The Internet of Agents is propelling edge computing toward agentic AI and edge general intelligence (EGI). However, deploying multi-agent service (MAS) on resource-constrained edge infrastructure presents severe challenges. MAS service workflows are driven by complex cross-node interactions, dynamic memory accumulation, and collaborative tool usage. Exhibiting chain-like topological dependencies and strict affinity constraints, these workflows demand real-time responsiveness that exceeds the capabilities of traditional VNE algorithms designed for static resources. To address this, we propose AgentVNE, a cloud-edge collaborative framework utilizing a dual-layer architecture. First, AgentVNE employs a large language model (LLM) to identify implicit semantic constraints and generate affinity-based resource augmentation to resolve physical dependency issues. Second, it constructs a resource similarity-aware neural network, utilizing a pre-training and PPO fine-tuning strategy to precisely capture topological similarities between dynamic workflows and heterogeneous networks. By coupling semantic perception with topological reasoning, this mechanism effectively bridges the gap between dynamic service requirements and physical infrastructure. Simulation results demonstrate that AgentVNE reduces workflow communication latency to less than 40% of baselines and improves the service acceptance rate by approximately 5%-10% under high-load scenarios. Ultimately, this work provides a foundational solution for the semantic-aware deployment of agentic AI.

翻译：智能体互联网正推动边缘计算迈向智能体AI与边缘通用智能（EGI）时代。然而，在资源受限的边缘基础设施上部署多智能体服务（MAS）面临严峻挑战。MAS服务流程由复杂的跨节点交互、动态内存累积及协作工具使用驱动，呈现出链式拓扑依赖与严格亲和性约束，其所需的实时响应能力已超越传统面向静态资源的虚拟网络嵌入（VNE）算法。为此，我们提出AgentVNE——一个采用双层架构的云边协同框架。首先，AgentVNE利用大语言模型（LLM）识别隐式语义约束，生成基于亲和性的资源增强方案以解决物理依赖问题；其次，构建资源相似性感知神经网络，通过预训练与PPO微调策略精确捕捉动态服务流程与异构网络间的拓扑相似性。该机制通过语义感知与拓扑推理的耦合，有效弥合了动态服务需求与物理基础设施间的鸿沟。仿真结果表明，AgentVNE将服务流程通信延迟降至基线方法的40%以下，并在高负载场景下将服务接受率提升约5%-10%。本研究最终为智能体AI的语义感知部署提供了基础性解决方案。