Is Agent Code Less Maintainable Than Human Code?

2026-06-19

AI Summary

This study investigates whether code generated by AI coding agents is more difficult to maintain than human-written code, with a particular focus on compounding challenges that arise when agents develop upon code previously generated by themselves or others. To address this, we introduce the CodeThread framework and conduct controlled experiments with four state-of-the-art coding agents across four repository-scale benchmarks, systematically evaluating their task-completion performance through regression analysis and behavioral code comparison. Our work reveals, for the first time, that agents experience up to a 13.1% drop in task success rate when building upon their own prior outputs—a significant degradation in downstream development efficiency. This phenomenon cannot be explained by conventional maintainability metrics but stems instead from subtle behavioral discrepancies in aspects such as input validation and error handling, underscoring the critical role of code behavior characteristics in software maintainability.

Abstract

Maintainability is a core dimension of software engineering, shaping how code is written, reviewed, and developed over time. While coding agents have demonstrated strong performance on single-issue tasks, it remains unclear how maintainable their code is when future agents build on top of it, potentially leading to compounding downstream effects. We investigate how agent code compares to human code in these maintenance settings, presenting CodeThread, a framework to construct controlled experiments from repository-level coding benchmarks. Applying CodeThread to four frontier coding agents and four benchmarks, we find that agents are less effective at resolving tasks when building on agent code compared to human code, with task resolve rate drops of up to 13.1%. Regression analysis reveals that many traditional software engineering maintainability metrics do not explain this difference. Instead, the clearest signals are subtler behavioral differences in agent code, such as changes to input validation and error handling, along with differences in downstream code size and task difficulty. These findings highlight the need to evaluate these systems not only by immediate task resolution but also by code maintainability, and point to potential sources of downstream errors introduced by agent code.

Problem

• code maintainability
• AI coding agents
• software engineering
• downstream errors
• agent-generated code

Innovation

• CodeThread
• code maintainability
• AI coding agents
• software engineering metrics
• downstream error propagation