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This paper introduces a hierarchical multi-agent framework for Intent-Based Networking (IBN) in 6G, using LLM-based agents to decompose natural language intents into executable network configurations. The framework employs an orchestrator agent coordinating RAN and Core Network specialist agents via ReAct cycles, grounded in structured network state representations. Experiments demonstrate the system's superiority over rule-based systems and direct LLM prompting in diverse network slice configuration scenarios, highlighting the need for careful prompt engineering to encode context-dependent decision thresholds for effective network automation.
LLM-powered agents can autonomously orchestrate 6G network slices from natural language intents, but only with a carefully designed architecture and prompts that encode domain-specific constraints.
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.
