Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2026
Abstract
Agentic AI coding tools increasingly automate software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. We present a systematic analysis of configuration mechanisms for agentic AI coding tools, covering Claude Code, GitHub Copilot, Cursor, Gemini, and Codex. We identify eight configuration mechanisms spanning from static context to executable and external integrations and, in an empirical study of 2,853 GitHub repositories, examine whether and how they are adopted, with a detailed analysis of Context Files, Skills, and Subagents. First, Context Files dominate the configuration landscape and are often the sole mechanism in a repository, with AGENTS.md emerging as an interoperable standard across tools. Second, few repositories adopt advanced mechanisms such as Skills and Subagents. Skills predominantly rely on static instructions rather than executable scripts. Third, distinct configuration practices are forming around different tools, with Claude Code users employing the broadest range of mechanisms. These findings establish an empirical baseline for understanding how developers configure agentic tools, suggest that AGENTS.md serves as a natural starting point, and motivate longitudinal and experimental research on how configuration strategies evolve and affect agent performance.
Keywords
AI agents, configuration, generative AI, software engineering
Discipline
Artificial Intelligence and Robotics | Software Engineering
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
AIware '26: Proceedings of the 3rd ACM International Conference on AI-Powered Software, Montreal, Canada, July 6-7
First Page
11
Last Page
20
ISBN
9798400726019
Identifier
10.1145/3805760.3814887
Publisher
ACM
City or Country
New York
Citation
GALSTER, Matthias; MOHSENIMOFIDI, Seyedmoein; LULLA, Jai Lal; ABUBAKAR, Muhammad Auwal; TREUDE, Christoph; and BALTES, Sebastian.
Configuring agentic AI coding tools: An exploratory study. (2026). AIware '26: Proceedings of the 3rd ACM International Conference on AI-Powered Software, Montreal, Canada, July 6-7. 11-20.
Available at: https://ink.library.smu.edu.sg/sis_research/11150
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3805760.3814887