As AI coding agents evolve from autocomplete tools to autonomous "AI workforce" teammates, they introduce a critical new bottleneck: human maintainers must now manage complex interaction loops rather than just reviewing code. Analyzing 33,707 agent-authored PRs, we uncover a stark two-regime reality: agents excel at narrow automation (28.3% of PRs merge instantly), but frequently fail at iterative refinement, leading to "ghosting" (abandonment) when faced with subjective feedback. This creates a hidden "attention tax" on maintainers. We introduce a creation-time Circuit Breaker model to predict high-maintenance PRs before human review begins. By leveraging simple static complexity cues (e.g., file types, patch size), our model identifies the "expensive tail" of contributions with AUC 0.96, enabling a gated triage process. At a 20% review budget, this approach captures 69% of the high-effort PRs, effectively allowing maintainers to fast-fail costly, low-quality agent contributions while fast-tracking simple fixes.

翻译：随着AI编程代理从自动补全工具演变为自主的“AI劳动力”团队成员，它们引入了一个关键的新瓶颈：人类维护者现在必须管理复杂的交互循环，而不仅仅是审查代码。通过分析33,707个由代理撰写的PR，我们揭示了一个鲜明的双模态现实：代理在狭窄的自动化任务上表现出色（28.3%的PR被即时合并），但在迭代优化方面经常失败，导致在面对主观反馈时出现“幽灵化”（被放弃）。这给维护者带来了隐性的“注意力税”。我们引入了一种创建时断路器模型，用于在人工审查开始前预测高维护成本的PR。通过利用简单的静态复杂性线索（例如文件类型、补丁大小），我们的模型以AUC 0.96的准确率识别出贡献的“昂贵尾部”，从而实现门控分流流程。在20%的审查预算下，该方法能捕获69%的高工作量PR，有效允许维护者快速终止成本高昂、低质量的代理贡献，同时快速处理简单的修复。