As agents move into shared workspaces and their execution becomes visible, human-agent collaboration faces a fundamental shift from sequential delegation to concurrent co-creation. This raises a new coordination problem: what interaction patterns emerge, and what agent capabilities are required to support them? Study 1 (N=10) revealed that process visibility naturally prompted concurrent intervention, but exposed a critical capability gap: agents lacked the collaborative context awareness needed to distinguish user feedback from independent parallel work. This motivated CLEO, a design probe that embodies this capability, interpreting concurrent user actions as feedback or independent work and adapting execution accordingly. Study 2 (N=10) analyzed 214 turn-level interactions, identifying a taxonomy of five action patterns and ten codes, along with six triggers and four enabling factors explaining when and why users shift between collaboration modes. Concurrent interaction appeared in 31.8% of turns. We present a decision model, design implications, and an annotated dataset, positioning concurrent interaction as what makes delegation work better.

翻译：随着智能体进入共享工作空间且其执行过程变得可见，人机协作正面临从顺序委派向并发共创的根本性转变。这引发了一个新的协调问题：会出现哪些交互模式？需要智能体具备哪些能力来支持这些模式？研究1（N=10）表明，过程可见性自然地触发了并发干预，但暴露出一个关键能力缺口：智能体缺乏区分用户反馈与独立并行工作所必需的协作情境意识。这促使了CLEO的设计——一个体现该能力的设计探针，它能够将用户的并发行为解读为反馈或独立工作，并据此调整执行过程。研究2（N=10）分析了214个回合级交互，识别出包含五种行为模式和十种编码的分类体系，以及六种触发条件和四种使能因素，用以解释用户何时及为何在协作模式间切换。并发交互出现在31.8%的回合中。我们提出了一个决策模型、设计启示和一个带标注的数据集，将并发交互定位为提升委派协作效能的关键要素。