agentic AI需更多解释而非互动，以维持用户掌控感。
Less Interaction But More Explanation: A Communication Perspective on Agentic AI Interfaces
- 从沟通视角看，AI应主动说明行动逻辑、不确定性和协作关系。
- 用户需更深入理解AI行为，而非简单问答，才能建立信任。
- 支持自定义解释的界面设计，有助于保护用户主导权。
传统AI以交互为核心，而代理型AI的目标是代表用户执行工作流。随着AI自主性增强，用户虽减少日常对话，却需要更多关于行动过程、不确定性及协作机制的解释，以实现有效监督。基于沟通理论，我们指出用户对AI角色的认知（是行动发起者还是传递通道）直接影响信任建立。由于代理型AI可承担多重沟通角色，易引发身份混淆与风险。为此，我们提出三类关键解释：行动过程解释、不确定性说明和协调机制披露，并建议通过可定制的解释呈现方式，让用户自主决定何时何地获取解释，从而在提升AI自治的同时保障人类控制力。
原文摘要 · Abstract (English)
AI systems have long been expected to interact with users, answering questions, generating content, and continuing (social) conversations. Agentic AI, however, breaks from this expectation, as its primary objective is workflow execution on behalf of the users. If a system becomes more agentic, do users need less interaction with the system? Our answer is: less routine back-and-forth, but more communication for oversight and explanation, as agentic AI proactively acts, not just responds. Grounded in a communication perspective, we discuss how users perceive the communicative roles of AI systems (whether as the source of actions or merely a channel), and how this can shape trust. Because agentic AI can play multiple communicative roles, it can complicate this source perception and introduce potential risks. To address this, we propose three types of explanations that agentic AI needs to incorporate (action-process, uncertainty, and coordination), and suggest that customization affordances that allow users to decide when and which explanations they see may be key to preserving human agency as AI autonomy increases.
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