P244: Governing the Autonomous Pharma: A Framework for Agentic AI Compliance Across FDA, EU AI Act, and MLR/PRC Operations
Poster Presenter
Atika Kumar
AI & Digital Strategy Advisor
WiZTREE Consulting United States
Objectives
To propose a practical governance framework for agentic AI in regulated biopharma environments, addressing compliance gaps across FDA AI credibility standards, EU AI Act high-risk obligations, and Medical Legal Review/ Promotional content review (MLR/PRC) promotional review workflows.
Method
Framework developed through analysis of FDA's 2025 AI credibility draft guidance, EU AI Act high-risk provisions (Aug 2026 deadline), NIST AI RMF, and observed governance gaps in biopharma commercial and regulatory operations.
Results
Agentic AI — systems that autonomously plan, reason, and execute multi-step actions — is no longer a future-state concept in biopharma. In December 2025, the FDA deployed agentic AI capabilities across all agency employees, including tools that autonomously navigate regulatory workflows. The European Commission's AI in Science Strategy similarly identified agentic AI as a priority for pharmaceutical development. Yet industry governance frameworks have not kept pace.
Analysis reveals four critical compliance gaps when agentic AI is deployed in regulated biopharma settings:
Human-in-the-loop breakdown: Existing GenAI governance assumes human review at each output stage. Agentic systems execute sequences of actions autonomously, rendering traditional review checkpoints structurally inadequate under 21 CFR Part 11 and ICH GCP (E6 R3) audit trail requirements.
MLR/PRC exposure: When agentic AI autonomously drafts, routes, and revises promotional content, standard Medical-Legal-Regulatory review processes lack defined intervention points. No industry-wide guardrail standard currently exists for agentic promotional workflows.
Regulatory divergence: FDA's risk-based AI credibility framework, the EU AI Act's high-risk classification (with fines up to €35M or 7% of global turnover), and UK GDPR impose materially different validation, traceability, and human oversight requirements — creating a compliance patchwork for global biopharma organizations operating across all three jurisdictions.
Model traceability gaps: Agentic systems generate decision chains across multiple model calls. Current model card and lineage documentation practices were not designed to capture multi-agent reasoning paths, creating audit readiness exposure.
Conclusion
This analysis proposes a four-pillar governance framework specifically designed for agentic AI in regulated biopharma environments:
Pillar 1 — Policy and Control Library: Organizations must extend existing AI policy frameworks to define agentic-specific controls, including scope boundaries for autonomous action, escalation triggers requiring human intervention, and change control procedures aligned to NIST AI RMF and internal GxP quality systems.
Pillar 2 — Regulatory Alignment: Governance controls must be mapped against three parallel frameworks simultaneously: FDA's AI credibility standards and 21 CFR Part 11; EU AI Act high-risk obligations (full enforcement August 2026); and UK GDPR-aligned oversight requirements. A unified control matrix prevents duplicative compliance work while ensuring no jurisdictional gap.
Pillar 3 — MLR/PRC Guardrails for Autonomous Workflows: Defined intervention checkpoints must be embedded within agentic promotional content workflows, including label/PI alignment validation, fair balance verification, and claims substantiation before any AI-generated content enters the review queue. Human sign-off cannot be assumed — it must be structurally enforced.
Pillar 4 — Audit-Ready Traceability: Agentic systems require multi-step decision logging, model lineage documentation, and incident response runbooks that capture the full reasoning chain — not just final outputs. Inspection-ready traceability for agentic AI means regulators can reconstruct every autonomous decision that contributed to a regulatory submission or safety signal.
Organizations that build these four pillars now will be positioned ahead of the August 2026 EU AI Act enforcement deadline and the FDA's evolving expectations for sponsors whose submissions are increasingly reviewed by AI systems. Governance cannot be considered a back-office function any longer , it is the foundation on which regulatory credibility is built.
