Direct answer
What is an AI agent evaluation?
An AI agent evaluation measures whether an agent completed a task well and whether its path was acceptable. It can score the final output, evidence, tool choice and arguments, intermediate trajectory, policy compliance, latency, cost, and human correction. Reliable evaluation uses reviewed cases, explicit criteria, repeatable runs, and more than one evaluator type.
Reviewed by Raltic Research · Updated August 15, 2026
The short answer first, followed by the operating details and next decision.
A repeatable evaluation ties one task to a versioned run and an explicit judgment.
- 01
Case
Input, context, constraints, expected behavior, and metadata.
- 02
Run
Versioned agent execution with outputs and intermediate steps.
- 03
Score
Code checks, rubrics, model judges, or human labels.
- 04
Compare
Baseline and candidate results on the same cases and criteria.
What should an agent evaluation measure?
Measure the result and the trajectory whenever the path affects safety, cost, or correctness.
A good final answer can still come from the wrong source or an unauthorized tool. Conversely, a correct route can fail because a dependency was unavailable. Keep outcome, evidence, tools, trajectory, policy, efficiency, and human correction visible as separate dimensions.
- 01Task outcome
- 02Evidence quality
- 03Tool use and arguments
- 04Trajectory and handoffs
- 05Policy, latency, cost, and correction
What types of evaluator are used?
Use deterministic code, model-based judges, pairwise comparisons, and human reviewers for different criteria.
Code works for schemas, required fields, exact tools, and hard policy. Model judges help with documented qualitative rubrics. Humans remain important for domain judgment, policy interpretation, and calibrating automated scores.
How does evaluation improve a production agent?
Known and newly discovered failures become versioned regression cases.
Run candidate changes against the same offline dataset before release. After deployment, sample traces and corrections, label failures, and promote representative cases back into the dataset. This joins pre-release testing with production learning.
- 01Curate cases
- 02Run a baseline
- 03Compare a candidate
- 04Monitor production
- 05Add failures to regression
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