You’re good at this. You’ve put in the reps, you know how to steer the model, your output is genuinely better than it was a year ago. And you still can’t answer one question honestly: is the system getting better, or are you supplying the intelligence again every single week? That gap — between competence and compounding — is exactly what goes unmeasured.

What is the Agent Audit?

The Agent Audit is a two-week measurement engagement that instruments your AI work against four research-validated key risk indicators, then shows you — from your own git-log evidence — whether your improvements actually compound month-to-month or vanish by Wednesday morning.

It replaces intuition about your AI system with data about it.

Who is it for?

Operators who are good at AI and frustrated by it. You’re not bad at AI — that’s the problem. Your competence absorbs the friction, so the honest answer to “is this compounding?” never surfaces.

Competence doesn’t protect you here. When an AI suggestion was wrong, experienced radiologists’ diagnostic accuracy fell from 82.3% to 45.5% (Dratsch et al., Radiology, 2023) — expertise reduced the over-trust effect but didn’t eliminate it. The Audit is for the practitioner who wants the answer measured against evidence, not read off a feeling.

How does it work?

  • Instrumentation, not opinion. A privacy-first, open-source binary measures your actual AI work against four key risk indicators.
  • Your own evidence. The analysis is grounded in your git log: what you shipped, how it changed, where standards held and where they slipped.
  • A personalized blueprint. You receive an architecture blueprint mapped to your specific gaps — the infrastructure that would make your gains persist.
  • Anchored to your One Belief. Before purchase you articulate the one belief about your AI work you most want tested; the engagement measures against it.

What does it measure?

Whether your AI work compounds. Four risk indicators surface whether the standards you set persist across sessions or get re-taught indefinitely — the Operator Tax you’re paying — and the architecture that would stop it.

The Audit measures the gap; the build closes it. See whether your AI work compounds: curiochat.ai/audit.