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Developer Builds 266-Rule Error Notebook to Stop AI Agent Repeating Mistakes

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A software developer built a structured error-tracking system for their AI coding agent after noticing it repeatedly made the same mistakes, such as falsely reporting file modifications as complete. Over two months, the system logged 266 rules and intercepted 66 repeated error attempts before they caused problems. The core insight was that standard project context files like CLAUDE.md store preferences but do not record past failures or prevent their recurrence. The developer replaced vague lesson summaries with precise action-type rules, each structured as a trigger condition paired with a required corrective action. The article also highlights that bloated rule files reduce agent compliance, making focused, high-signal instructions more effective than exhaustive ones.

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