Agentic AI networking (AgentNet) is a novel AI-native networking paradigm in which a large number of specialized AI agents collaborate to perform autonomous decision-making, dynamic environmental adaptation, and complex missions. It has the potential to facilitate real-time network management and optimization functions, including self-configuration, self-optimization, and self-adaptation across diverse and complex environments. This paper proposes SANet, a novel semantic-aware AgentNet architecture for wireless networks that can infer the semantic goal of the user and automatically assign agents associated with different layers of the network to fulfill the inferred goal. Motivated by the fact that AgentNet is a decentralized framework in which collaborating agents may generally have different and even conflicting objectives, we formulate the decentralized optimization of SANet as a multi-agent multi-objective problem, and focus on finding the Pareto-optimal solution for agents with distinct and potentially conflicting objectives. We propose three novel metrics for evaluating SANet. Furthermore, we develop a model partition and sharing (MoPS) framework in which large models, e.g., deep learning models, of different agents can be partitioned into shared and agent-specific parts that are jointly constructed and deployed according to agents' local computational resources. Two decentralized optimization algorithms are proposed. We derive theoretical bounds and prove that there exists a three-way tradeoff among optimization, generalization, and conflicting errors. We develop an open-source RAN and core network-based hardware prototype that implements agents to interact with three different layers of the network. Experimental results show that the proposed framework achieved performance gains of up to 14.61% while requiring only 44.37% of FLOPs required by state-of-the-art algorithms.

翻译：智能体AI网络（AgentNet）是一种新型的AI原生网络范式，其中大量专业化的AI智能体协同完成自主决策、动态环境适应和复杂任务。该范式有望赋能包括自配置、自优化和自适应的实时网络管理与优化功能，从而在多样化复杂环境中运行。本文提出SANet——一种面向无线网络的语义感知AgentNet架构，能够推断用户的语义目标，并自动分配与网络不同层相关联的智能体以实现推断目标。鉴于AgentNet是一个去中心化框架，其中协作智能体通常具有不同甚至冲突的目标，我们将SANet的去中心化优化建模为多智能体多目标问题，并重点为具有独特且潜在冲突目标的智能体寻找Pareto最优解。我们提出三个评估SANet的新指标。此外，我们开发了模型分区与共享（MoPS）框架，其中不同智能体的大型模型（如深度学习模型）可被划分为共享部分和智能体专属部分，并根据智能体本地计算资源联合构建与部署。我们提出两种去中心化优化算法，推导了理论界，并证明优化、泛化和冲突误差之间存在三向权衡。我们开发了基于开源RAN与核心网的硬件原型，实现智能体与网络三个不同层的交互。实验结果表明，所提框架在仅需最先进算法44.37%的FLOPs的前提下，实现了最高14.61%的性能提升。