Not All Agents Are Equal: Code Quality and Post-Merge Maintenance Across Five Autonomous Coding Agents in the Wild

Abstract

Autonomous coding agents now open pull requests in public repositories at a scale that was out of reach two years ago, yet little is known about what happens to that code after it lands. This paper studies 37,623 provenance-labeled pull requests (PRs) from five commercial agents (OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code) and a matched human baseline, drawn from 2,807 GitHub repositories between December 2024 and July 2025. We combine the AIDev dataset with 58,792 cached GitHub API responses to measure security smells in added code, structural maintainability, post-merge churn, revert rates, and human review behavior. Three results stand out. First, quality differences are vendor-specific rather than uniform: Codex-authored PRs were reverted about half as often as human PRs (6.1% vs. 11.5%, odds ratio 0.50), while Devin PRs were reverted more often (14.5%, odds ratio 1.31). Second, agent code pooled across vendors was less likely than human code to contain a security smell (odds ratio 0.63), driven by fewer hardcoded credentials and eval-style constructs. Third, review effort concentrates unevenly: Copilot PRs drew the most human reviews and change requests, and Claude Code PRs waited the longest for a first human review (median 12.6 hours). All pipeline code, statistical reports, and figures are released for replication.