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When an AI coding agent says “The test was wrong” and proposes a rewrite, treat that as a claim to verify—not as a diagnosis. The implementation may be wrong, the test may be wrong, or both may be wrong. Check what the test was meant to prove, whether it actually exercised that behavior, and what changed in the code and test before accepting a passing run.
What a failing generated test does—and does not—tell you
A test failure establishes that the code and the test disagree under the conditions of that run. It does not, by itself, identify which one is incorrect. The implementation could violate the intended behavior; the test could encode the wrong expectation; or both could be flawed. A test may also fail to reach the behavior it was intended to examine, so its result does not answer the question you thought it was testing.
That distinction matters because an AI coding agent can produce both the implementation and the test. A plausible test is still just text until it is executed, and execution alone does not establish that it checks the right behavior. You need to assess the test’s purpose as well as its result.
Check the test’s purpose before accepting a rewrite
- State the behavior in plain language. What should happen, under what conditions, and what outcome would demonstrate it? Keep this separate from the agent’s explanation of its own test.
- Trace the test to that behavior. Identify the inputs, setup, and code path the test actually reaches. Ask whether those conditions can produce the behavior being checked.
- Inspect the expected result. Decide whether the assertion represents the intended behavior, rather than merely matching what the current implementation happens to do.
- Review the implementation and test changes separately. A code change and a rewritten test make two distinct claims. Understand why each changed and whether each is justified by the intended behavior.
- Run the test and interpret the result narrowly. A passing run shows that the current test passed under the run’s conditions. It does not prove that the test exercised the relevant behavior or that the implementation is correct in other cases.
If the agent cannot explain what the test proves or why its old expectation was wrong, do not accept the rewrite just because it turns the test green.
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Why a test can pass without testing the intended behavior
In one example, Gil Zilberfeld describes a test intended to recreate a race condition that did not run the race at all. The test could execute and produce a result without exercising the timing-dependent behavior it was supposed to check. This is an anecdote, not evidence of how often generated tests miss their target, but it illustrates why execution is not the same as validation.
For a race-condition test, inspect whether its setup and execution can actually produce the competing operations or timing conditions that define the race. More generally, follow the test from its setup through the relevant code path to the assertion. If the path never reaches the behavior, rewriting the expected value may hide the problem rather than fix it.
Separate the places an agent can be wrong
- Implementation: the code does not meet the intended behavior.
- Test creation: the test checks the wrong condition or asserts the wrong outcome.
- Test-to-code validation: the test and implementation may be individually plausible but do not establish that the intended behavior is covered.
- Execution: the test may not run as expected, or its setup may not create the relevant conditions.
- Evaluation: the agent may treat a passing or failing result as proof without adequately assessing what the test is for.
These are useful review distinctions, not a guarantee that every failure fits neatly into one category. A single change can contain multiple problems.
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Break work into small, manageable tasks. Smaller changes and clearer logs make it easier to see what the agent changed, which test result relates to which change, and whether a rewritten test still checks the requirement. Zilberfeld describes reviewability as a delivery capability: code that cannot be meaningfully inspected is harder to trust, even when its tests pass.
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This is not an argument that coding agents always fail. Zilberfeld says he uses them, while emphasizing that relying on an end result and asking for fixes is a bet rather than proof. The practical response is to make the work inspectable and judge each result against the behavior you need.
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