Provy checks every agent run against the real outcome, so the whole team sees what delivered, what failed quietly, and whether the fixes held.
NewRead the Decision Assurance whitepaper →| Answered the customer’s real question | ✓ |
| Resolved without escalation | ✕ |
| Followed refund policy | ✓ |
| No duplicate actions | ✓ |
| Closed within SLA | ? |
An agent can run cleanly, pass every trace and every eval, and still get the outcome wrong. The failures that hurt are the ones that look fine. Provy reconciles each run against the real result, so those are the failures you catch first.
Runs proven against the real outcome, not just a clean trace or a passing score.
Confident, well formed, and wrong. Surfaced, ranked by impact, and attributed to the agent that caused it.
Every fix measured on the real result before and after, so a change that did nothing gets caught.
The real cost of the failures Provy caught, and what each fix actually recovered.
A reliability score per agent and per fleet that drops when real outcomes go wrong, so you know what to trust and what to watch.
Divergences grouped into patterns and marked addressable or inherent, so you fix causes, not symptoms.
When a run fails quietly, Provy names the tool and the agent behind it, even when every eval passed.
Where your agents were sure of themselves and still missed, measured against the real outcome.
Send the fix to GitHub, Jira, or a webhook, then verify on later runs that it held.
SDK or REST, any framework, any model. No rewrite of your agents. Start in shadow mode with no production risk.
Provy turns the workflow into the conditions a good run must meet, mapped to signals your agents already report.
Each run graded condition by condition, estimated against real, with the agents worth trusting ranked.