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One of the more dangerous failure modes we kept seeing wasn’t a visible crash. It was a run that looked clean because the agent generated tests, the tests passed, and everyone felt better - but the tests were validating the wrong thing.
That changed how we think about agent output. More generated tests do not automatically mean more trust. The useful question is whether the run leaves enough evidence for someone to see what behavior was actually exercised, what assumptions got locked in, and what still needs human review.
We built MartinLoop around that kind of problem, but I’m curious how others handle it in practice. Have you seen agent-generated tests create false confidence in a change? If so, what signal finally made it obvious?
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One of the more dangerous failure modes we kept seeing wasn’t a visible crash. It was a run that looked clean because the agent generated tests, the tests passed, and everyone felt better - but the tests were validating the wrong thing.
That changed how we think about agent output. More generated tests do not automatically mean more trust. The useful question is whether the run leaves enough evidence for someone to see what behavior was actually exercised, what assumptions got locked in, and what still needs human review.
We built MartinLoop around that kind of problem, but I’m curious how others handle it in practice. Have you seen agent-generated tests create false confidence in a change? If so, what signal finally made it obvious?
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