I built a QA agent that audits itself nightly. It has filed 79 findings against its own releases.

Most "AI QA agents" audit your repo from scratch every time and end with LGTM. I wanted the opposite: a tester with memory, a failure taxonomy, a verdict contract, and no ability to touch the code it judges.

So in Verdict the model only judges. A stdlib harness measures everything else — timestamps, SHAs, test counts, diff coverage, a signed run history — and a validator refuses any state that claims more than was measured.

What a run looks like

A state file carries the baseline, so every run is a delta:

NEW / STILL_OPEN / RESOLVED / REGRESSED, regressions ranked first, every finding with a stable ID and an age.