auto-merge only looked for an existing open PR and skipped if none was found. Now it auto-creates a PR from the dev branch to int-agentic if one doesn't exist, then merges it. This ensures every successful pipeline run promotes code to int-agentic regardless of whether a PR was manually created beforehand. * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22330447642 * chore: merge james-dev into int-agentic (auto) Auto-merged after successful CI/CD pipeline run 22334055353 --------- Co-authored-by: James N. * Bump agent-framework to 1.2.1 and migrate AzureOpenAIChatClient → OpenAIChatClient Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/b1edc1aa-9c20-46c5-afdc-0256050a03de Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Rewrite handoff agent on native HandoffBuilder; update regression tests for 1.2.1 Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/b1edc1aa-9c20-46c5-afdc-0256050a03de Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Update HANDOFF_README and dependency table for native HandoffBuilder migration Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/b1edc1aa-9c20-46c5-afdc-0256050a03de Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Address GPT-5.5 review: fix CheckpointStorage protocol, reflection_agent kwargs, observability sample Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/b1edc1aa-9c20-46c5-afdc-0256050a03de Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Fix PEP 8 spacing in mcp_agent_demo files (code review nits) Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/b1edc1aa-9c20-46c5-afdc-0256050a03de Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Regenerate uv.lock files; pass workflow_name to 1.2.x checkpoint listing Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/91307ce7-2444-4b5f-b75f-2b736bc022c5 Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> --------- Co-authored-by: James Nguyen Co-authored-by: James N. Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> --------- Co-authored-by: James N. Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com> Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> * Fix ChatOptions key for agent-framework 1.2.1 (model_id → model) Agent-Logs-Url: https://github.com/microsoft/OpenAIWorkshop/sessions/f921a448-fe80-4a39-8834-6b0ee615f1d1 Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> --------- Co-authored-by: James Nguyen Co-authored-by: James N. Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com> Co-authored-by: james-tn <25941658+james-tn@users.noreply.github.com> Co-authored-by: copilot --- .../agent_framework/multi_agent/handoff_multi_domain_agent.py | 2 +- .../agents/agent_framework/multi_agent/magentic_group.py | 2 +- .../agents/agent_framework/multi_agent/reflection_agent.py | 4 ++-- agentic_ai/agents/agent_framework/single_agent.py | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py index 320d634de..ab8ea3a08 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py @@ -340,7 +340,7 @@ async def _setup(self) -> None: description=cfg["description"], instructions=cfg["instructions"], tools=domain_tools, - default_options=ChatOptions(model_id=self.openai_model_name), + default_options=ChatOptions(model=self.openai_model_name), require_per_service_call_history_persistence=True, ) await agent.__aenter__() diff --git a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py index 33e2c679d..a8208d799 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py +++ b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py @@ -579,7 +579,7 @@ async def _create_participants( agent_kwargs: Dict[str, Any] = { **defaults, "client": participant_client, - "default_options": ChatOptions(model_id=self.openai_model_name), + "default_options": ChatOptions(model=self.openai_model_name), } # Apply tool filtering for this participant's domain diff --git a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py index 394e199db..f01158643 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py @@ -108,7 +108,7 @@ async def _setup_agents(self) -> None: name="PrimaryAgent", instructions=PRIMARY_AGENT_INSTRUCTIONS, tools=tools, - default_options=ChatOptions(model_id=self.openai_model_name), + default_options=ChatOptions(model=self.openai_model_name), ) self._reviewer = FrameworkAgent( @@ -116,7 +116,7 @@ async def _setup_agents(self) -> None: name="Reviewer", instructions=REVIEWER_INSTRUCTIONS, tools=tools, - default_options=ChatOptions(model_id=self.openai_model_name), + default_options=ChatOptions(model=self.openai_model_name), ) # Initialize agents diff --git a/agentic_ai/agents/agent_framework/single_agent.py b/agentic_ai/agents/agent_framework/single_agent.py index a9917754c..1a099c21c 100644 --- a/agentic_ai/agents/agent_framework/single_agent.py +++ b/agentic_ai/agents/agent_framework/single_agent.py @@ -85,7 +85,7 @@ async def _setup_single_agent(self) -> None: name="ai_assistant", instructions=instructions, tools=tools, - default_options=ChatOptions(model_id=self.openai_model_name), + default_options=ChatOptions(model=self.openai_model_name), ) try: From c2c22acd9d5d2f84ad962c28def7cf34a979af10 Mon Sep 17 00:00:00 2001 From: Copilot <223556219+Copilot@users.noreply.github.com> Date: Tue, 28 Apr 2026 12:43:21 -0700 Subject: [PATCH 37/37] Drop AZURE_OPENAI_API_VERSION; let agent-framework default to Responses 'preview' Live deployment was returning 500 'API version not supported' from Azure OpenAI because the agents pinned api_version='2025-04-01-preview' on every OpenAIChatClient construction. agent-framework 1.2.1 already reads AZURE_OPENAI_API_VERSION from env and otherwise defaults to the Responses-API magic value 'preview' (v1 endpoint), which is the right behaviour for Azure deployments like gpt-5.2-chat. Changes: - Remove api_version=self.api_version from all four agent_framework agents (single_agent, handoff_multi_domain_agent, magentic_group, reflection_agent). - Drop api_version from each agent's _validate_configuration. - Stop injecting AZURE_OPENAI_API_VERSION into ca-be Terraform; remove the openai_api_version variable and tfvars entries. - Also fix two latent CI issues that were already noted in PR #423 review: * tests/test_agent_framework_1_2_1_regression.py::test_chat_options_model_id asserted the wrong field name; renamed to test_chat_options_model and added an __annotations__ check. * magentic_group._validate_configuration no longer hard-requires the API key; managed identity is now accepted, matching the other three agents. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- .../multi_agent/handoff_multi_domain_agent.py | 6 ++--- .../multi_agent/magentic_group.py | 13 ++++++----- .../multi_agent/reflection_agent.py | 3 +-- .../agents/agent_framework/single_agent.py | 6 ++--- infra/terraform/_aca-be.tf | 5 ----- infra/terraform/dev.tfvars | 1 - infra/terraform/prod.tfvars | 1 - infra/terraform/variables.tf | 5 ----- .../test_agent_framework_1_2_1_regression.py | 22 +++++++++++++++---- 9 files changed, 31 insertions(+), 31 deletions(-) diff --git a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py index ab8ea3a08..fd242bcb5 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/handoff_multi_domain_agent.py @@ -286,10 +286,10 @@ async def _setup(self) -> None: has_api_key = bool(self.azure_openai_key) has_credential = bool(self.azure_credential) - if not all([self.azure_deployment, self.azure_openai_endpoint, self.api_version]): + if not all([self.azure_deployment, self.azure_openai_endpoint]): raise RuntimeError( "Azure OpenAI configuration is incomplete. Ensure " - "AZURE_OPENAI_CHAT_DEPLOYMENT, AZURE_OPENAI_ENDPOINT, and AZURE_OPENAI_API_VERSION are set." + "AZURE_OPENAI_CHAT_DEPLOYMENT and AZURE_OPENAI_ENDPOINT are set." ) if not has_api_key and not has_credential: raise RuntimeError( @@ -311,7 +311,6 @@ async def _setup(self) -> None: api_key=self.azure_openai_key, model=self.azure_deployment, azure_endpoint=self.azure_openai_endpoint, - api_version=self.api_version, ) logger.info("[HANDOFF] Using