The bottleneck of useful agentic intelligence has shifted from compressing world knowledge into a single model to executing a coordinated system. This position paper argues that personal-agent architecture must move to the edge because the core properties of agentic intelligence tasks, particularly their structural coupling with high-fidelity local context and the need for zero-latency execution loops, do not sit well with cloud-centric designs. We develop this claim through three structural shifts. First, the Prefrontal Turn: the main marginal lever of capability has moved from pre-training scale to framework-level executive control. Such control must remain physically close to the environment of action if the agent is to preserve cognitive alignment. Second, the Data-Geography Paradox, the ``dark matter'' of agentic data (local file hierarchies, real-time sensor streams, and transient OS states) degrades, disappears, or loses meaning once prepared for cloud transmission, thereby cutting the agent off from ground-truth context. Third, the interaction-alignment loop, the only economically and ecologically sustainable source of agentic refinement data is the high-fidelity implicit preference signal produced through real-time local interaction. Third, the interaction-alignment loop, the only economically and ecologically sustainable source of agentic refinement data is the high-fidelity implicit preference signal produced through real-time local interaction. We conclude with falsifiable predictions for the next deployment cycle of personal agents.

翻译：实用智能体智能的瓶颈已从将世界知识压缩至单一模型，转向协调系统执行。本文立场认为，个人智能体架构必须走向边缘，因为智能体任务的核心特性——特别是其与高保真局部上下文的结构耦合，以及对零延迟执行循环的需求——与云中心化设计难以兼容。我们通过三个结构性转变阐述这一主张。首先，前额叶转向：能力的主要边际杠杆已从预训练规模化转向框架级执行控制。若智能体要保持认知对齐，这种控制必须在物理上接近行动环境。其次，数据地理悖论：智能体数据的“暗物质”（如本地文件层级、实时传感器流及瞬态操作系统状态）一旦为云端传输而准备便会退化、消失或丧失意义，从而切断智能体与真实上下文之间的联系。第三，交互对齐循环：智能体优化数据唯一经济且生态可持续的来源，是通过实时本地交互产生的高保真隐式偏好信号。我们针对个人智能体的下一部署周期提出了可证伪的预测。