Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2026

Abstract

GenAI-based coding assistants have disrupted software development. The next generation of these tools is agent-based, operating with more autonomy and potentially without human oversight. Like human developers, AI agents require contextual information to develop solutions that are in line with the standards, policies, and workflows of the software projects they operate in. Vendors of popular agentic tools (e.g., Claude Code) recommend maintaining version-controlled Markdown files that describe aspects such as the project structure, code style, or building and testing. The content of these files is then automatically added to each prompt. Recently, AGENTS.md has emerged as a potential standard that consolidates existing tool-specific formats. However, little is known about whether and how developers adopt this format. Therefore, in this paper, we present the results of a preliminary study investigating the adoption of AI context files in 466 open-source software projects. We analyze the information that developers provide in AGENTS.md files, how they present that information, and how the files evolve over time. Our findings indicate that there is no established content structure yet and that there is a lot of variation in terms of how context is provided (descriptive, prescriptive, prohibitive, explanatory, conditional). Our commit-level analysis provides first insights into the evolution of the provided context. AI context files provide a unique opportunity to study real-world context engineering. In particular, we see great potential in studying which structural or presentational modifications can positively affect the quality of the generated content.

Keywords

AI Agents, Generative AI, Open Source, Software Engineering

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

MSR '26: Proceedings of the 23rd International Conference on Mining Software Repositories, Rio de Janeiro, Brazil, April 13-14

First Page

194

Last Page

198

ISBN

9798400724749

Identifier

10.1145/3793302.3793350

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3793302.3793350

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