API key authentication for Azure OpenAI") else: @@ -319,7 +318,6 @@ async def _setup(self) -> None: credential=self.azure_credential, model=self.azure_deployment, azure_endpoint=self.azure_openai_endpoint, - api_version=self.api_version, ) logger.info("[HANDOFF] Using managed identity authentication for Azure OpenAI") diff --git a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py index a8208d799..e6e08dacb 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py +++ b/agentic_ai/agents/agent_framework/multi_agent/magentic_group.py @@ -295,10 +295,15 @@ async def chat_async(self, prompt: str) -> str: return cleaned_answer def _validate_configuration(self) -> None: - if not all([self.azure_openai_key, self.azure_deployment, self.azure_openai_endpoint, self.api_version]): + if not all([self.azure_deployment, self.azure_openai_endpoint]): raise RuntimeError( - "Azure OpenAI configuration is incomplete. Ensure AZURE_OPENAI_API_KEY, " - "AZURE_OPENAI_CHAT_DEPLOYMENT, AZURE_OPENAI_ENDPOINT, and AZURE_OPENAI_API_VERSION are set." + "Azure OpenAI configuration is incomplete. Ensure AZURE_OPENAI_CHAT_DEPLOYMENT " + "and AZURE_OPENAI_ENDPOINT are set." + ) + if not self.azure_openai_key and not getattr(self, "azure_credential", None): + raise RuntimeError( + "Azure OpenAI authentication is not configured. Set AZURE_OPENAI_API_KEY " + "or provide a managed-identity credential." ) def _build_headers(self) -> Dict[str, str]: @@ -376,7 +381,6 @@ def _build_chat_client(self) -> OpenAIChatClient: api_key=self.azure_openai_key, model=self.azure_deployment, azure_endpoint=self.azure_openai_endpoint, - api_version=self.api_version, ) elif self.azure_credential: logger.info("[AgentFramework-Magentic] Using managed identity authentication for Azure OpenAI") @@ -384,7 +388,6 @@ def _build_chat_client(self) -> OpenAIChatClient: credential=self.azure_credential, model=self.azure_deployment, azure_endpoint=self.azure_openai_endpoint, - api_version=self.api_version, ) else: raise RuntimeError( diff --git a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py index f01158643..e7cb1a773 100644 --- a/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py +++ b/agentic_ai/agents/agent_framework/multi_agent/reflection_agent.py @@ -80,7 +80,7 @@ async def _setup_agents(self) -> None: return # Validate configuration - if not all([self.azure_deployment, self.azure_openai_endpoint, self.api_version]): + if not all([self.azure_deployment, self.azure_openai_endpoint]): raise RuntimeError("Azure OpenAI configuration incomplete.") if not self.azure_openai_key and not self.azure_credential: @@ -90,7 +90,6 @@ async def _setup_agents(self) -> None: client_kwargs = { "model": self.azure_deployment, "azure_endpoint": self.azure_openai_endpoint, - "api_version": self.api_version, } if self.azure_openai_key: client_kwargs["api_key"] = self.azure_openai_key diff --git a/agentic_ai/agents/agent_framework/single_agent.py b/agentic_ai/agents/agent_framework/single_agent.py index 1a099c21c..0076a1f81 100644 --- a/agentic_ai/agents/agent_framework/single_agent.py +++ b/agentic_ai/agents/agent_framework/single_agent.py @@ -38,10 +38,10 @@ async def _setup_single_agent(self) -> None: has_api_key = bool(self.azure_openai_key) has_credential = bool(self.azure_credential) - if not all([self.azure_deployment, self.azure_openai_endpoint, self.api_version]): + if not all([self.azure_deployment, self.azure_openai_endpoint]): raise RuntimeError( "Azure OpenAI configuration is incomplete. Ensure " - "AZURE_OPENAI_CHAT_DEPLOYMENT, AZURE_OPENAI_ENDPOINT, and AZURE_OPENAI_API_VERSION are set." + "AZURE_OPENAI_CHAT_DEPLOYMENT and AZURE_OPENAI_ENDPOINT are set." ) if not has_api_key and not has_credential: @@ -59,7 +59,6 @@ async def _setup_single_agent(self) -> None: api_key=self.azure_openai_key, model=self.azure_deployment, azure_endpoint=self.azure_openai_endpoint, - api_version=self.api_version, ) logger.info("[AgentFramework] Using