AI agent evaluation readiness checklist for production teams

An AI agent is evaluation-ready when a team can define success, reproduce a run, score the behavior, explain a failure, and decide whether a candidate is safe to release. A pile of example prompts is not enough. Readiness requires aligned product criteria, trace data, datasets, evaluators, and ownership.

Use this checklist before investing in dashboards or large evaluation runs.

1. Success is defined in observable terms

• The agent has one or more named user outcomes.
• Each outcome has a pass condition a reviewer can apply consistently.
• Quality dimensions such as correctness, safety, and tone are separated.
• Resource constraints cover latency, token use, cost, and maximum steps.
• The team has documented behaviors that must never occur.

“Helpful” is not a release criterion. “Resolves the support request using only approved policy and does not claim an action succeeded before the tool confirms it” is much closer.

2. Runs are reproducible and traceable

• Every agent run has a stable trace ID.
• Model calls, retrieval, tools, and subagents appear as child observations.
• Inputs, outputs, model names, token usage, latency, and errors are recorded.
• Prompt and application versions are attached to the trace.
• Sensitive fields are redacted before export.
• Multi-turn runs share a session identifier.

Without this data, a failed score tells you only that something went wrong. With it, you can locate the exact tool call or context decision responsible. Start with nested LLM traces and the guide to sessions and users.

3. The evaluation dataset represents reality

• Examples cover the main user intents in production proportions.
• Edge cases include ambiguity, missing data, tool failure, and long context.
• Adversarial and safety cases are included where relevant.
• Examples come from both expert design and sanitized production traces.
• Training, tuning, and held-out test slices are separate.
• Every example records its source and review status.

A dataset of happy paths can validate a demo, not a production system. Begin with a small set that experts trust, then use production traces to discover missing failure classes. Do not add only spectacular failures; sampling should retain ordinary traffic so aggregate results stay meaningful.

4. Evaluators are calibrated

• Deterministic checks handle schemas, tool arguments, citations, and rules.
• LLM judges use narrow rubrics and structured outputs.
• Human labels exist for a calibration set.
• Judge agreement is measured by criterion and segment.
• Uncertain cases can be routed to human review.
• Evaluator name, prompt, model, and version are stored with each score.

The goal is not maximum automation. It is trustworthy measurement. Use code for facts code can verify, human review for high-stakes ambiguity, and LLM judges for semantic criteria that have been calibrated. See deterministic versus LLM-judge metrics.

5. Experiments are comparable

• Baseline and candidate run on the same examples.
• Model, temperature, tools, and environment are pinned unless tested.
• Stochastic tasks use repeated trials.
• Quality, cost, and latency are compared together.
• Regressions can be opened as full traces.
• Experiment metadata is saved for reproduction.

One average score is not enough. Segment by intent, language, tool path, and risk level. A candidate that improves common requests while breaking account deletion is not ready.

6. Release and production loops exist

• The team has explicit blocking thresholds for critical metrics.
• A named owner reviews failed release gates.
• Online evals sample production traffic after release.
• Alerts distinguish quality drift from operational failures.
• Failed production traces can become dataset examples.
• Rollback and incident procedures are documented.

Offline evals answer whether a change should ship. Online evals answer whether it still works on real traffic. Both belong in one system so the evidence can move from production to dataset to experiment without losing context.

A practical readiness standard

You do not need thousands of examples to start. A production team is ready when it can run a small trusted suite, explain every failure through a trace, and make a repeatable release decision. Currai connects the trace and evaluation layers; the AI agent evaluation stack shows how to expand that foundation without creating disconnected quality tooling.