Testing AI Agents: Unit Tests, Integration Tests, and Eval Harnesses

Testing AI agents requires a fundamentally different approach than testing deterministic software. You cannot assert exact string matches — you need to evaluate quality across a distribution of outputs. The testing stack has four layers. Unit tests for tools: every tool the agent can call should have deterministic unit tests covering the happy path, error cases, and edge cases — this layer uses standard testing frameworks. Integration tests for agent behavior: run fixed inputs through the full agent loop and verify that outputs meet quality criteria (correct intent classification, appropriate tool selection, no policy violations). Regression tests: maintain a golden dataset of 100-500 real interactions with labeled correct responses. Run this suite on every prompt change and model update. Eval harnesses for continuous monitoring: production traffic sampled and scored by a separate evaluation model, with alerts when quality metrics drop. LangSmith and AgentOps both provide eval pipeline tooling. Build your testing infrastructure before your first production deployment, not after.