Agentic AI has taken on the role of assistant, collaborator, and decision-support tool. We argue the next role on that list is more personal: you. These are digital twins of each individual -- twin agents -- representing their knowledge, perspective, and communicative style to colleagues when they are unavailable. Drawing on early design work in an ongoing project in which agents represent knowledge workers in a professional setting, we identify a trust calibration problem specific to this approach. When a human colleague doubts a twin agent's output, they face three failure modes (a schema gap, an epistemic gap, and a model artifact) with no reliable attribution path between them. Cognitive forcing functions and related frameworks address overreliance effectively in contexts where there is a clear boundary between the AI and the human decision-maker. However, twin agents dissolve that boundary, raising a class of trust calibration challenge these frameworks were not designed to handle. We introduce the concept, distinguish it from digital twins, and outline the research questions this new class of agent demands.

翻译：代理型人工智能已承担起助手、协作者和决策支持工具的角色。我们认为这一列表中的下一个角色将更个人化：你。这些是个体数字孪生体——双生智能体——代表个体的知识、视角和沟通风格，在本人缺席时与同事互动。基于一个正在进行的项目中早期设计工作的经验（其中智能体在专业环境中代表知识工作者），我们识别出该方法特有的信任校准问题。当人类同事怀疑双生智能体的输出时，会面临三种失效模式（模式差距、认知差距和模型伪影），且三者之间缺乏可靠的归因路径。认知强制函数及相关框架虽能有效处理在人工智能与人类决策者之间存在明确边界情境下的过度依赖问题，但双生智能体消解了这一边界，引发了一系列这些框架无法应对的信任校准挑战。我们提出这一概念，将其与数字孪生体区分开来，并概述此类新型智能体所需研究的核心问题。