Why Static AI Rule Files Like AGENTS.md Are Failing (and What Actually Works)

Many teams have adopted the use of static instruction files like AGENTS.md to guide AI coding agents by documenting codebase rules, but research from ETH Zurich indicates that these files might hinder performance, reducing task success rates and increasing inference costs. The problem lies not in the rules themselves but in their static delivery method, which often overwhelms AI systems with irrelevant information, leading to inefficiencies. As these files grow, they become redundant and contradictory, lacking a feedback loop to refine their effectiveness. Qodo offers a solution with a dynamic, context-aware rule system that evaluates only the relevant rules for specific code modifications, thereby reducing unnecessary context and improving model performance. Their approach involves structuring rules with clear criteria, batching them to maintain focus, detecting conflicts, and using a separate agent to enforce rule compliance, ultimately transforming rules from passive guidance into active guardrails. This evolution in AI rule systems aims to enhance the quality and reliability of AI-generated code by moving beyond static files to more adaptable and enforceable solutions.

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