Agentic workflows promise efficiency, but adoption hinges on whether people can align systems that act on their behalf with their goals, values, and situational expectations. We present DoubleAgents, an agentic planning tool that embeds transparency and control through user intervention, value-reflecting policies, rich state visualizations, and uncertainty flagging for human coordination tasks. A built-in respondent simulation generates realistic scenarios, allowing users to rehearse and refine policies and calibrate their use of agentic behavior before live deployment. We evaluate DoubleAgents in a two-day lab study (n = 10), three deployment studies, and a technical evaluation. Results show that participants initially hesitated to delegate but used simulation to probe system behavior and adjust policies, gradually increasing delegation as agent actions became better aligned with their intentions and context. Deployment results demonstrate DoubleAgents' real-world relevance and usefulness, showing that simulation helps users effectively manage real-world tasks with higher complexity and uncertainty. We contribute interactive simulation as a practical pathway for users to iteratively align and calibrate agentic systems.

翻译：智能体工作流虽能提升效率，但其应用成效取决于人们能否使代表其行动的智能体系统与自身目标、价值观及情境预期保持一致。本文提出DoubleAgents——一种通过用户干预、价值映射策略、丰富状态可视化及不确定性标注机制实现透明可控的智能体规划工具，专为人类协同任务设计。该工具内置应答者仿真模块，可生成逼真场景，使用户能在实际部署前通过演练优化策略并校准智能体行为模式。我们通过为期两日的实验室研究（n = 10）、三项部署实验及技术评估对DoubleAgents进行验证。结果表明：参与者初始对委托决策持谨慎态度，但通过仿真探查系统行为并调整策略后，随着智能体行动与其意图及情境的契合度提升，委托意愿逐渐增强。部署实验证实了DoubleAgents在真实场景中的实用价值，表明仿真能帮助用户有效管理更高复杂度与不确定性的现实任务。本研究提出交互式仿真作为用户迭代式对齐与校准智能体系统的实践路径。