AI Engineer · October 26, 2026

Code review agents: replay incident-causing pull requests before changing prompts

video Featured · 70/100 practical score AI Engineering Model Evaluation

Code review agents: replay incident-causing pull requests before changing prompts video thumbnail

Why it matters

Daksh Gupta describes a review agent built from a sandbox, a PR webhook and a model harness. His practical evaluation method replays pull requests linked to past incidents, targets recurring bug classes and suppresses low-value comments. Codebase context and executable application flows supplement review. Vendor catch-rate and cost claims are not independently established.

My takeaway: Build a versioned evaluation set from historical incident-causing PRs and valid changes. Compare review prompts on confirmed defects, false alarms and repeated-run stability. Supply relevant architecture and requirements, run reproducible application checks in an isolated environment, and require evidence before accepting a suggested patch.

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