Drill Weekly: Turning Readiness Into a Standing Eval Suite
Chaos engineering for the document layer.
Confidence: hypothesis. The failure mode below is drawn from widely reported patterns across regulated industries, not firsthand deployment. The standing eval suite at the end is not shipped anywhere I know of. It is an unbuilt design. Argue with it.
You inherit a service with no test suite. It runs. Nobody has broken it visibly, so “runs” and “correct” have quietly become the same word. Then a real enterprise customer's security team asks for a walkthrough of your incident response process, and you spend two weeks building a paper trail for a system that was supposedly fine the day before. Everyone survives the audit. Nobody asks why it took two weeks to prove something that should have been provable in an afternoon.
That two-week scramble has a name in every regulated industry: audit prep. And it is the same failure mode software engineering solved for itself twenty years ago, then apparently forgot applies outside its own codebase.
The clinical version of the same story
In clinical drug development, the scramble has a formal counterpart: the regulatory inspection. A regulator's inspector — someone from the FDA, the EMA, or an equivalent national authority — sits down with a sponsor (the company running the trial), a clinical site, or a contract research organization, and starts asking for evidence. Show me the delegation log. Show me this patient's dose change and everything that led to it. Show me how this problem was found, escalated, and fixed.
The evidence is supposed to live in the trial master file, or TMF — the document repository that's meant to contain the trial's entire story: protocols, approvals, training records, correspondence, decisions. Good Clinical Practice, or GCP, is the rulebook that governs how that story gets written and kept. An inspection is, functionally, a retrieval exam: the inspector's question is a query, the organization is the database being queried, and the score is precision, latency, and whether the story holds together across three different people asked the same thing on three different days.
This pattern is one piece of a longer treatment. The full essay is issue 33 of Stage × AI, a series walking the entire clinical-trial lifecycle stage by stage — what each stage really does, where AI helps, where it must not go, and one buildable pattern per stage:
full essayEvidence in, evidence out. Corrections welcome.