Every team shipping AI-generated code has the same quiet fear: something in production isn’t doing what the report said it was doing. Almost nobody has an answer. I have a documented one — and the field notes below are all real defects from a live system I run, each one caught by the method I’d install for you.
A vendor silently changed a field's type. The page kept returning HTTP 200 with a full-size body while a nested component threw on every single load. Five weeks.
Status codes and response sizes cannot tell you a page works. Only rendered output can.
A team-strength table held 688 current rows and looked perfectly healthy. Every row's foreign key was NULL, and the model joined on that key — so one of the strongest predictors in football silently resolved to "missing" on every prediction the system had ever made.
A populated table is not a working feature. Verify what the consumer actually receives, not what the producer wrote.
Rows with no value were counted as wrong answers rather than excluded. The published accuracy figure was both wrong and confidently displayed — while a sibling metric on the same page contradicted it.
Every displayed number needs a provenance and a population. Aggregates that quietly swallow NULLs lie in a specific, detectable way.
A backfill logged "completed" every hour for weeks. It requested 1,200 rows; the API capped responses at 1,000; the code read a short batch as "reached the end" and reset its cursor to zero. It re-processed the same first 1,000 records forever and never reached the broken ones.
Success logs measure that code ran, not that work happened. Assert on the change you expected in the data.
The status panel read "All systems nominal." Two pipelines behind it had been idle for 59 and 97 days, both showing a green COMPLETED badge — because the badge printed the last stored status while the freshness check lived somewhere else entirely.
Status must be derived from reality, not declared. The instrument that reports on itself is the one that fails silently.
None of these were visible to standard checks. Each one shipped, sat in production, and looked fine from the outside — which is exactly the failure mode that matters.
I go through your AI-assisted codebase and pipelines and find where you're trusting completion reports instead of verified outcomes. Where a 200 can hide a failure. Where a displayed number comes from a model instead of a source of truth. What has never once been checked against reality.
You get: A prioritised findings document, plus a verification standard your team adopts.
I install the methodology rather than just report on it: a three-gate definition of done, rendered-output verification, instrument-versus-reality integrity checks that run nightly and flag their own drift, provenance on every published number, and two-lane autonomy for AI agents.
You get: A working verification layer, and the team trained to maintain it.
Standing oversight for a team scaling AI-assisted development. I'm the person making sure velocity doesn't quietly become invisible debt — reviewing what shipped, checking it did what it claimed, and escalating what didn't.
You get: A standing review cadence and a clear escalation path.
If your team is shipping AI-generated code and you’re not certain what’s actually working in production, that uncertainty is the risk I remove. Twenty minutes, and I’ll tell you what I’d look for in yours.