AI Governance
Regulatory Expectations for AI Agents in GMP
What FDA, EMA, MHRA, and TGA actually expect of AI agents in GxP — appropriately scoped, risk-based, transparent, human-overseen, and fully traceable.
Global regulators now recognise that AI agents and large language models play an active role in GxP environments, and their guidance is converging on a consistent principle: AI agents may support quality operations, but they cannot replace human accountability.
Where FDA, TGA, EMA, and MHRA agree
Across current guidance, draft annexes, and public statements, regulators are aligned on five core principles: AI agents constitute a computerised system under GMP; intended use remains the primary regulatory anchor; validation prioritises fitness for purpose over model internals; human oversight is mandatory for compliance-critical decisions; and traceability and data integrity remain non-negotiable. This consensus appears in FDA draft guidance on AI credibility and risk-based validation, the draft EU GMP Annex 22 on artificial intelligence, MHRA's participation in international AI principles and sandbox programmes, and TGA consultation outcomes on software and AI in regulated use.
Regulators do not expect organisations to validate or explain the internal workings of commercial AI agents. They do require control over the rationale for AI agent use, the defined tasks it performs, its data access parameters, the output review and application process, and the accountability framework around it — the same expectations already applied to ERP and QMS platforms.
Eight practical expectations
1. Define intended use clearly. Intended use is the single most important document produced for regulatory purposes — it defines the scope of deployment, sets validation boundaries, and determines the level of human oversight required. A well-formed statement is specific and bounded: "AI agents are used to assist with contextual review of GMP records. They do not approve, release, or certify data."
2. Apply risk-based validation. Validation depth should match the risk profile of the task. Regulators expect scenario-based testing across typical and edge cases, testing with representative data, subject-matter-expert review of outputs, and performance benchmarks against prior methods. They do not require mathematical proof of correctness, access to model weights, or revalidation after every vendor model update.
3. Maintain mandatory human oversight. Every major agency has stated clearly that AI agents may inform, assist, and accelerate quality work, but may not replace qualified human judgement. The distinction is between assistive use, where an agent supports a human decision, and autonomous use, where an agent makes or finalises a decision without review. Delegating a task to an agent does not transfer regulatory responsibility.
4. Prioritise evidence-based outputs. An agent output accepted without review, challenge, or traceability is indistinguishable from an undocumented decision — a data integrity risk. Treat all agent outputs as working papers, ensure every output is reviewable by a qualified person before it influences a GMP decision, and ensure all outputs trace back to the source data reviewed.
5. Maintain traceability and data integrity. Every interaction between an agent and quality data should be captured, timestamped, and linked to a human action, covering input data, timestamp, agent output, and human decision — mapping directly to ALCOA+ principles.
6. Apply segregation of duties. Regulators respond positively to architectures where no single agent both performs and approves a task. A two-agent model — one executes, a second independently reviews, a human verifies and decides — mirrors the author/reviewer patterns already embedded in pharmaceutical quality systems.
7. Set explainability expectations pragmatically. Regulators do not expect explanation of neural network internals or reproducibility of individual outputs. They do expect clear documented descriptions of the agent workflow, transparent criteria for evaluating outputs, and honest acknowledgement of known limitations.
8. Manage change control and monitoring. Prompt iterations, workflow adjustments, and data source changes belong inside existing change control. Vendor-managed model updates sit outside direct control, and the risk from those changes is mitigated through human verification, periodic output review, and defined escalation — not by attempting to control what cannot be accessed.
Preparing for inspection
Auditors typically ask why AI agents are used in a given process, how reliability is validated, who verifies outputs, what the contingency is for errors, and whether the organisation can demonstrate a concrete example. They generally avoid probing training methodology, internal algorithmic function, or vendor-selection rationale — the focus is on governance, not technical architecture.
A regulatory-safe position statement, consistent across FDA, TGA, and EMA/MHRA expectations, reads: "We use AI agents as a controlled, assistive tool within our quality system. Human authority drives all compliance-critical decisions, ensuring full traceability and oversight." Adopting AI agents under that governance model — retaining full accountability, anchoring use in human oversight, and managing the governance rather than the technology — is what regulators across jurisdictions are converging on.