testing-agents-with-subagents

Testing AI agents involves running them with known test inputs and verifying structured outputs against a defined test suite to detect and fix incorrect behavior before deployment. This validates schema compliance, severity calibration, and consistency.

TDD for AI agents applies a RED-GREEN-REFACTOR cycle to agent definitions. You run the agent against curated test inputs, document verbatim outputs, fix the agent definition, and re-run until all tests and consistency metrics pass.

Validate structured outputs for reviewer agents by running a minimum test suite containing known issues, clean cases, edge cases, and ambiguous inputs. This measures true and false positives, severity accuracy, and schema compliance.

Yes, TDD can stress test large inputs, ambiguous cases, and multi-agent aggregation. This ensures robustness by verifying schema compliance and consistency metrics across parallel workflows and edge-case scenarios before deployment.

Run agent test suites for schema validation during deployment and after any prompt or schema changes. This is critical when severity calibration and schema compliance matter, preventing regressions and production failures.