Every competitor ships fixes and hopes they work. Converra measures before/after from real production data 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 fix reduced failures in production. Before/after comparison shows statistically meaningful improvement.
The fix didn't reduce failures. The problem persists at the same rate or worsened. Flagged for re-diagnosis.
Too many variables changed simultaneously to attribute the result. Other deployments, traffic shifts, or model updates may have affected the outcome.
Before deploying a fix, Converra captures the failure rate for the specific pattern being addressed. This uses real production conversations from the past 7-30 days.
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 actual conversation counts and failure rates that support the verdict. Full transparency.
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, reducing failures by 74%. 1 didn't work — we're re-diagnosing. 2 confounded by a model update.” 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 failure pattern is re-queued for diagnosis. Converra re-examines the root cause with the additional data from the failed fix — what it tried, why it didn't work — and generates a new variant targeting a different aspect of the problem.
When multiple changes ship simultaneously or external factors (traffic shifts, model updates) cloud the result, Converra marks the fix as confounded rather than claiming false credit. It waits for a cleaner measurement window or isolates the variable in the next deployment.
It depends on conversation volume. High-volume agents (1,000+ conversations/day) can get verified results within 24-48 hours. Lower-volume agents may take 5-7 days to accumulate enough data for a meaningful comparison.
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.
Related: Simulation Testing · Regression Testing · How It Works
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