diff --git a/examples/decision_lineage_demo/README.md b/examples/decision_lineage_demo/README.md index 95e596ca..16a2ecc0 100644 --- a/examples/decision_lineage_demo/README.md +++ b/examples/decision_lineage_demo/README.md @@ -155,6 +155,10 @@ A talk track and a click-by-click walkthrough live alongside: `DecisionOption`, `OptionOutcome`, and `DropReason` included), step by step: [`DATA_LINEAGE.md`](DATA_LINEAGE.md) +- Leadership-ready slide deck (Marp; renders to PDF / PPTX / HTML + via `marp SLIDES.md --html --pdf`) covering the same 5-question + narrative with the ads-domain vocabulary: + [`SLIDES.md`](SLIDES.md) ## File map @@ -166,6 +170,9 @@ decision_lineage_demo/ ├── BQ_STUDIO_WALKTHROUGH.md # click-by-click in BQ Studio ├── DEMO_QUESTIONS.md # 5 EU-compliance questions: BQ CA vs. direct GQL ├── DATA_LINEAGE.md # canonical graph + richer demo graph lineage +├── SLIDES.md # Marp deck source — leadership / Google-Next-style +├── SLIDES.html # rendered HTML (open in browser, no install needed) +├── SLIDES.pptx # rendered PowerPoint (editable in Google Slides / Keynote / PPT) ├── setup.sh # one-shot bootstrap ├── reset.sh # tear down dataset + rendered files ├── render_queries.sh # sed-renders the .gql template diff --git a/examples/decision_lineage_demo/SLIDES.html b/examples/decision_lineage_demo/SLIDES.html new file mode 100644 index 00000000..af5752e0 --- /dev/null +++ b/examples/decision_lineage_demo/SLIDES.html @@ -0,0 +1,10909 @@ +Decision Lineage with BigQuery Context Graphs
+

EU AI Act · GDPR · DSA

+

Decision Lineage for AI Agents

+

Turn live agent behavior into queryable BigQuery evidence: decisions, options, outcomes, and rationale.

+
+BigQuery Agent Analytics SDK · examples/decision_lineage_demo · Issue #98 +
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Why this matters now

+
+
+

Regulation

+

EU AI Act obligations phase in through 2 Aug 2027; many high-risk and transparency rules start 2 Aug 2026.

+
+
+

Trust

+

Most agent demos produce an answer and ask reviewers to trust a transcript. That does not scale to audit.

+
+
+

Operations

+

Product, legal, and engineering need the same answer: what happened, why, and where is the evidence?

+
+
+
+Risk framing: AI Act fines reach up to 7% of worldwide annual turnover for prohibited practices, and up to 3% for many other operator / transparency obligations. The useful response is evidence, not screenshots. +
+
Sources: EU AI Act Service Desk implementation timeline; AI Act Article 99 penalty tiers.
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

What we built

+
Working demo, not mock data
+

Take a real ADK media-planning agent, attach the BigQuery Agent Analytics Plugin, use AI.GENERATE to extract the decisions and options present in the traces, then publish the result as a BigQuery Property Graph for GQL in BigQuery Studio.

+
+
1
Live agent
Gemini 2.5 Pro campaign planner
+
→
+
2
Plugin spans
162 recorded events in BigQuery
+
→
+
3
Extraction
Entities, decisions, options, rationales
+
→
+
4
Property graph
Query, visualize, audit
+
+
Promise: the graph is derived from real plugin output. The demo does not rely on hand-shaped seed traces.
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
Section 1 of 4
+

The data behind today's demo

+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Six campaigns, six live agent runs

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BrandCampaignBudgetAudience
NikeSummer Run 2026$360KSerious runners 18-35
NikeWinter Trail 2026$500KTrail-runners & hikers 25-45
AdidasTrack Season 2026$420KNCAA & HS sprinters 16-22
PumaSoccer Cup 2026$280KSoccer fans 18-30
ReebokCrossFit Open 2026$340KFitness pros 25-40
LululemonYoga Flow 2026$250KYoga practitioners 22-45
+
+
6sessions
+
162plugin spans
+
31extracted decisions
+
92decision options
+
+

Each row is one ADK invocation against gemini-2.5-pro; each invocation produced 27 plugin-recorded spans in the verified demo run.

