# On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agents

arXiv: https://arxiv.org/abs/2601.20404 · HTML: https://mlsanity.com/paper/2601.20404/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 5 of 5)

- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Assessing the Quality of the Steps to Reproduce in Bug Reports](https://mlsanity.com/paper/1906.07107/) (2019)
- [Agentic AI for Software: thoughts from Software Engineering community](https://mlsanity.com/paper/2508.17343/) (2025)
- [EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents](https://mlsanity.com/paper/2601.05777/) (2026)

## Built on by (most cited first, 17 of 17)

- [Harness Engineering for Agentic AI Coding Tools: An Exploratory Study](https://mlsanity.com/paper/2602.14690/) (2026)
- [Codified Context: Infrastructure for AI Agents in a Complex Codebase](https://mlsanity.com/paper/2602.20478/) (2026)
- [Do Context Files Help Coding Agents? A Two-Agent Ablation Study on Real Repositories](https://mlsanity.com/paper/2607.27250/) (2026)
- [Knowledge Activation: AI Skills as the Institutional Knowledge Primitive for Agentic Software Development](https://mlsanity.com/paper/2603.14805/) (2026)
- [FAVA: Formal Authorization for Verified Agents with Evidence-Backed Permission Graphs](https://mlsanity.com/paper/2607.27267/) (2026)
- [Why Does CLAUDE.md Keep Growing? Catastrophic Remembering in Agentic Coding](https://mlsanity.com/paper/2608.11095/) (2026)
- [The Devil Is in the Interface: Evaluating How Tool Architecture Shapes Coding Agent Behavior](https://mlsanity.com/paper/2608.11386/) (2026)
- [Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development](https://mlsanity.com/paper/2605.20456/) (2026)
- [Context Rot in AI-Assisted Software Development: Repurposing Documentation Consistency for AI Configuration Artifacts](https://mlsanity.com/paper/2606.09090/) (2026)
- [From Registry to Repository: How AI Agent Skills Are Written, Adapted, and Maintained](https://mlsanity.com/paper/2607.00911/) (2026)
- [Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study](https://mlsanity.com/paper/2606.12231/) (2026)
- [Probe-and-Refine Tuning of Repository Guidance for Coding Agents](https://mlsanity.com/paper/2606.20512/) (2026)
- [An Exploratory Study of Agent Plans for Agentic AI Coding Tools in Open-Source Software](https://mlsanity.com/paper/2608.04661/) (2026)
- [The Impact of Configuring Agentic AI Coding Tools on Build-vs-Buy Decisions: A Study Protocol](https://mlsanity.com/paper/2606.03907/) (2026)
- [Specifying and Maintaining Agentic Workflows: An Empirical Study of GitHub Agentic Workflows](https://mlsanity.com/paper/2609.27263/) (2026)
- [ParallelPilot: Supporting Coordination and Monitoring in Parallel AI Coding](https://mlsanity.com/paper/2609.33113/) (2026)
- [Scientific Agents: Evaluating Profession-Specific System Prompts on Scientific Tasks](https://mlsanity.com/paper/2610.00084/) (2026)

## Abstract (arXiv)

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 the role of repository-level instructions in shaping the behavior, efficiency, and integration of AI coding agents in software development workflows.