Aligning agentic AI with user intent is critical for delegating complex, socially embedded tasks, yet user preferences are often implicit, evolving, and difficult to specify upfront. We present DoubleAgents, a system for human-agent alignment in coordination tasks, grounded in distributed cognition. DoubleAgents integrates three components: (1) a coordination agent that maintains state and proposes plans and actions, (2) a dashboard visualization that makes the agent's reasoning legible for user evaluation, and (3) a policy module that transforms user edits into reusable alignment artifacts, including coordination policies, email templates, and stop hooks, which improve system behavior over time. We evaluate DoubleAgents through a two-day lab study (n=10), three real-world deployments, and a technical evaluation. Participants' comfort in offloading tasks and reliance on DoubleAgents both increased over time, correlating with the three distributed cognition components. Participants still required control at points of uncertainty - edge-case flagging and context-dependent actions. We contribute a distributed cognition approach to human-agent alignment in socially embedded tasks.

翻译：将智能体AI与用户意图对齐对于委托复杂的、社会嵌入性任务至关重要，然而用户偏好往往是隐式的、动态演变的，且难以预先明确指定。我们提出DoubleAgents系统，用于协调任务中的人机对齐，该方法基于分布式认知理论。DoubleAgents整合了三个组件：(1) 维护状态并提出计划和行动的协调智能体，(2) 使智能体推理过程可视化以供用户评估的仪表盘界面，以及(3) 将用户编辑转化为可复用对齐工件（包括协调策略、邮件模板和停止钩子）的策略模块，这些工件能逐步改善系统行为。我们通过为期两天的实验室研究（n=10）、三次真实世界部署以及技术评估对DoubleAgents进行验证。参与者对任务委托的舒适度及对DoubleAgents的依赖程度均随时间推移而提升，这与三个分布式认知组件呈相关性。在不确定性节点（边缘情况标记和情境依赖行动）上，参与者仍需要保持控制权。我们为社会嵌入任务中的人机对齐贡献了一种分布式认知方法。