Decoding the Configuration of AI Coding Agents: Insights from Claude Code Projects
By: Helio Victor F. Santos , Vitor Costa , Joao Eduardo Montandon and more
Potential Business Impact:
Helps AI build software by following rules.
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.
Similar Papers
On the Use of Agentic Coding Manifests: An Empirical Study of Claude Code
Software Engineering
Helps computers write code by themselves.
Context Engineering for AI Agents in Open-Source Software
Software Engineering
Helps AI understand code projects better.
Agentic Refactoring: An Empirical Study of AI Coding Agents
Software Engineering
Makes computer code cleaner and easier to fix.