testing-agents-with-subagents

To test AI agents using test-driven development, you run the agent against a documented suite of known-issue, clean, edge-case, and ambiguous inputs, then iterate fixes until outputs match expected schemas. This enforces a RED-GREEN-REFACTOR cycle for reliable agent behavior.

The RED-GREEN-REFACTOR cycle for agent testing maps traditional TDD to AI workflows by requiring baseline failing runs (RED), iterative fixes to meet schema compliance (GREEN), and edge-case hardening (REFACTOR). This ensures structured outputs and severity judgments are accurate and reliable.

You verify structured outputs and severity calibration by mandating exact output capture, schema compliance checks, consistency runs across multiple executions, and severity calibration metrics. This validates that code-reviewer agents accurately assess known-vulnerable and clean test cases.

Yes, test-driven validation applies to parallel workflows by running multiple reviewers or subagents simultaneously and ensuring their findings and severity alignments match. This verifies that parallel testing across aggregation workflows produces consistent and structured outputs.

To start testing agents with TDD, you need a documented test suite containing known-issue, clean, edge-case, and ambiguous inputs. The suite must specify minimum case counts and exact output capture to validate reviewer, analyzer, decision, and planning agents.