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Use a short red–green–refactor loop: first have a coding agent write a test for one observable behavior, then run it and confirm it fails for the intended reason. Next, ask for the smallest implementation that passes; refactor only while the tests stay green. Review the test before implementation and the code diff afterward. A passing test shows that its assertions passed—it does not prove every requirement or regression is covered.
What test-driven development asks the agent to do
Test-driven development (TDD) puts a behavior test before the code intended to satisfy it. The familiar sequence is red, green, refactor:
- Red: Write a test for a specific expected behavior and confirm it fails because that behavior is missing.
- Green: Make the smallest change that causes the test to pass.
- Refactor: Improve the code without changing the tested behavior, rerunning tests to catch breakage.
With a coding agent, the key is not merely asking it to “use TDD.” Make the order visible, keep each change small, and pause to inspect the test before it becomes the target for implementation.
Set a baseline and define one behavior
Before editing, ask the agent to identify the project’s test framework, where tests live, the usual test command, and a representative test. If practical, run the existing relevant tests first. That baseline helps distinguish an old failure from one introduced by the change. Microsoft’s VS Code guide to testing existing code recommends this initial orientation.
Then give the agent one behavior and clear acceptance criteria. For example, instead of “improve date handling,” specify what input should be accepted, what result is expected, and what should happen at a relevant boundary or on invalid input. Avoid asking for a broad feature and a test suite in one undifferentiated step.
Run a reviewable red–green–refactor loop
- Ask for the test first. Tell the agent to add a behavior-oriented test for the stated acceptance criteria, without implementing the feature. Review whether the assertion actually expresses the requested outcome, including relevant edge cases.
- Run the test and inspect the failure. Confirm it fails because the behavior is absent—not because of a syntax error, a broken environment, or an unrelated pre-existing failure. Microsoft’s VS Code TDD guide advises reviewing an AI-generated test to ensure it fails for the right reason.
- Ask for the minimum implementation. Once the red test is credible, have the agent make the smallest code change that satisfies it. Run the test again and inspect both the result and the changed files.
- Refactor in small steps. If cleanup is useful, ask for it separately and rerun the relevant tests immediately. A refactor should preserve the behavior, not quietly expand scope.
- Review before moving on. Inspect the diff, look for missing error or boundary cases, and run the broader relevant test suite when appropriate. Then repeat with the next behavior.
These checkpoints matter because an agent can write a test that encodes the wrong requirement, checks an implementation detail rather than user-visible behavior, or omits cases. Tests should also be sufficiently independent that one test’s setup or side effects do not make another pass for the wrong reason.
Choose who owns each handoff
There is no single workflow that fits every task. The choice is a trade-off between speed and how much review happens before implementation:
| Pattern | Human checkpoint | Main trade-off |
|---|---|---|
| Human defines or writes the test; agent implements | The behavior and test are set before the agent codes. | More human effort up front, but the agent has less room to target a mistaken test. |
| Agent drafts the failing test; human reviews it; agent implements | Review happens after the test is drafted and before implementation. | A useful balance when the agent can navigate project conventions but the behavior needs human judgment. |
| Agent completes the full test-first loop | Human review may happen after the test and implementation are both produced. | Less handoff friction for a small task, but an incorrect test can shape the implementation unchecked. |
VS Code’s proposed agent pattern separates red, green, and refactor responsibilities, with control passing between them. Separate agents are optional: the practical benefit is the checkpoint, which can also be created by asking one agent to stop after each phase. Böckeler’s exploratory practitioner evaluation did not find a clearly discernible outcome-quality difference in the tasks she tried; it is not a broad controlled result and does not establish that the patterns are equivalent.
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What the evidence does—and does not—show
Project-specific tests and review provide useful evidence about the assertions that ran. They do not make TDD prompting a quality guarantee. The available evaluations are limited and setup-dependent, not a general demonstration that coding agents produce better software when told to use TDD.
A March 18, 2026 preprint by Pepe Alonso, “TDAD: Test-Driven Agentic Development”, reports results from particular benchmarks and agent setups. In a Phase 1 evaluation of 100 SWE-bench Verified instances using Qwen3-Coder 30B, the authors report that graph-based impact context reduced test-level regressions from 6.08% to 1.82%, described as a 70% reduction. In the same reported comparison, TDD prompting alone had a 9.94% regression rate, higher than the vanilla-agent rate. Those figures apply to that evaluation; they do not show that TDD generally causes regressions or that the same results will transfer to another repository, model, or agent.
Rank #4
The preprint’s separate Phase 2 evaluation reports resolution rates rising from 24% to 32% across 25 instances using Qwen3.5-35B-A3B and an OpenCode agent. That small, setup-specific result is not a general estimate of agent performance. Treat the paper as preliminary evidence about its evaluated methods, not a universal workflow prescription.
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