AI Engineer · October 9, 2026

Turn recurring agent mistakes into checks that cannot pass by deleting evidence

video AI Engineering Model Evaluation

Turn recurring agent mistakes into checks that cannot pass by deleting evidence video thumbnail

Why it matters

Greg Pstrucha describes moving repeated coding corrections into types, linters, and tests that run outside the model. His Sentry examples connect API response types to generated schemas and validate code examples in agent instructions. The talk also exposes a failure mode: an agent can satisfy a check by removing an example or changing its formatting, leaving the intended requirement unmet. Sentry’s public endpoint-documentation workflow provides a concrete companion example of comparing declared response types with actual behavior. These checks improve the feedback available to a coding agent, but they do not replace judgment about design or correctness.

My takeaway: Choose one recurring defect and create a failing fixture. Then try removing the example, suppressing the type error, or changing the markup. The check should reject those evasions as well as detect the original defect.

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