
Agentic AI in GxP environments: A practical guide
What does it take for AI agents to do useful work in a GxP environment, and what controls should a quality organization expect?
Guides and posts for the people who run pharmaceutical operations.

What does it take for AI agents to do useful work in a GxP environment, and what controls should a quality organization expect?
What does it take for AI agents to do useful work in a GxP environment, and what controls should a quality organization expect?
How can pharmaceutical teams assemble evidence, investigate possible causes, and prepare a traceable deviation record faster?
How can pharmaceutical teams turn operational documents into accurate, structured, source-linked data that holds up in regulated work?
A scanned batch record can stand in for the paper original only if it is a true copy: a copy verified, by a dated signature or a validated process, to preserve the original's full content and meaning, including the metadata needed to reconstruct the activity. FDA's 2018 data integrity guidance and MHRA's 2018 guidance both set this expectation, and 21 CFR 211.180 allows GMP records to be kept as true copies. A true copy is still an image, though; digitizing its contents into data is a separate step with its own integrity requirements.
Digitizing a supplier certificate of analysis (CoA) means extracting the material, lot, tests, specifications, results, and methods into one schema, with each value linked to the certificate it came from, whatever layout the supplier used. The steps are to define one schema for every supplier, map each supplier's terms to it, extract, check the results against the specification, and review anything flagged. Structured certificate data makes it practical to trend a supplier's results across lots and to support the verification of supplier data that 21 CFR 211.84 requires.
Digitizing a paper batch record means reading every value on the executed record, including handwritten entries, corrections and signatures, and loading into a defined schema, with each value linked to the location it came from. The steps are to define the schema, capture a good image, extract, check against rules, have an expert review flags, and deliver the approved data. Keeping the link to the source at every step is what makes the result usable in regulated work.
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