Current in-IDE AI coding tools typically rely on time-consuming manual prompting and context management, whereas proactive alternatives that anticipate developer needs without explicit invocation remain underexplored. Understanding when humans are receptive to such proactive AI assistance during their daily work remains an open question in human-AI interaction research. We address this gap through a field study of proactive AI assistance in professional developer workflows. We present a five-day in-the-wild study with 15 developers who interacted with a proactive feature of an AI assistant integrated into a production-grade IDE that offers code quality suggestions based on in-IDE developer activity. We examined 229 AI interventions across 5,732 interaction points to understand how proactive suggestions are received across workflow stages, how developers experience them, and their perceived impact. Our findings reveal systematic patterns in human receptivity to proactive suggestions: interventions at workflow boundaries (e.g., post-commit) achieved 52% engagement rates, while mid-task interventions (e.g., on declined edit) were dismissed 62% of the time. Notably, well-timed proactive suggestions required significantly less interpretation time than reactive suggestions (45.4s versus 101.4s, W = 109.00, r = 0.533, p = 0.0016), indicating enhanced cognitive alignment. This study provides actionable implications for designing proactive coding assistants, including how to time interventions, align them with developer context, and strike a balance between AI agency and user control in production IDEs.

翻译：当前集成开发环境（IDE）中的AI编程工具通常依赖于耗时的手动提示和上下文管理，而能够预见开发者需求、无需显式调用的主动式替代方案仍未被充分探索。在人类与AI交互研究中，理解人类在日常工作中何时愿意接受此类主动式AI辅助仍是一个开放性问题。我们通过对专业开发者工作流程中主动式AI辅助的实地研究来填补这一空白。我们开展了一项为期五天的真实环境研究，邀请了15名开发者与集成在生产级IDE中的AI助手的一个主动功能进行交互，该功能基于IDE内的开发者活动提供代码质量建议。我们检查了5,732个交互点中的229次AI干预，以了解主动建议在不同工作流程阶段如何被接受、开发者的体验如何以及其感知影响。我们的研究结果揭示了人类对主动建议接受度的系统性模式：在工作流边界（例如提交后）的干预获得了52%的参与率，而任务中期（例如在拒绝编辑时）的干预则有62%被直接忽略。值得注意的是，时机恰当的主动建议所需的解读时间显著少于被动建议（45.4秒对比101.4秒，W = 109.00，r = 0.533，p = 0.0016），这表明了认知对齐度的提升。本研究为设计主动式编程助手提供了可行的启示，包括如何把握干预时机、使其与开发者上下文对齐，以及在生产级IDE中平衡AI自主性与用户控制。