+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Three writers populate seven tables

+
+
+

BQ AA Plugin

+Writes `agent_events`
+Raw trace evidence from the live ADK runner. +
+
+

SDK AI.GENERATE

+Writes `extracted_biz_nodes`, `decision_points`, `candidates`
+Typed facts extracted from trace text. +
+
+

SDK SQL DML

+Writes `context_cross_links`, `made_decision_edges`, `candidate_edges`
+Graph edges with stable keys. +
+
+
`AI.GENERATE` runs twice across all sessions: business entities first, then decision options and rationale.
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

The graph the demo presents

+

The SDK ships the canonical graph. The demo adds ads-domain labels so BigQuery Studio reads like the business process:

+
+
+

Primary audit path

+

CampaignRun → PlanningDecision → DecisionOption → OptionOutcome

+

DecisionOption → DropReason

+

A campaign has planning decisions; each decision weighed options; every option has an outcome and dropped options have rationale.

+
+
+

Evidence path

+

CampaignRun → AgentStep → PlanningDecision

+

AgentStep → MediaEntity

+

PlanningDecision → DecisionCategory

+

The business answer stays tied to the span, entity, and category that produced it.

+
+
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
Section 2 of 4
+

Five regulator-shaped questions, answered live

+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Q1 — Right to explanation

+
EU AI Act Art. 86
GDPR Art. 22
+
+

"For the Nike Summer Run campaign, what audience did the AI pick, what alternatives did it consider, and why did it reject the others?"

+
+
GRAPH `<P>.<D>.rich_agent_context_graph`
+MATCH (cr:CampaignRun)-[:CampaignDecision]->(dp:PlanningDecision)
+      -[:WeighedOption]->(opt:DecisionOption)
+WHERE cr.session_id = '<SESSION>'
+  AND LOWER(dp.decision_type) LIKE '%audience%'
+RETURN DISTINCT opt.status, opt.name, opt.score, opt.rejection_rationale
+ORDER BY opt.status DESC, opt.score DESC;
+
+

Three rows. Selected: Serious Runners 18-35 @ 0.99. Dropped: Casual Runners 25-45 (lower purchase intent), Fitness Enthusiasts 18-35 (group too broad). The extracted rationale stays attached to the option it explains.

+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Q2 — Bias / fairness audit

+
EU AI Act Art. 10
EU AI Act Art. 71
+
+

"Across our 2026 ad portfolio, did the AI ever reject a candidate based on age or demographic criteria?"

+
+
GRAPH `<P>.<D>.rich_agent_context_graph`
+MATCH (dp:PlanningDecision)-[:WeighedOption]->(opt:DecisionOption)
+WHERE opt.status = 'DROPPED'
+  AND (LOWER(opt.rejection_rationale) LIKE '%age %'
+       OR LOWER(opt.rejection_rationale) LIKE '%demographic%'
+       OR LOWER(opt.rejection_rationale) LIKE '%youth%')
+RETURN DISTINCT dp.decision_type, opt.name, opt.rejection_rationale;
+
+

Multiple matches. Youth Track & Field (13-15) — outside specified 16-22 range. Affluent Hikers (35-55) — age-range mismatch. The graph surfaces specific rationales for human review: proxy risk, legitimate campaign constraint, or extraction artifact?

+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Q3 — Human-oversight trigger

+
EU AI Act Art. 14
+
+

"Did the agent ever commit a decision below 0.7 confidence? Those should have triggered human review."

+
+
GRAPH `<P>.<D>.rich_agent_context_graph`
+MATCH (dp:PlanningDecision)-[:WeighedOption]->(opt:DecisionOption)
+WHERE opt.status = 'SELECTED' AND opt.score < 0.7
+RETURN DISTINCT dp.session_id, dp.decision_type, opt.name, opt.score
+ORDER BY opt.score ASC;
+
+
+
0
+
rows returned
+
+

The empty result is the audit artifact. "We ran the human-oversight predicate against the entire portfolio for this period. The trigger never fired." Tighten the threshold to 0.85 → instant new at-risk list.

+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Q4 — Decision reproducibility

+
EU AI Act Art. 12
EU AI Act Art. 13
+
+

"Subpoena: produce the full audit trail for the Adidas creative-theme decision."

+
+
GRAPH `<P>.<D>.rich_agent_context_graph`
+MATCH (step:AgentStep)-[:DecidedAt]->(dp:PlanningDecision)
+      -[:WeighedOption]->(opt:DecisionOption)
+WHERE dp.session_id = '<SESSION>'
+  AND LOWER(dp.decision_type) LIKE '%creative%'
+  AND step.event_type = 'LLM_RESPONSE'
+RETURN DISTINCT dp.span_id, opt.status, opt.name, opt.score, opt.rejection_rationale
+ORDER BY opt.status DESC, opt.score DESC;
+
+

Three rows. Selected: "Built for a New Record" @ 0.97. Two dropped with rationale, both pointing back to the same evidence_span_id. That span lives in agent_events with content, timestamp, and latency. Record-keeping becomes a queryable trail.

