One good demo proves nothing — you already know that, and so do your buyers, your investors, and their security teams. We turn your agent's real workload into a golden test suite of ~20 cases, run every case 7 times, and hand you a statistical reliability report anyone can check: pass rates with real confidence intervals, a map of what breaks, what flip-flops between runs, and a regression gate that runs before every release.
Every number ships with a Wilson 95% confidence interval — never a bare percentage.
Per-case verdicts: stable_pass, stable_fail, or flaky.
Raw outputs and judge transcripts included — delivered to your team under your data policy —
so every claim is checkable.
Which request types your agent gets wrong (refunds? cancellations? out-of-policy asks?), which answers change between identical runs, and — if you're mid-iteration — which regressions appeared after a prompt or code change.
The ~20-case YAML suite built from your real workload is yours to keep — plain, portable YAML, readable by any harness. Re-run it through 7runs whenever you ship a change; and because the suite is yours, there is no lock-in even if you never talk to us again.
A concrete "run this before you ship" checklist wired to the suite: which cases must stay green, what pass-rate floor to enforce, and how to compare two runs so real regressions turn CI red and statistical noise doesn't.
Workload interview. One call. We pull ~20 representative requests from your real traffic — the routine ones, the edge cases, the ones that must never go wrong.
Suite construction. Each request becomes a test case with an explicit pass rubric — deterministic checks where possible, double-judged LLM rubrics for the fuzzy parts.
Runs. Every case, 7 repetitions, against a controlled endpoint of your agent. Wrong answers are never retried — that would corrupt the statistics. Inputs and redacted outputs are retained only per the policy we agree on.
Report + walkthrough. The full report, the suite, the release gate, and a call to go through what we found and what to fix first.
We build 7runs, the measurement engine this audit runs on — and we published a reproducible three-wave benchmark of popular open-source code-review agents using exactly this methodology: 336 executions, every check judged twice, raw records public. The method you're buying is the method you can inspect.
Not a certification, and not a rubber stamp — if your agent is flaky, the report will say so, with numbers. All results are whole-system measurements (your prompt + your scaffolding + your model), and small suites mean honest, visibly wide confidence intervals rather than false precision. If we think the audit won't tell you anything useful, we'll say that before taking your money.
Tell us what your agent does and roughly what a bad day looks like. We'll reply with whether the audit fits, and if it does, a start date.