Agentic AI Empowered Intent-Based Networking for 6G

TL;DR

This paper introduces a hierarchical multi-agent system using LLMs for intent-based network configuration in 6G, improving automation, interpretability, and operational constraint enforcement over existing methods.

Contribution

It proposes a novel multi-agent framework with LLMs and specialist agents for autonomous 6G network orchestration, advancing interpretability and operational reliability.

Findings

Outperforms rule-based and direct LLM prompting systems in benchmarks

Effective in diverse 6G network scenarios and Open RAN deployments

Requires careful prompt engineering for context-dependent decision-making

Abstract

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.