Human collaborators coordinate dynamically through process visibility and workspace awareness, yet AI agents typically either provide only final outputs or expose read-only execution processes (e.g., planning, reasoning) without interpreting concurrent user actions on shared artifacts. Building on mixed-initiative interaction principles, we explore whether agents can achieve collaborative context awareness -- interpreting concurrent user actions on shared artifacts and adapting in real-time. Study 1 (N=10 professional designers) revealed that process visibility enabled reasoning about agent actions but exposed conflicts when agents could not distinguish feedback from independent work. We developed CLEO, which interprets collaborative intent and adapts in real-time. Study 2 (N=10, two-day with stimulated recall interviews) analyzed 214 turns, identifying five action patterns, six triggers, and four enabling factors explaining when designers choose delegation (70.1%), direction (28.5%), or concurrent work (31.8%). We present a decision model with six interaction loops, design implications, and an annotated dataset.

翻译：人类协作者通过过程可见性和工作空间感知实现动态协调，而人工智能代理通常仅提供最终输出或暴露只读执行过程（如规划、推理），无法解释用户对共享工件的并发操作。基于混合主动交互原则，我们探讨代理能否实现协作情境感知——即解释用户对共享工件的并发操作并实时适应。研究一（N=10名专业设计师）表明，过程可见性支持对代理行为的推理，但当代理无法区分反馈与独立工作时会暴露冲突。我们开发了能解释协作意图并实时适应的CLEO系统。研究二（N=10人，为期两天并辅以刺激回忆访谈）分析了214轮交互，识别出五种行动模式、六种触发因素和四种促成因素，解释了设计师选择委托（70.1%）、指导（28.5%）或并发工作（31.8%）的决策情境。我们提出了包含六个交互回路的决策模型、设计启示及带标注的数据集。