Prove your agents are safe, continuously.
Every agent is evaluated against golden scenarios, so you can promote autonomy on evidence and catch a regression before it reaches production.
Autonomy you cannot measure is autonomy you cannot trust. Agent evals run each agent against a suite of golden scenarios, scoring quality and safety, and gate any change behind a regression check. It is how an agent earns the right to act on its own, and how the platform's own self-improvement stays safe.
What you get.
A curated suite exercises each agent on the cases that matter, including the edge cases and the ones it must refuse.
A change to an agent is shadow-evaluated against history and blocked if it regresses.
Promotion from advisory to autonomous is driven by a measured track record, not a hunch.
Evals score not just quality but safety, so an agent that would take an unsafe action is caught before promotion.
Three moves.
Curate golden scenarios with expected findings and safe outcomes.
Run each agent against the suite and score quality and safety.
Block regressions, and let a proven track record drive earned autonomy.
The part competitors leave out.
- Evals are wired to earned autonomy, so evaluation has teeth, not just a dashboard.
- Safety is scored alongside quality, not assumed.
- The same harness governs the platform's self-improvement, so it can never regress itself into production.
Frequently asked.
The platform ships a starting suite, and your domain experts add the cases that matter to your operation. Realized incidents can become new scenarios.
An agent is promoted from advisory to autonomous only by a measured track record against the evals, and any change that regresses is blocked.
See it on your own operations.
Book a working session with our team, or start with a free Operations X-Ray of your systems.