Last updated: 2026-05-11
How to Evaluate AI Agents
Agent evaluation should cover traces, tool calls, retrieved evidence, final outcomes, cost, and latency.
Definition
An agent eval is a repeatable check over a full run, usually combining deterministic assertions, human review, and model-judged rubrics.
Why it matters
Agents fail in the middle, not just at the answer. Evaluating only text output misses bad tool calls and unsafe state changes.
Problems it solves
- Prompt and model regressions
- Tool-call safety failures
- Retrieval and grounding drift
Common misconceptions
- Generic benchmarks rarely represent your product workflow.
- LLM judges need calibration against real failures.
- Cost and latency are quality dimensions for agents.
Minimal example
Create 20 real tasks, store expected constraints, run the agent with tracing, then score outcome, tool path, evidence, cost, and latency.
Next step: Turn production failures into eval cases before adding more autonomy.