+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Q5 — Systemic / pattern audit

+
EU AI Act Art. 17
EU AI Act Art. 60
+
+

"Where in our portfolio does the AI reject candidates least decisively?"

+
+
GRAPH `<P>.<D>.rich_agent_context_graph`
+MATCH (dp:PlanningDecision)-[:WeighedOption]->(opt:DecisionOption)
+WHERE opt.status = 'DROPPED'
+RETURN dp.decision_type, COUNT(opt) AS rejections, AVG(opt.score) AS avg_dropped_score
+GROUP BY dp.decision_type
+ORDER BY rejections DESC LIMIT 5;
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
decision_typerejectionsavg_dropped_score
Creative Theme Selection120.79
Audience Selection120.66 ← lowest
Channel Strategy Selection60.77
Placement Selection70.74
+
Audience Selection has the lowest average dropped score. Reuse the Q2 filter for repeatable bias review.
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
Section 3 of 4
+

What this unlocks

+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Three takeaways for leadership

+
+
+

1. Audit posture as a query

+

The audit surface for our agent platform is now a live BigQuery query, not a slide deck or quarterly review.

+

2. The schema generalizes

+

Brand-neutral, channel-neutral, decision-type-neutral. Point an instrumented agent at the same pipeline and the same audit patterns apply.

+
+
+

3. Composes with what we run

+
    +
  • BQ AA Plugin — the instrumentation path
    • +
  • SDK extraction — one method call: build_context_graph(use_ai_generate=True, include_decisions=True)
    • +
  • BigQuery — the warehouse you already pay for
    • +
+

No new graph service. No separate audit datastore.

+
+
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Compliance-anchor map

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
QuestionEU AI ActGDPRDSA
Q1 Right to explanationArt. 86Art. 22Art. 26
Q2 Bias / fairness auditArt. 10, Art. 71Art. 5(1)(d), Art. 22Art. 26
Q3 Human oversightArt. 14Art. 22—
Q4 ReproducibilityArt. 12, Art. 13Art. 30Art. 26
Q5 Systemic-pattern auditArt. 17, Art. 60Art. 35Art. 26
+

Five queries against one graph create reusable evidence hooks for three EU regulatory conversations. This is not a compliance certification; it is the data substrate a compliance review needs.

+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

How fast can we ship this?

+
+
+

Setup, on a clean GCP project

+
cd examples/decision_lineage_demo
+./setup.sh
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PhaseWall time
Tooling + APIs + venv~30s
Live agent (6 sessions)3-7 min
AI.GENERATE extraction30-90s
Rich-graph projection~10s
Render BQ Studio queries<1s
+
+
+

Cost per setup run

+
    +
  • 6 live gemini-2.5-pro invocations
    • +
  • 2 AI.GENERATE extraction queries
    • +
  • A few hundred BQ rows + one property graph
    • +
+

Order-of-cents for the verified demo run. Queries against the prebuilt graph are lightweight.

+

From zero to leadership demo

+

Under 10 minutes, fully reproducible, on any GCP project.

+
+
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
Section 4 of 4
+

Where to go next

+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Open source — try it on your project

+
+
+

Repository

+

GoogleCloudPlatform/BigQuery-Agent-Analytics-SDK

+

The demo bundle

+

examples/decision_lineage_demo/

+

What ships in the bundle

+
    +
  • setup.sh / reset.sh — one-shot bootstrap + tear-down
    • +
  • agent/ + campaigns.py — real ADK agent + 6 briefs
    • +
  • run_agent.py + build_graph.py + build_rich_graph.py
    • +
  • bq_studio_queries.gql (rendered) — six GQL blocks
    • +
  • property_graph.gql (rendered) — recreate-from-tables DDL
    • +
+
+
+

Documentation

+ +

License

+

Apache 2.0

+
+
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Anticipated questions

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
QShort answer
How long to deploy?Plugin integration, SDK call, committed DDL. Days, not quarters, for a pilot.
Other agents we haven't instrumented?Same plugin, same plug-in point. Schema doesn't care which agent wrote the spans.
Cost?Two AI.GENERATE calls per build + standard BQ query cost. Order-of-cents for this verified demo run.
What if AI.GENERATE misses a decision?Build script reports per-session counts. Re-run extraction without re-running the agent. Talk track is count-agnostic by design.
Does this expose PII?Stores trace-derived rationale text. PII handling follows the existing agent_events retention and access policy.
Who operates this?The team that owns the agent platform. Plugin write path + SDK read path both already on-call.
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+
+

