When I Entrusted TDD to an AI Agent

Description and use

The author implemented an autonomous TDD cycle with an LLM, preserving test and architectural control while reducing manual intervention and message noise.

Problem

Relying on AI agents for TDD risked losing control over test-driven development flow and architectural integrity.

Outcome

The autonomous TDD skill reduced author friction, maintained test‑driven flow, and enforced architectural boundaries through checkpoints and quiet output, preventing implementation‑detail loops and fragile test suites.

Workflow

• 1. Author approves next behavior.
• 2. Agent writes failing test and explains expected failure.
• 3. Agent implements minimal production code to pass test.
• 4. Agent evaluates refactoring opportunity and runs relevant tests.
• 5. Agent pauses for author approval before next cycle (or continues autonomously).
• 6. Agent performs vertical checkpoints every two cycles to ensure progress on public feature path.
• 7. Agent emits only meaningful events in quiet mode (unexpected failures, architectural decisions, significant refactorings, final summary).

Evidence

The agent would: 1) write a comment describing the intention of the smallest test it wanted to implement; 2) wait for my approval; 3) implement the test and explain how it expected the test to fail; 4) wait for approval again; 5) implement the minimum production code necessary to make the test pass; 6) wait before refactoring or moving to the next behavior.

Source

I created another variation called tdd‑strict‑autonomous. This version no longer stopped after every completed cycle. It continued automatically through red, green, and refactor, pausing only when it encountered an exceptional situation such as ambiguous behavior, an unexpected failure, an architectural decision, or an environment problem.

Source

The agent still produced a large volume of messages. Detailed output had initially helped me trust the process. Once the agent became autonomous, however, the same volume made it harder to notice the information that actually mattered.

Source