Mobile agentic AI is extending autonomous capabilities to resource-constrained platforms such as edge robots and unmanned aerial vehicles (UAVs), where strict size, weight, power, and cost (SWAP-C) constraints and intermittent wireless connectivity limit both on-device computation and cloud access. Existing approaches mostly optimize per-round communication efficiency, yet mobile agents must sustain competence across a stream of tasks. We propose a knowledge-driven reasoning framework that extracts reusable decision structures from past execution, synchronizes them over bandwidth-limited links, and injects them into on-device reasoning to reduce latency, energy, and error accumulation. A DIKW-inspired taxonomy distinguishes raw observations, episode-scoped traces, and persistent cross-task knowledge, and categorizes knowledge into retrieval, structured, procedural, and parametric representations, each with a distinct tradeoff between reasoning speedup and failure risk. A key finding is that knowledge exposure is non-monotonic: too little forces costly trial-and-error replanning, while too much introduces conflicting cues and errors. A UAV case study validates the framework, where a compact knowledge pack synchronized over intermittent backhaul enables a 3B-parameter onboard model to achieve perfect mission reliability with lower reasoning cost than both knowledge-free on-device reasoning and cloud-centric replanning.

翻译：移动智能体人工智能正在将自主能力扩展到资源受限平台，如边缘机器人和无人机，其中严格的尺寸、重量、功率和成本约束以及间歇性无线连接限制了设备端计算和云端访问。现有方法主要优化单轮通信效率，但移动智能体必须在一系列任务中持续保持能力。我们提出了一个知识驱动的推理框架，该框架从过往执行中提取可重用的决策结构，通过带宽受限链路同步这些结构，并将其注入设备端推理以减少延迟、能耗和错误累积。受DIKW启发的分类法区分了原始观测、任务内轨迹和跨任务持久知识，并将知识分类为检索式、结构化、过程式和参数化表示，每种表示在推理加速与失败风险之间存在不同的权衡。一个关键发现是知识暴露具有非单调性：过少会迫使成本高昂的试错式重规划，而过多则会引入冲突线索和错误。一项无人机案例研究验证了该框架，其中通过间歇性回程链路同步的紧凑知识包，使一个30亿参数的机载模型能以低于无知识设备端推理和以云端为中心的重规划的推理成本，实现完美的任务可靠性。