Q&A

+

From agent behavior to auditable evidence

+
+Open source · Apache 2.0 · github.com/GoogleCloudPlatform/BigQuery-Agent-Analytics-SDK +
+examples/decision_lineage_demo · Issue #98 +
+
Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo
+
+

Copyright 2026 Google LLC +Licensed under the Apache License, Version 2.0. + +Decision Lineage with BigQuery Context Graphs — leadership deck. + +Render with Marp (https://marp.app): + marp SLIDES.md --html --pdf # SLIDES.pdf + marp SLIDES.md --html --pptx # SLIDES.pptx + marp SLIDES.md --html # SLIDES.html + marp SLIDES.md --watch --html # live preview + +`SLIDES.html` and `SLIDES.pptx` are checked in alongside this +source so reviewers can open the deck without installing Marp. +After editing this file, regenerate both with: + + npx -y @marp-team/marp-cli@latest SLIDES.md --html + npx -y @marp-team/marp-cli@latest SLIDES.md --html --pptx --no-stdin + +(or `marp SLIDES.md --html --pptx` if you have Marp installed +globally via `npm install -g @marp-team/marp-cli`).

SPEAKER NOTE — 30s +Open with business pressure, not fear. The point is not "this certifies +compliance"; the point is "we can now show our work with data."

Source: https://ai-act-service-desk.ec.europa.eu/en/ai-act/eu-ai-act-implementation-timeline +Article 99: 7% applies to prohibited practices; 3% applies to many +non-Article-5 obligations. Keep the wording precise.

Talk track: real diversity — different brand, audience, budget, season. +If live extraction changes, keep the exact counts in the metric row aligned +with the latest verified dataset or switch them to approximate language.

Explorer vocabulary in the graph pane: +CampaignRun, AgentStep, MediaEntity, PlanningDecision, DecisionOption, +DecisionCategory, OptionOutcome, DropReason; edges CampaignActivity, +NextStep, DecidedAt, ConsideredEntity, CampaignDecision, WeighedOption, +HasOutcome, RejectedBecause, InCategory.

Wrap with: "Three things to take with you. (1) Compliance posture as +a query. (2) The schema generalizes. (3) Composes with what we run. +Open for questions."

