Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study
For researchers and practitioners evaluating AI coding agents, this work demonstrates that current outcome-based metrics are misleading and that interaction-aware evaluation is necessary.
This study analyzed 11,048 Agentic-PRs and found that only 35.7% of rejections reflect clear agent failures, while 31.2% are due to workflow constraints and 33.1% lack observable rationale; among merged PRs, 15.4% required explicit reviewer involvement. The results show that PR outcomes alone do not reliably capture agent capability.
AI coding agents increasingly submit pull requests (Agentic-PRs) to open-source repositories, yet their performance is commonly assessed using merge and rejection outcomes alone. We hypothesized that these outcome labels do not reliably reflect agent capability without considering review interactions. To test this, we conducted a decision-oriented analysis of 11,048 closed Agentic Pull Requests, refined to 9,799 human-reviewed PRs, and manually inspected 717 representative cases to recover decision rationale from interaction artifacts. We found that rejection outcomes substantially overstate agent error: only 35.7% of rejected PRs reflected clear agentic failures, while 31.2% were driven by workflow constraints and 33.1% lacked observable decision rationale. Among merged PRs, 15.4% required explicit reviewer involvement through feedback or direct commits, and 5.5% showed no visible interaction trace. We further observed systematic differences across agents, with Copilot and Devin more often embedded in reviewer-mediated workflows, while Codex and Cursor PRs were typically merged with minimal interaction. These results reject the assumption that PR outcomes alone capture agent performance and demonstrate the need for interaction-aware evaluation grounded in review behavior.