Test-Driven Development With Coding Agents That Works

Most teams adopting coding agents follow the same pattern: describe a feature, let the agent write the code, skim the diff, merge. It feels fast. Then a bug surfaces that the agent quietly introduced because nothing in the loop ever verified the code's actual behaviour. The agent produced plausible-looking output. It just wasn't correct.

The fix isn't slower review. It's a different workflow. When you ask the agent to write failing tests before implementation, the red-green-refactor cycle becomes its own feedback loop. It can't hallucinate its way through: the test suite tells it whether its output is right.

That's the core premise of agentic coding with TDD: tests are not just quality gates you add at the end. They're the specification the agent executes against. This post walks through exactly how to set that up.

What you'll learn

• Why hallucination is a feedback problem, not a model problem
• The agent-native red-green-refactor loop
• Writing prompts that produce failing tests first
• Choosing the right test granularity
• Handling edge cases and boundary conditions
• TDD vs test-after: a practical comparison
• Where this approach breaks down
• Frequently Asked Questions

Why hallucination is a feedback problem, not a model problem

Hallucination in code generation is often framed as a model-quality issue. A better model hallucinates less. That's partially true, but it misses the structural cause: agents produce output without observing its consequences unless your workflow forces that observation.

A developer writing code manually runs it constantly. The feedback is tight. An agent completing a task in one shot has no such feedback unless you design it in. The red-green-refactor cycle is, at its core, a feedback mechanism. Each test run tells the agent something true about the state of the code.

This is why test-first prompting works better than test-after review. When the agent authors the tests, it builds a model of the intended behaviour before generating any implementation. The tests make that model explicit and executable. And the implementation will diverge from intent, especially across edge cases. Failing tests surface it immediately.

The opinionated take here: code review is a poor substitute for a failing test. Review catches what a human notices. A test catches what the spec says. For agents that ship dozens of changes per session, you need the spec.

The agent-native red-green-refactor loop

Classic TDD follows three steps: write a failing test (red), make it pass with the simplest possible code (green), then refactor without breaking it. That loop maps directly onto an agent workflow, with one adjustment. You stay in the driver's seat for the refactor phase.

Here's the practical sequence the Laxaar team uses:

1. Write the spec prompt. Describe the behaviour in terms of inputs and outputs, not implementation details. Include at least one happy path and two edge cases.
2. Ask the agent for failing tests only. Explicitly instruct it not to write any implementation yet. Run the tests. Confirm they fail for the right reason.
3. Ask the agent to make the tests pass. The constraint is tight: don't change the tests.
4. Run the full suite. Check that no previously passing tests regressed.
5. Refactor together. Ask the agent to clean up the implementation while keeping tests green. Review that diff.

The key moment is step two. If you skip the "failing tests only" constraint, agents default to writing implementation and tests simultaneously. They'll write tests that match whatever they just implemented, which tests nothing meaningful.

Writing prompts that produce failing tests first

The prompt structure matters more than you'd expect. Vague instructions produce vague tests. Here's a pattern that works:

Task: write failing tests for a `parseInvoice(raw: string): Invoice` function.

Behaviour contract:
- Returns an Invoice object with fields: id (string), total (number), currency (string).
- Throws `ParseError` if the input is not valid JSON.
- Throws `ParseError` if any required field is missing.
- If currency is absent, defaults to "USD".
- total must be a positive number; throw `ParseError` if zero or negative.

Write only the test file. Do not write the implementation.
Use Vitest. Import from '../src/parseInvoice'.

Three things make this prompt effective. First, the contract is stated in observable terms: inputs and outputs, not "it should handle errors nicely." Second, the edge cases are listed explicitly. Agents won't invent edge cases unless you name them. Third, the stop condition is explicit: test file only, no implementation.

After the agent writes the tests, run them. They should all fail with Cannot find module or a similar import error. That's correct. If any pass without an implementation existing, the test is checking nothing.

Choosing the right test granularity

Not every agent task maps neatly to unit tests. The granularity you choose changes what the feedback loop can catch.

Unit tests are the right default for pure functions and isolated modules. They run fast and give precise failure messages. For an agent writing a data transformation, a set of unit tests covering inputs, outputs, and error paths is the tightest possible feedback.

Integration tests make more sense when the agent is wiring together multiple components (an API handler that validates input, queries a database, and formats a response). Unit-testing each piece separately misses the wiring bugs, which are exactly the ones agents introduce.

End-to-end tests are rarely the right starting point for agent TDD. They're slow, they require infrastructure, and a failure gives the agent too little signal to act on. Save E2E tests for the verification phase after the feature is complete.

The trade-off: tighter granularity gives the agent faster feedback but requires more upfront test-writing effort. For high-churn features, unit tests pay off quickly. For long-lived, stable APIs, investing in integration tests catches more of the real failure modes.

Handling edge cases and boundary conditions

Agents are optimistic. Left to themselves, they'll write the happy path well and treat edge cases as footnotes. TDD fixes this, but only if the tests you ask for include edges explicitly.

