Can AI Agents Satisfy CGMP Documentation Requirements?

Digital transformation in pharmaceutical manufacturing carries real promise for quality and efficiency, but only when validation, data integrity, and capable infrastructure keep pace with the technology, according to Andrea K. Kerrigan, MSV, Branch Chief, OGAD, CVM, FDA.1 Kerrigan laid out this foundation during her panel, Validation and Utilization of Digital Tools, delivered at the 2026 PDA/FDA Joint Regulatory Conference.

What Is CDER's Stake in Digital Transformation?

Digital tools can deliver quality and efficiency advantages, but three foundational elements determine whether that promise is realized: suitability for intended use, data integrity, and capable facilities.1 Kerrigan also flagged a subtler risk: software can encode the same cognitive biases as the humans who build it, a concern addressed in ICH's Q9(R1) training materials on quality risk management.

Why Does Data Integrity Resurface as a Compliance Failure?

Despite years of FDA guidance built around the ALCOA framework, data integrity lapses remain among the most cited violations in warning letters, noted Kerrigan,1 who quoted Dr. Carmelo Rosa's 2024 remarks at a PDA India Chapter meeting, describing testing into compliance, data alteration, and result omission as one of the most important and relevant topics currently discussed by industry and regulators from around the world. That persistence tracks with a 2016 analysis of rising data integrity citations to more recent scrutiny of falsified microbiology records.2

What Happens When AI Outputs Go Unreviewed?

Two case studies anchored Kerrigan’s compliance discussion.1 In the first, FDA investigators found that a topical drug manufacturer had used AI agents to draft product specifications, SOPs, and master production records. When confronted about missing process validation under 21 CFR 211.100, it was determined that the AI agent never told them it was required. FDA's resulting warning letter made clear that AI-generated output must be reviewed and cleared by the firm's quality unit, and that overreliance on AI without human verification itself constitutes a CGMP violation.3 The second case study centered on an OTC sterile manufacturer for whom investigators found microbial plates with visible growth swapped for clean ones and lab forms pre-filled with results before testing occurred. These violations were tied to 21 CFR 211.194(a) that triggered a CAPA requiring time-stamped photographic documentation of every sample.

How Should Manufacturers Apply Quality Risk Management to Modernization?

Kerrigan tied both case studies back to ICH Q10's pharmaceutical quality system framework, arguing that sustainable compliance depends on proactive, prevention-focused systems rather than reactive fixes.1 Opportunities cited included electronic batch records, vision-based defect detection, predictive maintenance, and AI-assisted investigations, provided each is validated for its intended use. Citing analysis from Rick Friedman, Kerrigan linked higher-capability technology to measurable gains in yield, throughput, and first-cycle regulatory approval.

What's the Bottom Line for Manufacturing Quality?

CGMP, Kerrigan concluded, is not a checkbox exercise but a lifecycle commitment to quality assurance.1 Facilities that are slow to correct excessive process variability will continue to draw heightened FDA scrutiny, regardless of how sophisticated their digital tools appear on paper.

References

1. Parenteral Drug Association. PDA/FDA Join Regulatory Conference 2026 Agenda. Available at https://www.pda.org/global-event-calendar/event-detail/pda-fda-joint-regulatory-conference-2026#agenda. Accessed Sept 17, 2026.
2. Wechsler J. FDA and manufacturers intensify concerns about data integrity. Pharmaceutical Technology. 2016;40(7):16-17. Accessed September 18, 2026. https://www.pharmtech.com/view/fda-and-manufacturers-intensify-concerns-about-data-integrity
3. Chan H. Pharma’s next AI problem is authority. Pharmaceutical Technology. Published online September 8, 2026. Accessed September 18, 2026. https://www.pharmtech.com/view/pharmas-next-ai-problem-is-authority