A Test Strategy for AI-Generated Code

A test strategy for AI-generated code has one job: catch the specific ways an agent gets things wrong, which are not the ways humans get things wrong. Human bugs come from incomplete understanding, so they cluster in complex logic. Agent bugs come from producing plausible output, so they cluster where the wrong answer looks right. If your test strategy was built for human mistakes, it has blind spots exactly where the agent fails.

The instinct is to let the agent write its own tests and call it covered. That gives you tests that pass, which is not the same as tests that protect you. You need a strategy that treats the agent as a productive but unreliable author whose work must be independently verified, because that is what it is.

Tests are the spec, not an afterthought

The strongest thing you can do is write the tests first, before the agent writes the code, from the acceptance criteria. Now the tests are a target the agent has to hit, not a rationalization it produces afterward. This flips the dynamic: instead of the agent deciding what "done" means and testing to that, you decide, and the agent codes to your definition.

This is TDD with the roles rearranged, and it works unusually well with agents because they are good at making a red test green. I go deep on the mechanics in TDD with AI coding agents, but the headline is simple: the test written before the code is worth ten written after, because the after ones inherit the code's blind spots.

Do not trust agent-written tests at face value

You will still have agent-written tests, and some are fine. But read them like a skeptic. The classic failure is a test that asserts too loosely, checks that a function returns something rather than the right thing, and passes against a broken implementation. Another is coverage theater: high line coverage, zero meaningful assertions, every branch touched and nothing verified.

Coverage numbers lie here more than usual. An agent can generate a suite that hits ninety percent of lines and proves almost nothing, because it wrote tests to the code it already wrote. That circularity is the trap. The question is never how much is covered, it is whether the tests would fail if the behavior were wrong. Whether AI-written tests should count at all is a real debate I take up in should AI write its own tests.

Aim tests at the agent's known failure modes

Build your strategy around where agents actually break. Boundary conditions: the empty list, the value one past the limit, the null. Agents write the middle of the range and skip the edges, so test the edges hard. Error paths: what happens on timeout, on a duplicate call, on a downstream failure. Agents write the sunny day, so test the storm.

Integration seams are another hot spot. An agent writing a single function often invents how it connects to the rest of the system, so test the actual integration, not the mocked version the agent set up to make its unit test pass. And test idempotency wherever a call might repeat, which matters even more in agent-driven pipelines, as I lay out in idempotency in AI workflows. These are the seams where plausible-looking code quietly does the wrong thing.

Keep the suite honest as volume grows

AI raises the rate at which code enters your repo, and a test suite that was adequate at ten PRs a week can rot at fifty. The suite needs the same maintenance discipline as the code: prune flaky tests, delete tests that assert nothing, and make sure a failure actually means something. A suite full of noise trains everyone to ignore red, which is worse than no suite.

Set a bar that a change does not merge unless its tests would catch the regression it is meant to prevent. That is a higher bar than "tests pass," and it is the one that holds under volume. Scoring code before it ships, as I describe in how to score AI-generated code, gives you a repeatable gate instead of a case-by-case judgment call.

Done right, testing is where AI-assisted development stops being a demo and becomes something you can run in production. The tools we build at Bootspring treat tests as the contract the agent has to satisfy, not a box it checks itself. Write the tests first, distrust the ones the agent writes, aim at the real failure modes, and keep the suite honest. That is the whole strategy, and it is the difference between shipping and hoping.