AI Engineer · October 3, 2026

Close the coding-agent feedback loop with failure traces and staged releases

video Model Evaluation AI Engineering

Close the coding-agent feedback loop with failure traces and staged releases video thumbnail

Why it matters

Harald Kirschner describes feeding production failures and developer corrections back into coding-agent evaluation. The workflow groups error traces, routes actionable failures to owners, creates candidate fixes, and checks changes before staged rollout. The VS Code team’s published evaluation work gives a complementary example: repeatedly running a tiny task while recording full tool sequences can reveal overhead that a pass/fail score misses. A smoke test cannot stand in for a diverse task suite, and code survival or release frequency does not by itself prove software quality. The reusable method is traceable regression feedback with controlled release exposure.

My takeaway: Turn one confirmed production failure into a stable regression fixture. Compare completion, tool sequence, latency, and cost before and after a fix; validate broader tasks and monitor a limited rollout before widening exposure.

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