How Codebase Structure, Not Prompts, Determines AI Coding Agent Performance

AI coding agents like Cursor and Claude Code perform significantly better when the repositories they work on follow clear structural conventions, according to a developer analysis. Key factors include organizing code into folder-per-concern layouts with one component per file, rather than large monolithic files that force agents to reason about entire codebases at once. Explicitly typed and exported component props act as a readable API, allowing agents to use components correctly without guessing at implementation details. Configuration files such as .cursorrules and CLAUDE.md provide persistent system-level context to agents on every run, reducing the need to re-explain project conventions in each prompt. These structural choices are described as low-cost to implement but capable of substantially improving the reliability and output quality of AI-assisted development.