# Agentic AI Empowered Intent-Based Networking for 6G arXiv: https://arxiv.org/abs/2601.06640 ยท HTML: https://mlsanity.com/paper/2601.06640/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. - Builds on 25 of its 47 references that are in the map. - First posted on arXiv 10 Jan 2026. ## Placed in - LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/) ## Builds on (most cited first, 25 of 25) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023) - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023) - [Prompting GPT-3 To Be Reliable](https://mlsanity.com/paper/2210.09150/) (2022) - [Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks](https://mlsanity.com/paper/2307.02477/) (2023) - [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018) - [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019) - [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018) - [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019) - [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024) - [NetLLM: Adapting Large Language Models for Networking](https://mlsanity.com/paper/2402.02338/) (2024) - [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023) - [Large Language Model Enhanced Multi-Agent Systems for 6G Communications](https://mlsanity.com/paper/2312.07850/) (2023) - [Large Generative AI Models for Telecom: The Next Big Thing?](https://mlsanity.com/paper/2306.10249/) (2023) - [TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models](https://mlsanity.com/paper/2407.09424/) (2024) - [Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale](https://mlsanity.com/paper/1801.01270/) (2018) - [Agentic AI Software Engineers: Programming with Trust](https://mlsanity.com/paper/2502.13767/) (2025) - [Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction](https://mlsanity.com/paper/2508.05294/) (2025) - [Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling](https://mlsanity.com/paper/2510.26603/) (2025) - [The ITU Vision and Framework for 6G: Scenarios, Capabilities and Enablers](https://mlsanity.com/paper/2305.13887/) (2023) - [NeutRAN: An Open RAN Neutral Host Architecture for Zero-Touch RAN and Spectrum Sharing](https://mlsanity.com/paper/2301.07653/) (2023) ## Built on by (most cited first, 7 of 7) - [6G-Bench: An Open Benchmark for Semantic Communication and Network-Level Reasoning with Foundation Models in AI-Native 6G Networks](https://mlsanity.com/paper/2602.08675/) (2026) - [6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence](https://mlsanity.com/paper/2605.01546/) (2026) - [Beyond State Machines: Executing Network Procedures with Agentic Tool-Calling Sequences](https://mlsanity.com/paper/2605.02584/) (2026) - [GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing](https://mlsanity.com/paper/2605.27360/) (2026) - [Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN](https://mlsanity.com/paper/2605.10036/) (2026) - [Benchmarking LLM-Guided Control-Plane Policies for Backend Fault Isolation in HAProxy](https://mlsanity.com/paper/2608.10532/) (2026) - [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026) ## Abstract (arXiv) 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.