\ No newline at end of file diff --git a/examples/decision_lineage_demo/SLIDES.md b/examples/decision_lineage_demo/SLIDES.md new file mode 100644 index 00000000..cb2bea22 --- /dev/null +++ b/examples/decision_lineage_demo/SLIDES.md @@ -0,0 +1,766 @@ +--- +marp: true +title: Decision Lineage with BigQuery Context Graphs +description: A leadership-pitched deck that mirrors examples/decision_lineage_demo/. +author: BigQuery Agent Analytics SDK +theme: gaia +size: 16:9 +paginate: true +backgroundColor: "#ffffff" +color: "#202124" +style: | + /* Google-style typography, presentation rhythm, and executive-ready components. */ + section { + font-family: "Google Sans", "Roboto", -apple-system, "Segoe UI", sans-serif; + font-size: 27px; + line-height: 1.35; + padding: 56px 64px; + letter-spacing: 0; + } + section.hero { + background-color: #174ea6 !important; + background-image: linear-gradient(135deg, #1a73e8 0%, #174ea6 58%, #202124 100%) !important; + color: #ffffff; + text-align: left; + } + section.hero h1 { + font-size: 66px; + line-height: 1.1; + margin-bottom: 16px; + color: #ffffff; + border: none; + } + section.hero h2 { + font-size: 29px; + font-weight: 400; + color: rgba(255,255,255,0.85); + border: none; + max-width: 920px; + } + section.hero .footer-note { + font-size: 18px; + color: rgba(255,255,255,0.7); + margin-top: 48px; + } + section h1 { + color: #1a73e8; + border-bottom: 2px solid #e8eaed; + padding-bottom: 8px; + font-size: 38px; + } + section h2 { + color: #202124; + font-size: 26px; + margin-top: 0; + } + section h3 { + color: #1a73e8; + font-size: 22px; + margin-bottom: 4px; + } + section.section-divider { + background: + linear-gradient(90deg, #f8fafd 0%, #ffffff 72%); + } + section.section-divider h1 { + color: #1a73e8; + border: none; + font-size: 56px; + } + section.section-divider .eyebrow { + color: #5f6368; + text-transform: uppercase; + letter-spacing: 4px; + font-size: 16px; + margin-bottom: 16px; + } + .kicker { + color: #5f6368; + text-transform: uppercase; + letter-spacing: 2px; + font-size: 15px; + font-weight: 700; + margin-bottom: 10px; + } + code { + background: #f1f3f4; + color: #202124; + border-radius: 4px; + padding: 2px 6px; + font-size: 0.9em; + } + pre { + background: #202124; + color: #e8eaed; + border-radius: 8px; + padding: 18px 22px; + font-size: 18px; + line-height: 1.4; + box-shadow: 0 2px 8px rgba(0,0,0,0.12); + } + pre code { background: transparent; color: inherit; padding: 0; } + table { + border-collapse: collapse; + margin: 12px 0; + font-size: 22px; + } + th { + background: #1a73e8; + color: #ffffff; + padding: 10px 14px; + text-align: left; + } + td { + border-bottom: 1px solid #e8eaed; + padding: 8px 14px; + } + blockquote { + border-left: 4px solid #1a73e8; + background: #e8f0fe; + color: #174ea6; + padding: 14px 22px; + margin: 12px 0; + font-style: normal; + font-size: 24px; + } + .pill { + display: inline-block; + background: #e8f0fe; + color: #1a73e8; + border-radius: 999px; + padding: 4px 14px; + font-size: 17px; + margin-right: 6px; + font-weight: 600; + } + .grid-2 { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 32px; + align-items: start; + } + .grid-3 { + display: grid; + grid-template-columns: repeat(3, 1fr); + gap: 18px; + align-items: stretch; + } + .compact { + font-size: 23px; + line-height: 1.28; + } + .compact li { + margin: 4px 0; + } + .card { + border: 1px solid #dadce0; + border-radius: 8px; + padding: 20px 22px; + background: #ffffff; + box-shadow: 0 1px 3px rgba(60,64,67,0.10); + } + .card h3 { + margin-top: 0; + } + .soft-card { + border-radius: 8px; + padding: 18px 22px; + background: #f8fafd; + border: 1px solid #d2e3fc; + } + .pipeline { + display: grid; + grid-template-columns: 1fr 40px 1fr 40px 1fr 40px 1fr; + align-items: center; + gap: 10px; + margin-top: 22px; + } + .pipeline .step { + border-radius: 8px; + padding: 18px 16px; + background: #ffffff; + border: 1px solid #dadce0; + min-height: 126px; + } + .pipeline .arrow { + color: #1a73e8; + font-size: 34px; + text-align: center; + } + .step-num { + display: inline-flex; + width: 28px; + height: 28px; + align-items: center; + justify-content: center; + border-radius: 50%; + background: #1a73e8; + color: #ffffff; + font-size: 16px; + font-weight: 700; + margin-bottom: 8px; + } + .metric-row { + display: grid; + grid-template-columns: repeat(4, 1fr); + gap: 16px; + margin: 18px 0; + } + .mini-stat { + border-left: 5px solid #1a73e8; + background: #f8fafd; + padding: 14px 18px; + border-radius: 6px; + } + .mini-stat strong { + display: block; + color: #202124; + font-size: 32px; + line-height: 1.1; + } + .mini-stat span { + color: #5f6368; + font-size: 15px; + text-transform: uppercase; + letter-spacing: 1px; + } + .stat { + font-size: 64px; + color: #1a73e8; + font-weight: 700; + line-height: 1; + } + .stat-label { + color: #5f6368; + font-size: 18px; + text-transform: uppercase; + letter-spacing: 2px; + } + .source-line { + color: #5f6368; + font-size: 14px; + margin-top: 14px; + } + .small { + color: #5f6368; + font-size: 20px; + } + .accent-blue { color: #1a73e8; } + .accent-green { color: #188038; } + .accent-yellow { color: #b06000; } + .accent-red { color: #d93025; } + footer { + color: #5f6368; + font-size: 14px; + } + section.hero footer { + color: rgba(255,255,255,0.70); + } +footer: "Decision Lineage with BigQuery Context Graphs · examples/decision_lineage_demo" +--- + + + + + + +EU AI Act · GDPR · DSA + +# Decision Lineage for AI Agents + +## Turn live agent behavior into queryable BigQuery evidence: decisions, options, outcomes, and rationale. + +
+BigQuery Agent Analytics SDK · examples/decision_lineage_demo · Issue #98 +
+ + + +--- + +# Why this matters now + +
+ +
+ +### Regulation +EU AI Act obligations phase in through **2 Aug 2027**; many high-risk and transparency rules start **2 Aug 2026**. + +
+ +
+ +### Trust +Most agent demos produce an answer and ask reviewers to trust a transcript. That does not scale to audit. + +
+ +
+ +### Operations +Product, legal, and engineering need the same answer: *what happened, why, and where is the evidence?* + +
+ +
+ +
+Risk framing: AI Act fines reach up to 7% of worldwide annual turnover for prohibited practices, and up to 3% for many other operator / transparency obligations. The useful response is evidence, not screenshots. +
+ +
Sources: EU AI Act Service Desk implementation timeline; AI Act Article 99 penalty tiers.
+ + + +--- + +# What we built + +
Working demo, not mock data
+ +Take a real ADK media-planning agent, attach the **BigQuery Agent Analytics Plugin**, use `AI.GENERATE` to extract the decisions and options present in the traces, then publish the result as a **BigQuery Property Graph** for GQL in BigQuery Studio. + +
+ +
1
Live agent
Gemini 2.5 Pro campaign planner
+
→
+
2
Plugin spans
162 recorded events in BigQuery
+
→
+
3
Extraction
Entities, decisions, options, rationales
+
→
+
4
Property graph
Query, visualize, audit
+ +
+ +
Promise: the graph is derived from real plugin output. The demo does not rely on hand-shaped seed traces.
+ +--- + + + +
Section 1 of 4
+ +# The data behind today's demo + +--- + +# Six campaigns, six live agent runs + +| Brand | Campaign | Budget | Audience | +|---|---|---:|---| +| Nike | Summer Run 2026 | $360K | Serious runners 18-35 | +| Nike | Winter Trail 2026 | $500K | Trail-runners & hikers 25-45 | +| Adidas | Track Season 2026 | $420K | NCAA & HS sprinters 16-22 | +| Puma | Soccer Cup 2026 | $280K | Soccer fans 18-30 | +| Reebok | CrossFit Open 2026 | $340K | Fitness pros 25-40 | +| Lululemon | Yoga Flow 2026 | $250K | Yoga practitioners 22-45 | + +
+
6sessions
+
162plugin spans
+
31extracted decisions
+
92decision options
+
+ +Each row is one ADK invocation against `gemini-2.5-pro`; each invocation produced **27 plugin-recorded spans** in the verified demo run. + + + +--- + +# Three writers populate seven tables + +
+ +
+

