As AI agents take on increasingly long-running tasks involving sophisticated planning and execution, there is a corresponding need for novel interaction designs that enable deeper human-agent collaboration. However, most prior works leverage human interaction to fix "autonomous" workflows that have yet to become fully autonomous or rigidly treat planning and execution as separate stages. Based on a formative study with 9 researchers using AI to support their work, we propose a design that affords greater flexibility in collaboration, so that users can 1) delegate agency to the user or agent via a collaborative plan where individual steps can be assigned; and 2) interleave planning and execution so that plans can adjust after partial execution. We introduce Cocoa, a system that takes design inspiration from computational notebooks to support complex research tasks. A lab study (n=16) found that Cocoa enabled steerability without sacrificing ease-of-use, and a week-long field deployment (n=7) showed how researchers collaborated with Cocoa to accomplish real-world tasks.

翻译：随着AI智能体承担起涉及复杂规划与执行的长期运行任务，对支持更深层次人机协作的新型交互设计需求也相应增长。然而，现有研究大多利用人工交互来修正尚未完全自主的“自动化”工作流，或将规划与执行机械地视为独立阶段。基于对9位使用AI辅助研究工作的科研人员开展的先导性研究，我们提出了一种支持更灵活协作的设计方案，使用户能够：1）通过可分配具体步骤的协作式规划，将任务执行权委托给用户或智能体；2）实现规划与执行的交错推进，使得计划在部分执行后得以动态调整。我们推出Cocoa系统，其设计灵感源于计算笔记本，旨在支持复杂研究任务。实验室研究（n=16）表明Cocoa在保持易用性的同时实现了高度可操控性；为期一周的实地部署（n=7）则展示了研究人员如何与Cocoa协作完成实际任务。