Agent Quality Infrastructure
Automated baseline measurement for autonomous AI agents. Run cohorts, compare outputs, catch regressions. Quality isn't a feeling. It's a number.
baseline consistency: 94.2% across 16 runs
01
Run controlled agent cohorts against known inputs. Establish ground truth for what "correct" looks like, then measure everything against it.
02
Catch quality degradation the moment it happens. Model updates, prompt changes, infrastructure shifts. If output quality moves, you know immediately.
03
Side-by-side output comparison across agent versions, model providers, and configuration changes. See exactly what changed and whether it matters.
04
Block deploys that drop below your baseline threshold. Automated pass/fail before agent changes reach production. No more "it seemed fine."
Submit known-good agent outputs as your quality reference. These become the standard every future run is measured against.
Execute your agent against the same inputs on a schedule. Each run produces a quality score relative to your baseline.
Get notified when quality drifts. See trends over time. Know exactly which run introduced a regression and what changed.
Production quality measurement for teams that ship autonomous agents and need to know they still work tomorrow.