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Track Q: AI & Automation Governance

Priority: HIGH — Required for regulatory trust in AI-driven QMS decisions Agent: responsible-ai, ai-model-integration Sprint Range: S8-S10 Reference: docs/agents/24-agent-orchestration-mapping.md, docs/agents/25-agent-orchestration-spec.md, docs/agents/26-agent-message-contracts.md

Status Summary​

Progress: 0% (0/12 tasks)

SectionTitleStatusTasks
Q.1AI Model Governance FrameworkPending0/4
Q.2Agent Autonomy & GuardrailsPending0/4
Q.3Predictive Compliance AnalyticsPending0/4

Q.1: AI Model Governance Framework​

Sprint: S8 | Priority: P1 | Depends On: C.3 Goal: Model validation, versioning, and audit trail for all AI decisions in regulated workflows

  • Q.1.1: Define AI model validation protocol
    • Training data: lineage documentation and quality assessment
    • Performance baselines: accuracy, precision, recall per task type
    • Bias metrics: decision distribution analysis by system/plant/team
    • Methodology: aligned with GAMP 5 for computerized system validation
  • Q.1.2: Implement model versioning and registry
    • Registry: model name, version, training date, performance metrics, approval status
    • Versioning: immutable model snapshots with rollback capability
    • Promotion: dev -> staging -> production with approval gates
    • Deprecation: sunset policy with migration plan
  • Q.1.3: Create AI decision audit trail
    • Fields: model version, input context, output decision, confidence score, reasoning
    • Storage: append-only audit table linked to main audit trail (D.5)
    • Retention: 7 years minimum (FDA Part 11 alignment)
    • Query: searchable by decision type, confidence range, model version
  • Q.1.4: Build model approval workflow for regulated decisions
    • Classification: which agent decisions require human-in-the-loop
    • Approval authority: Quality Head for critical decision models
    • Evidence: model validation report required before production deployment
    • Revalidation: trigger on model retrain, data drift, or performance degradation

Q.2: Agent Autonomy & Guardrails​

Sprint: S9 | Priority: P1 | Depends On: Q.1, C.3 Goal: Decision-level guardrails for agent autonomy with escalation and confidence thresholds

  • Q.2.1: Define autonomy decision matrix
    • Full autonomy: low-risk decisions (document categorization, search suggestions)
    • Supervised: medium-risk (deviation classification, CAPA prioritization)
    • Human-required: high-risk (deviation closure, regulatory submission, approval override)
    • Configuration: per-organization autonomy level settings
  • Q.2.2: Implement confidence-based human review triggers
    • Threshold: decisions below 70% confidence require human review
    • Notification: reviewer notification with agent reasoning and alternatives
    • Override: human can accept, reject, or modify agent recommendation
    • Feedback loop: human decisions feed back into model improvement
  • Q.2.3: Build escalation rules for critical decisions
    • Critical CAPA closure: always requires human approval regardless of confidence
    • Regulatory classification changes: escalate to Compliance Officer
    • Cross-tenant pattern detection: escalate to platform admin
    • Timeout: auto-escalate if no human response within SLA
  • Q.2.4: Create agent session replay and audit
    • Recording: full agent session capture (prompts, tool calls, decisions, outputs)
    • Replay: ability to replay and inspect any agent session
    • Forensics: investigation tools for incorrect or disputed decisions
    • Reporting: weekly agent autonomy rate and escalation metrics

Q.3: Predictive Compliance Analytics​

Sprint: S10 | Priority: P2 | Depends On: Q.1, L.1-L.2, C.3 Goal: ML-powered compliance prediction, benchmarking, and proactive risk identification

  • Q.3.1: Build compliance trend prediction model
    • Input: historical CAPA rates, deviation trends, audit findings, training compliance
    • Output: 30/60/90-day compliance risk forecast per organization
    • Alert: proactive notification when predicted compliance drops below threshold
    • Accuracy: target >80% for 30-day predictions
  • Q.3.2: Create compliance benchmarking engine
    • Metrics: CAPA resolution time, deviation rate, training compliance, audit score
    • Anonymized benchmarks: industry percentile comparisons (anonymized cross-tenant)
    • Reports: quarterly benchmarking report per organization
    • Privacy: aggregate-only data sharing, no individual tenant identification
  • Q.3.3: Implement AI-powered audit readiness scoring
    • Assessment: automated audit readiness score (0-100) per compliance framework
    • Gaps: specific gap identification with remediation recommendations
    • Mock audit: AI-simulated audit questions and evidence requests
    • Preparation: audit preparation checklist auto-generated per framework
  • Q.3.4: Build bias monitoring and fairness dashboard
    • Distribution: decision distribution analysis by system, plant, team, role
    • Alerts: statistical significance test for decision pattern anomalies
    • Reporting: monthly fairness report for compliance review
    • Remediation: automated retraining trigger when bias detected

Updated: 2026-02-14 Compliance: CODITECT Track Nomenclature Standard (ADR-054)