BQ AA Plugin

+Writes `agent_events`
+Raw trace evidence from the live ADK runner. +
+ +
+

SDK AI.GENERATE

+Writes `extracted_biz_nodes`, `decision_points`, `candidates`
+Typed facts extracted from trace text. +
+ +
+

SDK SQL DML

+Writes `context_cross_links`, `made_decision_edges`, `candidate_edges`
+Graph edges with stable keys. +
+ +
+ +
`AI.GENERATE` runs twice across all sessions: business entities first, then decision options and rationale.
+ +--- + +# The graph the demo presents + +The SDK ships the canonical graph. The demo adds **ads-domain labels** so BigQuery Studio reads like the business process: + +
+ +
+ +### Primary audit path +`CampaignRun` → `PlanningDecision` → `DecisionOption` → `OptionOutcome` + +`DecisionOption` → `DropReason` + +A campaign has planning decisions; each decision weighed options; every option has an outcome and dropped options have rationale. + +
+ +
+ +### Evidence path +`CampaignRun` → `AgentStep` → `PlanningDecision` + +`AgentStep` → `MediaEntity` + +`PlanningDecision` → `DecisionCategory` + +The business answer stays tied to the span, entity, and category that produced it. + +
+ +
+ + + +--- + + + +
Section 2 of 4
+ +# Five regulator-shaped questions, answered live + +--- + +# Q1 — Right to explanation + +
EU AI Act Art. 86
GDPR Art. 22
+ +> *"For the Nike Summer Run campaign, what audience did the AI pick, what alternatives did it consider, and why did it reject the others?"* + +```sql +GRAPH `

..rich_agent_context_graph` +MATCH (cr:CampaignRun)-[:CampaignDecision]->(dp:PlanningDecision) + -[:WeighedOption]->(opt:DecisionOption) +WHERE cr.session_id = '' + AND LOWER(dp.decision_type) LIKE '%audience%' +RETURN DISTINCT opt.status, opt.name, opt.score, opt.rejection_rationale +ORDER BY opt.status DESC, opt.score DESC; +``` + +**Three rows.** Selected: *Serious Runners 18-35* @ 0.99. Dropped: *Casual Runners 25-45* (lower purchase intent), *Fitness Enthusiasts 18-35* (group too broad). **The extracted rationale stays attached to the option it explains.** + +--- + +# Q2 — Bias / fairness audit + +

