The transition towards sixth-generation (6G) wireless networks necessitates autonomous orchestration mechanisms capable of translating high-level operational intents into executable network configurations. Existing approaches to Intent-Based Networking (IBN) rely upon either rule-based systems that struggle with linguistic variation or end-to-end neural models that lack interpretability and fail to enforce operational constraints. This paper presents a hierarchical multi-agent framework where Large Language Model (LLM) based agents autonomously decompose natural language intents, consult domain-specific specialists, and synthesise technically feasible network slice configurations through iterative reasoning-action (ReAct) cycles. The proposed architecture employs an orchestrator agent coordinating two specialist agents, i.e., Radio Access Network (RAN) and Core Network agents, via ReAct-style reasoning, grounded in structured network state representations. Experimental evaluation across diverse benchmark scenarios shows that the proposed system outperforms rule-based systems and direct LLM prompting, with architectural principles applicable to Open RAN (O-RAN) deployments. The results also demonstrate that whilst contemporary LLMs possess general telecommunications knowledge, network automation requires careful prompt engineering to encode context-dependent decision thresholds, advancing autonomous orchestration capabilities for next-generation wireless systems.

翻译：向第六代（6G）无线网络的演进需要能够将高层级操作意图转化为可执行网络配置的自主编排机制。现有的意图驱动网络方法要么依赖于难以处理语言变体的基于规则的系统，要么依赖于缺乏可解释性且无法强制执行操作约束的端到端神经模型。本文提出了一种分层多智能体框架，其中基于大语言模型的智能体能够自主分解自然语言意图、咨询领域特定专家，并通过迭代推理-行动循环合成技术可行的网络切片配置。所提出的架构采用一个编排器智能体，通过基于结构化网络状态表示的ReAct式推理，协调无线接入网和核心网两个专家智能体。在多样化基准场景下的实验评估表明，该系统性能优于基于规则的系统及直接LLM提示方法，其架构原则适用于开放式无线接入网部署。结果还表明，虽然当代大语言模型具备通用电信知识，但网络自动化仍需通过精心设计的提示工程来编码上下文相关的决策阈值，从而提升下一代无线系统的自主编排能力。