# How do I detect agent regressions before users complain?

Grade the content of every production turn against the call site's own baseline, and alert when the rate shifts rather than on a single bad run. The signal then comes from traces instead of tickets.

Grade what the turn was for: task completion against the end state the user asked for, groundedness on each answer built from retrieved documents, failure rate per tool. A review sample only sees failures that are both common and sampled, so [a rare one takes more sessions than a review queue gets through](/answers/silent-failures/how-many-traces-before-you-catch-a-rare-failure). And [one bad run isn't a regression](/answers/regression-detection/why-isnt-one-bad-run-a-regression): the evidence is a rate moving across two windows of comparable traffic, the same [before-and-after comparison](/answers/regression-detection/how-do-you-detect-a-regression-after-deployment) you run against a change you already suspect.

Tessary runs its classifiers on every trace, writes a finding when a score crosses its threshold, and [triage decides whether that finding becomes a case](/answers/tessary-classifiers/what-happens-when-a-tessary-classifier-flags-a-trace).

The baseline is the limit. A call site with too few comparable turns has nothing to shift against, so it stays quiet until enough traffic has run through it.

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Source: https://tessary.ai/answers/regression-detection/how-do-i-detect-agent-regressions-before-users-complain
More on Regression detection: https://tessary.ai/answers/regression-detection
From Tessary, agent reliability for AI agents in production: https://tessary.ai