Resource
TDD with AI coding tools
Test-driven development gives AI-assisted development a visible contract: expected behavior, regression protection, and a safe way to judge whether generated changes are acceptable.
The point is not to compare tools. The point is to keep agentic coding workflows bounded, testable, reviewable, and separated from release decisions.
Purpose
Use tests as the contract before generated code is trusted
TDD helps a team define expected behavior before implementation, protect existing behavior before change, and keep human acceptance grounded in evidence.
AI coding tools can accelerate implementation, but they do not remove the need to define behavior, run validation, inspect the diff, and decide whether the result fits the task. TDD makes that judgment visible.
TDD with AI
Adapt the test cycle to agent-assisted implementation
The test cycle still matters, but the agent changes where scope, proof, and review boundaries need to be explicit.
Why TDD matters more with AI-generated code
AI-assisted implementation can produce plausible code before anyone has stated the behavior clearly. TDD gives the work a visible contract: expected behavior, regression protection, and a way to judge whether generated changes are acceptable.
Red / green / refactor adapted to AI workflows
The red step names the behavior or regression before implementation. The green step lets the agent make the smallest passing change. The refactor step stays bounded so cleanup does not become an unrelated rewrite.
Regression-first changes for existing behavior
When an existing route, API, or workflow changes, protect the current behavior before asking the agent to modify it. A regression test turns an assumed contract into evidence the team can rerun.
Smoke tests for UI routes and buyer paths
UI work often fails through broken routes, missing links, invisible states, or copy that no longer matches the buyer path. Smoke tests should prove the route renders, the main headings appear, and the next-step links still point where intended.
Validation command discipline
Each task should name the commands that must pass before acceptance. Lint, build, unit tests, route tests, and focused E2E checks are evidence gates, not optional cleanup at the end.
Agent boundaries and task limits
Codex, Cursor, Claude-style, and similar agentic workflows need bounded prompts, clear exclusions, and explicit stopping points. The agent should not expand scope simply because it can generate more code.
Human review ownership
AI can help write tests and implementation, but a human owner remains accountable for accepting the behavior, reviewing the diff, judging risk, and deciding whether the task is ready to merge.
Failure modes when tests lag behind generated changes
When tests trail implementation, generated code can hide route drift, weakened validation, accidental config changes, broken buyer paths, and behavior that looks complete only because nothing checked it.
AI workflow adaptation
Keep generated implementation behind validation coverage
A useful AI coding workflow turns tests into acceptance evidence before the generated diff becomes the new baseline.
- Define the behavior before asking the agent to modify code.
- Keep changes small enough for human review.
- Run tests and build checks before accepting generated output.
- Use regression tests before modifying existing behavior.
- Avoid letting AI-generated implementation outrun validation coverage.
- Preserve human responsibility for acceptance.
Connect TDD discipline to the WinMedia audit path
If tests are missing, generated diffs are too large, or validation commands are unclear, use the related resources before moving deeper into implementation or intake.
Boundaries
What TDD with AI coding tools does not prove
Tests support disciplined acceptance, but they do not turn generated code into certification, deployment approval, or a replacement for accountable human review.
- not a guarantee of production readiness
- not a security certification
- not proof of live email delivery or production intake acceptance
- not a substitute for human review
- not a request for passwords, API keys, private keys, service-account JSON, or production credentials through intake or prompts
- not deployment approval; deployment remains a separate human-governed decision