[2601.20404] On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agents

Computer Science > Software Engineering arXiv:2601.20404 (cs) [Submitted on 28 Jan 2026 (v1), last revised 30 Mar 2026 (this version, v2)] Title:On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agents Authors:Jai Lal Lulla, Seyedmoein Mohsenimofidi, Matthias Galster, Jie M. Zhang, Sebastian Baltes, Christoph Treude View a PDF of the paper titled On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agents, by Jai Lal Lulla and 5 other authors View PDF HTML (experimental) Abstract: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 ($\Delta 28.64$%) and reduced output token consumption ($\Delta 16.58$%), 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 ...