Evals from your production failures
Know whether your last change made the agent better or worse.
Zoroval builds evals from the failures your agent actually produces in production, then re-checks every release against them. A regression surfaces as a number that moved, not a customer complaint.
For teams past the demo stage. Now onboarding a limited number of design partners.
Every failure mode, counted, every day
Dashboard
Latest evaluation day · 14 Mar 2026Error rate by failure mode
Each failure mode is scored by a judge calibrated against your team's own labels, on every trace, every day.
How it works
Mine
We read your production traces and surface the failure modes your agent actually exhibits, grounded in your data rather than a vendor's checklist.
Codify
Those failures become a taxonomy specific to your team, with judge models calibrated against human labels, so the evals are ones you can trust.
Guard
Every change you ship is re-checked against that taxonomy, so you know whether a release made things better or worse before your users do.
What you get
From dashboard signal to the exact traces your engineers need to fix.
Every monitored failure mode, how often it fired, and the exact traces behind each number, written up so it can go straight to the team that owns the fix.
Top agent errors
277 production traces analysed · 11–14 Mar 2026
| # | Failure mode | Flagged | Rate |
|---|---|---|---|
| 1 | Hallucinated external fact | 198 / 277 | 71% |
| 2 | Wrong output format | 86 / 277 | 31% |
| 3 | Ignored gate failure | 23 / 277 | 8% |
trace 4e6393a6d1b8
Output cited a study and two named sources with no supporting material anywhere in the trace.
Who this is for
Teams running LLM agents in production (LangChain, LangGraph, or custom stacks) who are past the demo stage and now debugging failures they can't name, count, or catch before shipping.