Agent evals in production: a minimal system that works
Production agent evals combine a fixed offline set of hard cases with online signals from real traffic — containment, escalation quality, and task success — reviewed on a weekly cadence. Without that loop, prompt changes are guesswork.
- AI Agents
- Evals
- Production
Offline: freeze the hard cases
Keep a versioned set of transcripts or chat turns that previously failed: ambiguous intents, policy edge cases, tool failures. Every prompt or model change must beat or match the last score on that set.
Online: measure what the business cares about
Track task success (booked, resolved, qualified), forced escalations, and user repeats. Pair quantitative metrics with spot review of low-confidence sessions.
- Confidence thresholds that trigger human handoff
- Sampling of failed and low-rated sessions
- Regression alerts when success rate drops
Handoff is a feature
The best agents know when to stop. Escalation with full context to a human is a product win, not a model failure.
If you need a studio that builds agents with evals and handoff included — not as phase two — that is the core of how Doers Studio works.
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