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This article focuses specifically on debugging techniques for autonomous coding agents that utilize machine learning models to execute tool calls and modify codebases. It provides a practical framework for logging model requests, function results, and patches to improve the reliability of AI-driven development workflows. The content is directly relevant to practitioners building or integrating LLM-based software engineering tools rather than general programming tutorials.
How to Debug AI Coding Agents When They Change the Wrong Thing

A practical tutorial for recording model tool requests, real function results, patches, checks, screenshots, and a saved run log. The post How to Debug AI Coding Agents When They Change the Wrong Thing appeared first on Towards Data Science.
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