A useful technique is asking the agent to list edge cases before writing the tests:

Before writing tests, list all edge cases and boundary conditions
for `calculateDiscount(price: number, code: string): number`.
Do not write any code yet.

The agent's list becomes your checklist. If it missed something obvious, add it before proceeding. Then ask it to write tests covering every item on the list. This two-step approach catches gaps before they become missing test cases.

Boundary conditions worth naming in almost every domain: empty string inputs, zero values, negative numbers, null/undefined arguments, strings that look like numbers, and collections with zero or one element. Agents handle middle-of-the-range cases well. Boundaries are where they slip.

TDD vs test-after: a practical comparison

Teams new to agentic coding often ask whether the upfront test-writing cost is worth it. Here's an honest comparison:

Dimension	Test-first (TDD)	Test-after
Feedback loop	Tight — agent fixes before PR	Loose — review catches some
Test quality	Tests match the spec	Tests match the implementation
Edge case coverage	Forced by prompt	Often incomplete
Session length	Longer per feature	Faster first-pass
Regression safety	High — suite guards refactors	Depends on coverage
Agent hallucination risk	Low — caught by failing tests	Higher — code reviewed, not run

The honest answer is that test-after is faster in the short run. If you're running a one-day spike or a throwaway prototype, test-after is fine. For production code, the rework cost of a hallucinated implementation that passes review but fails in production dwarfs the upfront test-writing time.

At Laxaar, we've found the break-even point is roughly any feature that will survive a second sprint. If it's living in the codebase past the next planning cycle, write tests first.

Where this approach breaks down

TDD with agents isn't a silver bullet. There are real cases where it struggles.

Highly visual or interactive code. Writing tests for a React animation or a canvas-rendering component is awkward. The tests end up checking internal state rather than visible behaviour, which is testing the implementation, not the spec.

Exploratory work. When you don't yet know what the right behaviour is, writing a test is premature. TDD assumes you know what correct looks like. If you're discovering the shape of a feature, spike first, then write the tests once the design stabilises.

Agent-generated tests for agent-generated code in the same prompt. This is the trap. If you ask the agent to write tests and implementation together, it'll write tests that confirm whatever it built. The tests will be green and meaningless. The failing-first step is the entire point. Don't skip it.

Infrastructure-heavy code. Database migrations, cloud resource provisioning, and deployment scripts are hard to unit test meaningfully. Integration or smoke tests are more appropriate, but they require real environments the agent can't spin up itself.

Knowing these limits helps you apply the workflow where it pays off and skip it where it doesn't. Our AI-powered development services at Laxaar lean on agent TDD for business logic, API contracts, and data transformation. Everything else gets different verification strategies.

If you're looking to hire engineers who know how to run this loop well, our agentic coding team builds it into every production engagement. You can also see real examples of how we apply it in our project portfolio.

Frequently Asked Questions

Does TDD with agents work for any programming language?

Yes, the workflow is language-agnostic. The pattern (spec prompt, failing tests, implementation, refactor) applies whether you're working in TypeScript, Python, Go, or Rust. The agent needs a test runner it can invoke and clear error output to act on. Most modern test frameworks (Jest, Vitest, pytest, Go's testing package) give it both. The prompts you write will reference the specific library, but the structure stays the same.

What if the agent writes tests that immediately pass?

That's a signal the tests are not testing the specified behaviour. It usually means the agent wrote tests against an implementation it anticipated rather than against the spec. Reject the tests, re-read your prompt, and add more specificity to the contract. Explicitly include a case you know should fail (like passing a null value that should throw) and verify the test fails before accepting the file.

How do we handle tests that need mocks for external services?

Ask the agent to set up mocks in the same test-writing step. Include the external dependency in the contract description: "the function calls paymentGateway.charge(amount) — mock this dependency." Agents handle mock setup reliably when it's part of the initial prompt. Where teams get into trouble is asking for tests first and mocks second. The agent will sometimes write tests that reach the real network, making your test suite brittle and environment-dependent.

Can this workflow integrate with CI?

It should, and that's the goal. The test suite the agent authors becomes the CI gate for every subsequent change, including future agent sessions. The Laxaar team runs agent-authored tests in the same pipeline as human-authored ones. The only setup requirement is that the CI environment can execute the test runner the agent used. Because we ask agents to use the project's existing test tooling rather than inventing new frameworks, this is usually zero extra configuration.

Is TDD-first slower overall?

Per-feature, yes, slightly. Across a sprint, no. The time saved in debugging regressions, reviewing incorrect implementations, and manually verifying edge cases more than recovers the upfront cost. Teams that track cycle time across a quarter consistently find that agentic coding with TDD outperforms agentic coding without it once you're past the first few weeks of adopting the workflow.

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If you want to apply this workflow to your own product and don't want to figure out the tooling from scratch, talk to the Laxaar team. We run agentic coding engagements where test-first is the default, not the exception. Tell us about your project and we'll walk you through what the loop looks like for your stack.