AGENTS.md文件能显著提升AI编程助手的效率。
On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agents
- 在代码仓库中添加AGENTS.md文件，可指导AI编写更高效代码。
- 有该文件时，运行时间中位数减少28.64%，输出令牌消耗降低16.58%。
- 适合关注AI编程工具优化与自动化流程的开发者参考。
AI编程助手如Codex和Claude Code正越来越多地用于自主贡献软件仓库。然而，代码仓库级别的配置文件如何影响这些助手的运行效率仍不明确。本文研究了AGENTS.md文件对GitHub合并请求中AI编程助手运行时间和令牌消耗的影响。我们分析了10个仓库中的124个合并请求，在有无AGENTS.md文件两种条件下执行代理，并测量了实际运行时间和令牌使用量。结果表明，存在AGENTS.md文件时，中位运行时间降低28.64%，输出令牌消耗减少16.58%，同时任务完成率保持相当。基于此，我们讨论了实际部署中配置策略的启示，并提出了关于仓库级指令在塑造AI编程助手行为、效率及集成方面的更广泛研究方向。
原文摘要 · Abstract (English)
AI coding agents such as Codex and Claude Code are increasingly used to autonomously contribute to software repositories. However, little is known about how repository-level configuration artifacts affect operational efficiency of the agents. In this paper, we study the impact of AGENTS$.$md files on the runtime and token consumption of AI coding agents operating on GitHub pull requests. We analyze 10 repositories and 124 pull requests, executing agents under two conditions: with and without an AGENTS$.$md file. We measure wall-clock execution time and token usage during agent execution. Our results show that the presence of AGENTS$.$md is associated with a lower median runtime ($Δ28.64$%) and reduced output token consumption ($Δ16.58$%), while maintaining a comparable task completion behavior. Based on these results, we discuss immediate implications for the configuration and deployment of AI coding agents in practice, and outline a broader research agenda on the role of repository-level instructions in shaping the behavior, efficiency, and integration of AI coding agents in software development workflows.
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