Autonomous coding agents are generating code at an unprecedented scale, with OpenAI Codex alone creating over 400,000 pull requests (PRs) in two months. As agentic PR volumes increase, code review agents (CRAs) have become routine gatekeepers in development workflows. Industry reports claim that CRAs can manage 80% of PRs in open source repositories without human involvement. As a result, understanding the effectiveness of CRA reviews is crucial for maintaining developmental workflows and preventing wasted effort on abandoned pull requests. However, empirical evidence on how CRA feedback quality affects PR outcomes remains limited. The goal of this paper is to help researchers and practitioners understand when and how CRAs influence PR merge success by empirically analyzing reviewer composition and the signal quality of CRA-generated comments. From AIDev’s 19,450 PRs, we analyze 3,109 unique PRs in Commented review state, comparing human-only versus CRA-only reviews. We examine 98 closed CRA-only PRs to assess whether low signal-to-noise ratios contribute to abandonment. CRA-only PRs achieve a 45.20% merge rate, 23.17 percentage points lower than human-only PRs (68.37%), with significantly higher abandonment. Our signal-to-noise analysis reveals that 60.2% of closed CRA-only PRs fall into the 0–30% signal range, and 12 of 13 CRAs exhibit average signal ratios below 60%, indicating substantial noise in automated review feedback. These findings suggest that CRAs without human oversight often generate low-signal feedback associated with higher abandonment. For practitioners, our results indicate that CRAs should augment rather than replace human reviewers, and that human involvement remains critical for effective and actionable code review.

AI coding agents are increasingly integrated into modern software engineering workflows, actively collaborating with human developers to create pull requests (PRs) in open-source repositories. Although coding agents improve developer productivity, they often generate code with more bugs and security issues than human-authored code. While human-authored PRs often break backward compatibility, leading to breaking changes, the potential for agentic PRs to introduce breaking changes remains underexplored. The goal of this paper is to help developers and researchers evaluate the reliability of AI-generated PRs by examining the frequency and task contexts in which AI agents introduce breaking changes.