How to Evaluate AI Agent Performance

Agent evaluation should reflect the way the system is actually used. That means combining offline test sets, production traces, and workflow-level success criteria rather than relying on one broad quality score.

The most useful evaluation programs separate business outcomes from technical signals. Business outcomes tell you whether the workflow is helping. Technical signals tell you why it is failing.

Core dimensions

At minimum, measure task success, grounding quality, tool success rate, latency, cost per run, and escalation rate. These metrics create a more complete picture than generic accuracy alone.

Retrieval and context checks

If the agent depends on retrieval, evaluate ranking quality, freshness, citation coverage, and context assembly. Many apparent reasoning problems start earlier in the pipeline.

Safety and policy checks

Add targeted evaluations for refusal behavior, sensitive data handling, approval rules, and domain-specific policy requirements. These checks should run before major releases and after model changes.

Production feedback loops

Evaluation should not stop after launch. Review traces, label real failures, and add those cases back into your regression suite so the system improves with operational learning.