Most teams ship agent fixes and hope they work. Converra measures the targeted failure in real production conversations before and after the change and tells you whether the fix actually landed.
Teams diagnose agent failures, write fixes, maybe test them — and then deploy. After that? Silence. Did the fix work? Did it make things worse? Did something else change at the same time?
Without production verification, every deployment is a guess. With it, every deployment is a data point.
Every deployed fix gets one of three verdicts based on real production data. No ambiguity.
The targeted failure stopped in production: at least 15 diagnosed conversations after the deploy and zero recurrences of a failure that occurred before it.
The fix didn't reduce failures: the targeted failure recurred at least as often after the deploy as before. The issue stays open and your team is notified.
Another change touched the same agent during measurement, so the result can't be credited to this fix: the prompt was edited outside the deployed fix, or another merged change touched the same prompt.
Converra counts the specific failure pattern the fix targets in real production conversations before the change, using the same diagnosis it will use afterward.
The fix ships with a deployment marker. Converra knows exactly when the change went live and which failure pattern it targeted.
Over the following days, Converra measures the same failure pattern in new production conversations. Same detection criteria, same scoring — the only variable is the fix.
Each fix is marked verified, not fixed, or confounded, with the conversation counts behind the verdict. Until there is enough evidence, it shows as monitoring, likely fixed, or improving.
When you know which fixes worked, each cycle starts from proof — not hope. The system learns what actually moves the needle for your agents and doubles down.
A “not fixed” verdict is as valuable as a “verified” one. It tells you the root cause diagnosis was wrong and triggers re-analysis with new evidence — instead of assuming the problem is solved while it persists.
“We deployed 6 fixes last month. 3 verified. 1 didn't work, so we're re-diagnosing. 2 were confounded by other changes to the same prompt.” That's a report your CEO can act on.
Production verification means measuring whether a deployed change to an AI agent actually improved performance — using real production conversations, not simulation scores. Most teams deploy fixes and hope they work. Production verification proves it, or flags that it didn't.
Monitoring tells you your overall error rate changed. Production verification isolates a specific fix and measures its impact on the specific failure pattern it targeted. It's the difference between 'errors went down' and 'this fix reduced routing failures by 74%.'
The issue stays open, the fix keeps being checked as new conversations arrive, and your team is notified by email or webhook. A not-fixed result usually means the cause sits in a different layer than the fix changed, so the next step is to re-diagnose. If a previously verified fix regresses, Converra can roll it back automatically when you enable automatic rollback.
When the prompt is edited outside the deployed fix, or another merged change touches the same prompt during measurement, Converra marks the result confounded rather than claiming credit. Converra does not yet detect traffic-mix shifts or model-version changes automatically, so check those yourself; the guide to verifying a fix after deployment shows how.
It depends on conversation volume. For a fix Converra ships, a verdict needs at least 5 diagnosed conversations after the deploy, and verified needs at least 15 with no recurrence. For a change your team merges, Converra compares 7 days before and after the merge and needs at least 10 diagnosed conversations on each side.
Yes. Every verification verdict links to the specific conversations that contributed to the before/after measurement. You can inspect individual conversations to understand why the fix worked or didn't.
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