Agent Evaluation — Testing Agent Behavior End-to-End

Why it matters

Untested agents drift silently. Investment in eval pays back in stability.

The architecture

Test cases: input + expected outcome (final answer, tool calls, or both).

Metrics: success rate, tool accuracy, trajectory length, cost per task.

How it works end to end

Trajectory eval: correct tools called? In right order? Efficient?

LLM-as-judge for semantic evaluation of open-ended outputs.

Regression testing in CI: catch behavior drift before it hits users.