Clean code in the age of coding agents (yanist.com) AI

The article argues that “clean code” still matters in the era of coding agents and LLMs because an agent’s performance depends on the codebase’s structure, not just whether the system works. It frames clean code in terms of readability, simplicity, modularity, and testability, noting that poorly organized code forces agents to read and modify more files, increasing token usage and cost. The author recommends explicitly describing expected code organization when assigning tasks to agents and continuing to review the agent’s changes.