Configuring Agentic AI Coding Tools: An Exploratory Study
This study systematically analyzes configuration mechanisms for five agentic AI coding tools (Claude Code, GitHub Copilot, Cursor, Gemini, Codex) and examines adoption across 2,853 GitHub repositories. Findings reveal that context files, especially AGENTS$.md, dominate as a de facto standard; advanced mechanisms like Skills and Subagents are rarely used; and distinct configuration practices emerge per tool, with Claude Code users employing the broadest range.
[2602.14690] Configuring Agentic AI Coding Tools: An Exploratory Study
[Submitted on 16 Feb 2026 (v1), last revised 8 May 2026 (this version, v4)]
Title:Configuring Agentic AI Coding Tools: An Exploratory Study
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Abstract:Agentic AI coding tools increasingly automate software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. We present a systematic analysis of configuration mechanisms for agentic AI coding tools, covering Claude Code, GitHub Copilot, Cursor, Gemini, and Codex. We identify eight configuration mechanisms spanning from static context to executable and external integrations and, in an empirical study of 2,853 GitHub repositories, examine whether and how they are adopted, with a detailed analysis of Context Files, Skills, and Subagents. First, Context Files dominate the configuration landscape and are often the sole mechanism in a repository, with AGENTS$.$md emerging as an interoperable standard across tools. Second, few repositories adopt advanced mechanisms such as Skills and Subagents. Skills predominantly rely on static instructions rather than executable scripts. Third, distinct configuration practices are forming around different tools, with Claude Code users employing the broadest range of mechanisms. These findings establish an empirical baseline for understanding how developers configure agentic tools, suggest that AGENTS$.$md serves as a natural starting point, and motivate longitudinal and experimental research on how configuration strategies evolve and affect agent performance.
Comments: 10 pages, 7 figures, 3 tables, Proceedings of the 3rd ACM/IEEE International Conference on AI-powered Software (AIware 2026)
Subjects:
Software Engineering (cs.SE)
Cite as: arXiv:2602.14690 [cs.SE]
(or arXiv:2602.14690v4 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2602.14690
arXiv-issued DOI via DataCite
Submission history
From: Sebastian Baltes [view email] [v1] Mon, 16 Feb 2026 12:24:28 UTC (217 KB)
[v2] Fri, 20 Mar 2026 22:48:47 UTC (206 KB)
[v3] Thu, 9 Apr 2026 15:25:44 UTC (201 KB)
[v4] Fri, 8 May 2026 20:59:28 UTC (240 KB)
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