Most developers discover that coding agents like Claude Code deliver rapid results initially, then gradually slow down and introduce more errors. The codebase accumulates inconsistencies that compound over time, making future AI-assisted changes increasingly difficult. This degradation signals when refactoring becomes essential for maintaining productivity.

When AI agents struggle with implementations or introduce bugs in untouched areas, messy code is usually the culprit. Poor architectural decisions multiply maintenance overhead—a scattered chat component across multiple app locations requires redundant updates instead of single-point changes. The author argues refactoring isn't just periodic maintenance but an ongoing necessity for AI-assisted workflows.

Effective refactoring with Claude Code requires using high-effort reasoning modes like Ultracode and plan mode to analyze the entire repository before changes. The process works best when you provide context about why refactoring matters, not just what to refactor. Running tests before and after ensures the AI doesn't introduce regressions while restructuring code.

The author reports Claude Code performs nearly perfectly for refactoring tasks when properly configured. Rather than eliminating refactoring, developers should embrace it as a natural evolution of codebases working with AI agents. Clean, well-structured code directly translates to more reliable AI assistance.