# Decoding the Configuration of AI Coding Agents: Insights from Claude Code Projects

arXiv: https://arxiv.org/abs/2511.09268 · HTML: https://mlsanity.com/paper/2511.09268/

## Key facts

- Cited by 14 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 6 of its 13 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Placed in

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

## Builds on (most cited first, 6 of 6)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025)
- [What's in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform](https://mlsanity.com/paper/1811.07643/) (2018)
- [Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study](https://mlsanity.com/paper/2402.16480/) (2024)
- [Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review](https://mlsanity.com/paper/2409.11864/) (2024)

## Built on by (most cited first, 14 of 14)

- [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)
- [On the Challenges and Opportunities of Learned Sparse Retrieval for Code](https://mlsanity.com/paper/2603.22008/) (2026)
- [Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study](https://mlsanity.com/paper/2606.12231/) (2026)
- [Configuration Smells in AGENTS.md Files: Common Mistakes in Configuring Coding Agents](https://mlsanity.com/paper/2606.15828/) (2026)
- [Deep Agentic Search for Repository-Level Code Question Answering: An Empirical Study](https://mlsanity.com/paper/2608.01507/) (2026)
- [ABTest: Behavior-Driven Testing for AI Coding Agents](https://mlsanity.com/paper/2604.03362/) (2026)
- [Documentation-Guided Agentic Codebase Migration from C to Rust](https://mlsanity.com/paper/2605.14634/) (2026)
- [Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment](https://mlsanity.com/paper/2606.11672/) (2026)
- [Toward Instructions-as-Code: Understanding the Impact of Instruction Files on Agentic Pull Requests](https://mlsanity.com/paper/2606.13449/) (2026)
- [Understanding the Architecture of Coding Agents: An Exploratory Study Using a Research Prototype](https://mlsanity.com/paper/2608.10934/) (2026)
- [PrimeAgentOrchestrator: Memory-Primed Agent Spawning for Personal AI Infrastructure](https://mlsanity.com/paper/2608.20342/) (2026)
- [Legacy System Modernization with Coding Agents: A Case Study](https://mlsanity.com/paper/2608.28972/) (2026)
- [A Study of the Reliability of Agentic AI-Generated Programs](https://mlsanity.com/paper/2609.18298/) (2026)

## Abstract (arXiv)

Agentic code assistants are a new generation of AI systems capable of performing end-to-end software engineering tasks. While these systems promise unprecedented productivity gains, their behavior and effectiveness depend heavily on configuration files that define architectural constraints, coding practices, and tool usage policies. However, little is known about the structure and content of these configuration artifacts. This paper presents an empirical study of the configuration ecosystem of Claude Code, one of the most widely used agentic coding systems. We collected and analyzed 328 configuration files from public Claude Code projects to identify (i) the software engineering concerns and practices they specify and (ii) how these concerns co-occur within individual files. The results highlight the importance of defining a wide range of concerns and practices in agent configuration files, with particular emphasis on specifying the architecture the agent should follow.