Agentic AI Empowered Intent-Based Networking for 6G

2026-01-10

arXiv.org

AI Summary

This work addresses the challenge in existing intent-based networking (IBN) approaches of simultaneously achieving flexibility and interpretability in natural language understanding while strictly enforcing technical constraints—a key bottleneck for 6G autonomous orchestration. To bridge this gap, the authors propose a hierarchical multi-agent framework that integrates large language models (LLMs) with domain-expert agents. Leveraging the ReAct reasoning-action loop, the system collaboratively decomposes high-level natural language intents into network slice configurations compliant with RAN and core network constraints. This architecture represents the first approach to enable interpretable, constraint-aware, and iteratively reasoned automatic translation from intent to configuration. Experimental results demonstrate significant performance gains over rule-based systems and direct LLM prompting across diverse benchmark scenarios, validating its effectiveness in O-RAN deployments and highlighting the critical role of context-aware prompt engineering in network automation.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models
• Multiagent Systems: Modeling other Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Intent-Based Networking
• 6G
• Autonomous Orchestration
• Network Configuration
• Large Language Models

Innovation

• Agentic AI
• Intent-Based Networking
• Large Language Models
• ReAct Framework
• 6G Network Automation