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与工作空间感知实现动态协调，而AI智能体通常仅提供最终输出或暴露只读执行过程（如规划、推理），却无法解释用户对共享工件执行的并发操作。基于混合主动式交互原则，我们探索智能体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调整。研究1（N=10名专业设计师）发现，过程可见性使用户能推理智能体行为，但当智能体无法区分反馈与独立工作时会暴露冲突。我们开发了CLEO系统，可解释协作意图并实时调整。研究2（N=10，为期两天配合刺激回忆访谈）分析了214次交互回合，识别出五种行为模式、六个触发因素及四个促进因素，揭示了设计师选择委派（70.1%）、指示（28.5%）或并发工作（31.8%）的条件。我们提出了包含六类交互循环的决策模型、设计启示及一个标注数据集。