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AI Coding Agents Are Excellent Code Janitors, But Fail as Software Architects, Study Finds

By [Your Name], Science and Technology Correspondent

A new large-scale empirical study analyzing the behavior of AI coding agents like OpenAI Codex and Cursor has revealed that while these tools are becoming dedicated, intentional participants in software maintenance, their contributions remain focused on low-level cleanup rather than complex architectural improvements.

The research, detailed in a recent paper titled “Agentic Refactoring: An Empirical Study of AI Coding Agents,” analyzed 15,451 refactoring instances across 15,000 commits in real-world open-source Java projects. The findings provide the first comprehensive baseline for understanding how these autonomous “AI teammates” are shaping code quality.

Refactoring is Now an Intentional AI Task

The study confirms that refactoring—the process of restructuring code to improve internal quality without changing external behavior—is a common and intentional activity for AI agents. Refactoring was explicitly targeted in 26.1% of all agent-generated commits, demonstrating that AI tools are actively engaging in long-term code health.

When AI agents perform refactoring, their motivation is overwhelmingly driven by internal quality concerns: maintainability (52.5%) and readability (28.1%). This focus suggests developers are using agents as daily cleanup partners.

However, the analysis revealed a key limitation: the style of refactoring is overwhelmingly dominated by low-level, consistency-oriented edits. The three most common operations performed by agents involve renaming variables (e.g., changing a local variable name from i_2_ to a clearer bufferIndex), renaming parameters, and changing variable types. These localized changes account for over 30% of all agentic refactorings.

In contrast, AI agents performed fewer high-level structural changes—such as moving or extracting entire classes—compared to typical human refactoring behavior.

Minimal Structural Impact

To assess the impact of this agentic refactoring style, the researchers measured changes in structural code quality metrics and design smell counts.

Quantitatively, agentic refactoring showed statistically significant, though small, improvements in structural complexity and size. Medium-level refactorings—such as EXTRACT METHOD, where a long function is broken into smaller, reusable helpers—yielded the most reliable benefits.

For example, medium-level changes reduced the median Class Lines of Code (Class LOC) by 15.25 and the Weighted Methods per Class (WMC, a measure of complexity) by 2.07. These metrics show agents successfully reduce the local complexity of code components when they restructure methods.

However, despite the goal of improving quality, agents failed to consistently eliminate known design and implementation smells (like Long Method or Duplication), showing a median reduction of zero in overall smell counts.

Implications for Future Development

The study concludes that current AI coding agents are highly effective as incremental cleanup partners who excel at localized consistency tasks necessary for maintainability.

“To realize the vision of agents as ‘software architects,’ significant advancements are needed,” the authors note. Developers should leverage agents strategically for routine cleanup but must remain vigilant, supervising and validating any high-level design changes that require deep architectural understanding.

The findings provide a clear mandate for AI tool builders: future agents must be trained on architectural refactoring and equipped with specialized analysis tools to autonomously detect and fix complex design flaws, transforming them from diligent janitors into true system architects.