AI BriefingDoes Code Cleanliness Affect Coding Agents
Key point
The structural quality of code maintains AI agents' task success rate while improving token usage and exploration efficiency.
Details
To analyze the impact of code's structural/stylistic quality (Cleanliness) on AI coding agent performance, a Minimal-pair-based evaluation protocol was introduced, using code pairs identical in architecture and behavior but differing only in whether they violate static analysis rules.
After conducting 660 experiments targeting Claude Code, code cleanliness did not change the agent's task pass rate itself.
However, a significant difference appeared in the agent's operational footprint.
- Token usage: Used 7-8% fewer tokens when working with clean code
- Exploration efficiency: File revisit count decreased by 34%
In conclusion, code maintainability principles remain important even in AI-based development environments, acting as a factor that directly affects the agent's computational cost and exploration efficiency just as much as model selection or prompting.
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