# Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach arXiv: https://arxiv.org/abs/2503.15764 ยท HTML: https://mlsanity.com/paper/2503.15764/ ## Key facts - Cited by 19 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 16 of its 22 references that are in the map. - First posted on arXiv 20 Mar 2025. ## 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, 16 of 16) - [Agent AI: Surveying the Horizons of Multimodal Interaction](https://mlsanity.com/paper/2401.03568/) (2024) - [Levels of AGI for Operationalizing Progress on the Path to AGI](https://mlsanity.com/paper/2311.02462/) (2023) - [RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion](https://mlsanity.com/paper/2404.09140/) (2024) - [Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks](https://mlsanity.com/paper/2102.03832/) (2021) - [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024) - [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024) - [An Interactive Agent Foundation Model](https://mlsanity.com/paper/2402.05929/) (2024) - [Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning](https://mlsanity.com/paper/2210.16118/) (2022) - [Distributed Optimization for Energy-efficient Fog Computing in the Tactile Internet](https://mlsanity.com/paper/2001.10199/) (2020) - [6G Network AI Architecture for Everyone-Centric Customized Services](https://mlsanity.com/paper/2205.09944/) (2022) - [Generative AI for Game Theory-based Mobile Networking](https://mlsanity.com/paper/2404.09699/) (2024) - [The Seven Worlds and Experiences of the Wireless Metaverse: Challenges and Opportunities](https://mlsanity.com/paper/2304.10282/) (2023) - [Towards an AI/ML-driven SMO Framework in O-RAN: Scenarios, Solutions, and Challenges](https://mlsanity.com/paper/2409.05092/) (2024) - [SANSee: A Physical-layer Semantic-aware Networking Framework for Distributed Wireless Sensing](https://mlsanity.com/paper/2410.19795/) (2024) - [Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach](https://mlsanity.com/paper/2302.00207/) (2023) - [Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond](https://mlsanity.com/paper/2401.01007/) (2023) ## Built on by (most cited first, 19 of 19) - [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025) - [LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach](https://mlsanity.com/paper/2505.12247/) (2025) - [SANet: A Semantic-aware Agentic AI Networking Framework for Cross-layer Optimization in 6G](https://mlsanity.com/paper/2512.22579/) (2025) - [Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?](https://mlsanity.com/paper/2508.05670/) (2025) - [A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks](https://mlsanity.com/paper/2510.19973/) (2025) - [SANNet: A Semantic-Aware Agentic AI Networking Framework for Multi-Agent Cross-Layer Coordination](https://mlsanity.com/paper/2505.18946/) (2025) - [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025) - [Toward an Unbiased Collective Memory for Efficient LLM-Based Agentic 6G Cross-Domain Management](https://mlsanity.com/paper/2509.26200/) (2025) - [Agentic AI for Mobile Network RAN Management and Optimization](https://mlsanity.com/paper/2511.02532/) (2025) - [Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows](https://mlsanity.com/paper/2512.20640/) (2025) - [Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks](https://mlsanity.com/paper/2509.06775/) (2025) - [Distributed Information Bottleneck Theory for Multi-Modal Task-Aware Semantic Communication](https://mlsanity.com/paper/2510.04000/) (2025) - [Agentic AI-Based Joint Computing and Networking via Mixture of Experts and Large Language Models](https://mlsanity.com/paper/2605.02911/) (2026) - [SANEmerg: An Emergent Communication Framework for Semantic-aware Agentic AI Networking](https://mlsanity.com/paper/2605.05861/) (2026) - [Synchronization Dynamics of Heterogeneous, Collaborative Multi-Agent AI Systems](https://mlsanity.com/paper/2508.12314/) (2025) - [From Passive to Proactive: A Hierarchical Multi-Agent Framework for Automated Medical Pre-Consultation](https://mlsanity.com/paper/2511.01445/) (2025) - [Generalization Bounds of Emergent Communications for Agentic AI Networking](https://mlsanity.com/paper/2605.08613/) (2026) - [Skillsets on the Chain: A Blockchain-based Zero-Trust Framework for Agentic AI Networking](https://mlsanity.com/paper/2608.00104/) (2026) - [When Agentic AI Meets Integrated Sensing and Communication](https://mlsanity.com/paper/2608.05792/) (2026) ## Abstract (arXiv) The promising potential of AI and network convergence in improving networking performance and enabling new service capabilities has recently attracted significant interest. Existing network AI solutions, while powerful, are mainly built based on the close-loop and passive learning framework, resulting in major limitations in autonomous solution finding and dynamic environmental adaptation. Agentic AI has recently been introduced as a promising solution to address the above limitations and pave the way for true generally intelligent and beneficial AI systems. The key idea is to create a networking ecosystem to support a diverse range of autonomous and embodied AI agents in fulfilling their goals. In this paper, we focus on the novel challenges and requirements of agentic AI networking. We propose AgentNet, a novel framework for supporting interaction, collaborative learning, and knowledge transfer among AI agents. We introduce a general architectural framework of AgentNet and then propose a generative foundation model (GFM)-based implementation in which multiple GFM-as-agents have been created as an interactive knowledge-base to bootstrap the development of embodied AI agents according to different task requirements and environmental features. We consider two application scenarios, digital-twin-based industrial automation and metaverse-based infotainment system, to describe how to apply AgentNet for supporting efficient task-driven collaboration and interaction among AI agents.