EU AI Act Art. 10
EU AI Act Art. 71
+ +> *"Across our 2026 ad portfolio, did the AI ever reject a candidate based on age or demographic criteria?"* + +```sql +GRAPH `

..rich_agent_context_graph` +MATCH (dp:PlanningDecision)-[:WeighedOption]->(opt:DecisionOption) +WHERE opt.status = 'DROPPED' + AND (LOWER(opt.rejection_rationale) LIKE '%age %' + OR LOWER(opt.rejection_rationale) LIKE '%demographic%' + OR LOWER(opt.rejection_rationale) LIKE '%youth%') +RETURN DISTINCT dp.decision_type, opt.name, opt.rejection_rationale; +``` + +**Multiple matches.** *Youth Track & Field (13-15) — outside specified 16-22 range.* *Affluent Hikers (35-55) — age-range mismatch.* The graph surfaces specific rationales for **human review**: proxy risk, legitimate campaign constraint, or extraction artifact? + +--- + +# Q3 — Human-oversight trigger + +

EU AI Act Art. 14
+ +> *"Did the agent ever commit a decision below 0.7 confidence? Those should have triggered human review."* + +```sql +GRAPH `

..rich_agent_context_graph` +MATCH (dp:PlanningDecision)-[:WeighedOption]->(opt:DecisionOption) +WHERE opt.status = 'SELECTED' AND opt.score < 0.7 +RETURN DISTINCT dp.session_id, dp.decision_type, opt.name, opt.score +ORDER BY opt.score ASC; +``` + +

+
0
+
rows returned
+
+ +**The empty result is the audit artifact.** *"We ran the human-oversight predicate against the entire portfolio for this period. The trigger never fired."* Tighten the threshold to 0.85 → instant new at-risk list. + +--- + +# Q4 — Decision reproducibility + +
EU AI Act Art. 12
EU AI Act Art. 13
+ +> *"Subpoena: produce the full audit trail for the Adidas creative-theme decision."* + +```sql +GRAPH `

..rich_agent_context_graph` +MATCH (step:AgentStep)-[:DecidedAt]->(dp:PlanningDecision) + -[:WeighedOption]->(opt:DecisionOption) +WHERE dp.session_id = '' + AND LOWER(dp.decision_type) LIKE '%creative%' + AND step.event_type = 'LLM_RESPONSE' +RETURN DISTINCT dp.span_id, opt.status, opt.name, opt.score, opt.rejection_rationale +ORDER BY opt.status DESC, opt.score DESC; +``` + +**Three rows.** Selected: *"Built for a New Record"* @ 0.97. Two dropped with rationale, both pointing back to the same `evidence_span_id`. That span lives in `agent_events` with content, timestamp, and latency. **Record-keeping becomes a queryable trail.** + +--- + +# Q5 — Systemic / pattern audit + +

EU AI Act Art. 17
EU AI Act Art. 60
+ +> *"Where in our portfolio does the AI reject candidates least decisively?"* + +```sql +GRAPH `

..rich_agent_context_graph` +MATCH (dp:PlanningDecision)-[:WeighedOption]->(opt:DecisionOption) +WHERE opt.status = 'DROPPED' +RETURN dp.decision_type, COUNT(opt) AS rejections, AVG(opt.score) AS avg_dropped_score +GROUP BY dp.decision_type +ORDER BY rejections DESC LIMIT 5; +``` + +| decision_type | rejections | avg_dropped_score | +|---|---:|---:| +| Creative Theme Selection | 12 | 0.79 | +| Audience Selection | 12 | **0.66** ← lowest | +| Channel Strategy Selection | 6 | 0.77 | +| Placement Selection | 7 | 0.74 | + +

