Agentic AI increasingly intervenes proactively by inferring users' situations from contextual data yet often fails for lack of principled judgment about when, why, and whether to act. We address this gap by proposing a conceptual model that reframes behavior as an interpretive outcome integrating Scene (observable situation), Context (user-constructed meaning), and Human Behavior Factors (determinants shaping behavioral likelihood). Grounded in multidisciplinary perspectives across the humanities, social sciences, HCI, and engineering, the model separates what is observable from what is meaningful to the user and explains how the same scene can yield different behavioral meanings and outcomes. To translate this lens into design action, we derive five agent design principles (behavioral alignment, contextual sensitivity, temporal appropriateness, motivational calibration, and agency preservation) that guide intervention depth, timing, intensity, and restraint. Together, the model and principles provide a foundation for designing agentic AI systems that act with contextual sensitivity and judgment in interactions.

翻译：随着智能体AI日益通过上下文数据推断用户情境而主动介入，其常因缺乏关于何时、为何以及是否应采取行动的原则性判断而失效。本文通过提出一个概念模型来弥合这一差距，该模型将行为重新定义为融合场景（可观察情境）、语境（用户建构的意义）与人类行为因素（塑造行为可能性的决定要素）的阐释性结果。该模型根植于人文学科、社会科学、人机交互及工程学等多学科视角，将可观察要素与对用户有意义的内容相分离，并阐释了同一场景如何产生不同的行为意义与结果。为将这一视角转化为设计实践，我们推导出五项智能体设计原则（行为对齐、语境敏感性、时序适当性、动机校准与能动性保持），用以指导干预的深度、时机、强度与克制。该模型与原则共同为设计具有语境敏感性与交互判断力的智能体AI系统奠定了理论基础。