API key authentication for Azure OpenAI") else: @@ -67,7 +66,6 @@ async def _setup_single_agent(self) -> None: credential=self.azure_credential, model=self.azure_deployment, azure_endpoint=self.azure_openai_endpoint, - api_version=self.api_version, ) logger.info("[AgentFramework] Using managed identity authentication for Azure OpenAI") diff --git a/infra/terraform/_aca-be.tf b/infra/terraform/_aca-be.tf index 641d36d9b..6375d0bc9 100644 --- a/infra/terraform/_aca-be.tf +++ b/infra/terraform/_aca-be.tf @@ -85,11 +85,6 @@ resource "azurerm_container_app" "backend" { value = local.openai_endpoint } - env { - name = "AZURE_OPENAI_API_VERSION" - value = var.openai_api_version - } - env { name = "AZURE_OPENAI_EMBEDDING_DEPLOYMENT" value = var.openai_embedding_deployment_name diff --git a/infra/terraform/dev.tfvars b/infra/terraform/dev.tfvars index 48046bccc..93f1ccfbd 100644 --- a/infra/terraform/dev.tfvars +++ b/infra/terraform/dev.tfvars @@ -14,7 +14,6 @@ create_openai_deployment = true openai_deployment_name = "gpt-5.2-chat" openai_model_name = "gpt-5.2-chat" openai_model_version = "2025-12-11" -openai_api_version ="2025-04-01-preview" openai_deployment_capacity = 200 # 200k tokens/minute - needed for agent evaluation # OpenAI embedding deployment configuration diff --git a/infra/terraform/prod.tfvars b/infra/terraform/prod.tfvars index 8ee8b17d5..61f201161 100644 --- a/infra/terraform/prod.tfvars +++ b/infra/terraform/prod.tfvars @@ -14,7 +14,6 @@ create_openai_deployment = true openai_deployment_name = "gpt-5.2-chat" openai_model_name = "gpt-5.2-chat" openai_model_version = "2025-12-11" -openai_api_version = "2025-04-01-preview" openai_deployment_capacity = 200 # 200k tokens/minute # OpenAI embedding deployment configuration diff --git a/infra/terraform/variables.tf b/infra/terraform/variables.tf index 31c8aca54..880095ea6 100644 --- a/infra/terraform/variables.tf +++ b/infra/terraform/variables.tf @@ -41,11 +41,6 @@ variable "openai_model_version" { type = string default = "2025-12-11" } -variable "openai_api_version" { - description = "OpenAI API version" - type = string - default = "2025-04-01-preview" -} variable "openai_deployment_capacity" { description = "Capacity (TPM in thousands) for OpenAI deployment" type = number diff --git a/tests/test_agent_framework_1_2_1_regression.py b/tests/test_agent_framework_1_2_1_regression.py index 2b1af5111..491341321 100644 --- a/tests/test_agent_framework_1_2_1_regression.py +++ b/tests/test_agent_framework_1_2_1_regression.py @@ -149,11 +149,25 @@ def test_framework_agent_constructor_signature(self): # description was wired through in 1.2.x and is consumed by HandoffBuilder assert 'description' in params - def test_chat_options_model_id(self): - """ChatOptions accepts model_id for specifying the model.""" + def test_chat_options_model(self): + """ChatOptions exposes ``model`` (not ``model_id``) in 1.2.x. + + Regression for the CI failure where agents constructed + ``ChatOptions(model_id=...)``. ``ChatOptions`` is a ``TypedDict`` with + ``total=False``, so unknown keys are silently accepted at construction + time and only blow up later when forwarded as kwargs to the OpenAI + ``responses.create`` call (``TypeError: unexpected keyword argument + 'model_id'``). Assert against ``__annotations__`` so the test fails + loudly if the field name changes again. + """ from agent_framework import ChatOptions - opts = ChatOptions(model_id="gpt-4o") - assert opts["model_id"] == "gpt-4o" + opts = ChatOptions(model="gpt-4o") + assert opts["model"] == "gpt-4o" + assert "model" in ChatOptions.__annotations__ + assert "model_id" not in ChatOptions.__annotations__, ( + "ChatOptions field is `model` in agent-framework 1.2.x; " + "passing model_id= silently constructs a bad dict that fails downstream." + ) def test_openai_client_constructor_supports_azure_kwargs(self): """OpenAIChatClient accepts model, api_key, credential, azure_endpoint, api_version."""