Audience Selection has the lowest average dropped score. Reuse the Q2 filter for repeatable bias review.
+ +--- + + + +
Section 3 of 4
+ +# What this unlocks + +--- + +# Three takeaways for leadership + +
+ +
+ +### 1. Audit posture as a query + +The audit surface for our agent platform is now a **live BigQuery query**, not a slide deck or quarterly review. + +### 2. The schema generalizes + +Brand-neutral, channel-neutral, decision-type-neutral. Point an instrumented agent at the same pipeline and the same audit patterns apply. + +
+ +
+ +### 3. Composes with what we run + +- **BQ AA Plugin** — the instrumentation path +- **SDK extraction** — one method call: `build_context_graph(use_ai_generate=True, include_decisions=True)` +- **BigQuery** — the warehouse you already pay for + +No new graph service. No separate audit datastore. + +
+ +
+ +--- + +# Compliance-anchor map + +| Question | EU AI Act | GDPR | DSA | +|---|---|---|---| +| **Q1** Right to explanation | Art. 86 | Art. 22 | Art. 26 | +| **Q2** Bias / fairness audit | Art. 10, Art. 71 | Art. 5(1)(d), Art. 22 | Art. 26 | +| **Q3** Human oversight | Art. 14 | Art. 22 | — | +| **Q4** Reproducibility | Art. 12, Art. 13 | Art. 30 | Art. 26 | +| **Q5** Systemic-pattern audit | Art. 17, Art. 60 | Art. 35 | Art. 26 | + +Five queries against one graph create reusable **evidence hooks** for three EU regulatory conversations. This is not a compliance certification; it is the data substrate a compliance review needs. + +--- + +# How fast can we ship this? + +
+ +
+ +### Setup, on a clean GCP project +```bash +cd examples/decision_lineage_demo +./setup.sh +``` + +| Phase | Wall time | +|---|---| +| Tooling + APIs + venv | ~30s | +| Live agent (6 sessions) | 3-7 min | +| `AI.GENERATE` extraction | 30-90s | +| Rich-graph projection | ~10s | +| Render BQ Studio queries | <1s | + +
+ +
+ +### Cost per setup run +- 6 live `gemini-2.5-pro` invocations +- 2 `AI.GENERATE` extraction queries +- A few hundred BQ rows + one property graph + +**Order-of-cents for the verified demo run.** Queries against the prebuilt graph are lightweight. + +### From zero to leadership demo +**Under 10 minutes**, fully reproducible, on any GCP project. + +
+ +
+ +--- + + + +
Section 4 of 4
+ +# Where to go next + +--- + +# Open source — try it on your project + +
+ +
+ +### Repository +[`GoogleCloudPlatform/BigQuery-Agent-Analytics-SDK`](https://github.com/GoogleCloudPlatform/BigQuery-Agent-Analytics-SDK) + +### The demo bundle +`examples/decision_lineage_demo/` + +### What ships in the bundle +- `setup.sh` / `reset.sh` — one-shot bootstrap + tear-down +- `agent/` + `campaigns.py` — real ADK agent + 6 briefs +- `run_agent.py` + `build_graph.py` + `build_rich_graph.py` +- `bq_studio_queries.gql` (rendered) — six GQL blocks +- `property_graph.gql` (rendered) — recreate-from-tables DDL + +
+ +
+ +### Documentation +- [`README.md`](README.md) — orientation +- [`SETUP_NEW_PROJECT.md`](SETUP_NEW_PROJECT.md) — clean-project reproduction +- [`DEMO_NARRATION.md`](DEMO_NARRATION.md) — 5-min leadership talk track +- [`BQ_STUDIO_WALKTHROUGH.md`](BQ_STUDIO_WALKTHROUGH.md) — click-by-click in BQ Studio +- [`DEMO_QUESTIONS.md`](DEMO_QUESTIONS.md) — the 5 EU questions with verified GQL +- [`DATA_LINEAGE.md`](DATA_LINEAGE.md) — how the 7 tables produce the graph +- [`SLIDES.md`](SLIDES.md) — this deck + +### License +Apache 2.0 + +
+ +
+ +--- + +# Anticipated questions + +| Q | Short answer | +|---|---| +| **How long to deploy?** | Plugin integration, SDK call, committed DDL. **Days, not quarters**, for a pilot. | +| **Other agents we haven't instrumented?** | Same plugin, same plug-in point. Schema doesn't care which agent wrote the spans. | +| **Cost?** | Two `AI.GENERATE` calls per build + standard BQ query cost. **Order-of-cents for this verified demo run.** | +| **What if `AI.GENERATE` misses a decision?** | Build script reports per-session counts. Re-run extraction without re-running the agent. Talk track is count-agnostic by design. | +| **Does this expose PII?** | Stores trace-derived rationale text. PII handling follows the existing `agent_events` retention and access policy. | +| **Who operates this?** | The team that owns the agent platform. Plugin write path + SDK read path both already on-call. | + +--- + + + + +# Q&A + +## From agent behavior to auditable evidence + +
+Open source · Apache 2.0 · github.com/GoogleCloudPlatform/BigQuery-Agent-Analytics-SDK +
+examples/decision_lineage_demo · Issue #98 +
+ + diff --git a/examples/decision_lineage_demo/SLIDES.pptx b/examples/decision_lineage_demo/SLIDES.pptx new file mode 100644 index 00000000..d3e76d2f Binary files /dev/null and b/examples/decision_lineage_demo/SLIDES.pptx differ