How to Write Test Cases for Code an AI Agent Wrote
Testing AI-generated code well requires a slightly different mindset than testing your own — you didn’t write the implementation, so you can’t lean on memory of “what I was thinking” while writing the test. Knowing how to write test cases for code an AI agent wrote means testing behavior deliberately rather than confirming what the code happens to already do.
Focus Tests on Behavior, Not Implementation
Write tests against what the function is supposed to do, not how the agent happened to implement it. Agents often produce correct-but-unusual implementations, and tests tied too closely to implementation details break on the next refactor even when nothing is actually wrong — which trains a team to distrust its own test suite.
Where to Focus First: Edge Cases the Agent Likely Didn’t Consider
- Empty or null inputs — a common gap, since an agent’s happy-path generation often assumes well-formed input by default.
- Boundary values — the first and last valid values, and the ones just outside that range.
- Concurrent or repeated calls — race conditions and idempotency issues that don’t show up in a single-call test.
- Malformed or unexpected data shapes — especially for anything processing external input or API responses.
A Practical Testing Workflow for AI-Generated Code
- Read the code once for intent before writing any tests — understand what it’s supposed to do, not just what it currently does.
- Write tests for the stated requirements first, independent of the implementation.
- Add edge-case tests specifically targeting the categories above — this is usually where AI-generated code has the largest gap versus a human-written equivalent.
- Run the full suite against a small deliberate change to confirm tests actually fail when they should — a passing suite that can’t detect a real regression is worse than no suite.
Using AI to Help Write Tests (Carefully)
AI tools can generate a useful first draft of test cases, but asking the same agent that wrote the implementation to also write its tests risks inheriting the same blind spots twice — it tends to test the behavior it produced, not the behavior that was actually required. A second, independent look (human or a different prompt entirely divorced from the original context) catches more.
A Rule of Thumb Borrowed From General Testing Practice
A good test suite should tell you where a change broke something, not just confirm that it did — a principle covered well in Martin Fowler’s testing guidance. Combine this with the review habits in reviewing AI-generated code before production for a complete quality gate that catches both logic and security issues.
How Much Test Coverage Is Actually Enough
| Code Type | Recommended Focus |
|---|---|
| Business logic, calculations | High coverage, explicit edge cases |
| Auth, payments, data mutation | Very high coverage + manual review |
| Static UI, display-only code | Lighter coverage, visual/snapshot tests |
Frequently Asked Questions
Should AI-generated code have a higher testing bar than human-written code?
In practice, yes — since the implementation wasn’t reasoned through by someone accountable for edge cases in the same way, extra coverage on boundary conditions is a reasonable default rather than optional.
Can AI-written tests be trusted at all?
As a first draft, yes, but always add edge-case tests independently rather than relying solely on tests generated in the same context as the implementation.
How do I know if my test suite is actually catching real bugs?
Periodically introduce a deliberate small bug and confirm the suite catches it — a suite that never fails during this exercise likely has coverage gaps worth investigating.
The Bottom Line
Testing AI-generated code well means testing intent, not implementation — focus on the edge cases an agent’s happy-path generation likely skipped, keep test authorship independent from the code’s original generation context, and periodically verify your suite can actually catch a real regression.
