AI Engineer · October 26, 2026

Agent improvement loops: verify failure cases before adding them to regression data

video Model Evaluation AI Engineering

Agent improvement loops: verify failure cases before adding them to regression data video thumbnail

Why it matters

Fuad Ali’s workshop follows a toy-shopping agent from trace review to a failure dataset, evaluator, proposed fix and comparison run. A planted filter bug produces redundant searches; the walkthrough checks whether the fix removes that behavior without breaking other requirements. Human review remains central to confirming issues, curating examples and judging fixes. Demo scores are not general release thresholds.

My takeaway: Capture a complete failing trace, verify the expected behavior and add it alongside successful cases to a versioned dataset. Prefer code checks for deterministic rules and calibrate model judges against human labels. Run baseline and candidate versions on the same cases, inspect regressions and cost, and approve deployment only against explicit acceptance criteria.

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