Fog and edge computing require adaptive control schemes that can handle partial observability, severe latency requirements, and dynamically changing workloads. Recent research on Agentic AI (AAI) increasingly integrates reasoning systems powered by Large Language Models; however, these tools are not applicable to infrastructure-level systems due to their high computational cost, stochastic nature, and poor formal analyzability. In this paper, a generic model, Agentic Fog (AF), is presented, in which fog nodes are represented as policy-driven autonomous agents that communicate via p2p interactions based on shared memory and localized coordination. The suggested architecture decomposes a system's goals into abstract policy guidance and formalizes decentralized fog coordination as an exact potential game. The framework is guaranteed to converge and remain stable under asynchronous updates, bounded-rational best-response dynamics, and node failures. Simulations demonstrate that the AF system achieves lower average latency and adapts more efficiently to varying demand than greedy heuristics and integer linear programming under dynamic conditions. The sensitivity analysis also demonstrates the capability to perform optimally under different memory and coordination conditions.

翻译：雾计算与边缘计算需要能够处理部分可观测性、严格延迟要求以及动态变化工作负载的自适应控制方案。近期关于代理人工智能的研究日益融合由大型语言模型驱动的推理系统；然而，由于这些工具存在计算成本高、随机性强及形式化可分析性差等问题，它们并不适用于基础设施级系统。本文提出一种通用模型——代理雾，其中雾节点被表示为策略驱动的自主智能体，通过基于共享内存与局部协调的对等交互进行通信。所提出的架构将系统目标分解为抽象策略指导，并将去中心化的雾协调形式化为精确势博弈。该框架在异步更新、有限理性最优响应动态及节点故障条件下均能保证收敛性与稳定性。仿真实验表明，在动态环境下，代理雾系统相比贪婪启发式算法与整数线性规划方法，能够实现更低的平均延迟，并对变化需求表现出更高效的适应能力。敏感性分析进一步证明了该系统在不同内存与协调条件下保持最优性能的能力。