Agentic AI, AI-RAN, AI-Core Networks, and Future 6G

Speakers

Honggang ZHANG (School of Computer Science and Engineering. Macau University of Science and Technology (MUST), Macau, China),
Rongpeng LI (College of Information Science and Electronic Engineering. Zhejiang University, Hangzhou, China)
Yuyi MAO (School of Computer Science and Engineering. Macau University of Science and Technology (MUST), Macau, China)

Abstract

Large language model (LLM)-enabled agentic AI has triggered tremendous academic interests to empower information generation, processing, and inference. Towards personalized generative AI agent applications, agentic cloud-edge-terminal networking collaboration is a promising approach, as it facilitates the effective ubiquitous orchestration of heterogeneous communication and computing resources among the widely distributed AI agents.

Accordingly, rooted in LLM-based agentic AI mechanisms and models, this tutorial will address this cutting-edge topic on how to inject agentic AI (intelligence) genes into the next-generation communication & computing networks and achieve AI-native 6G network.

Target Audience

This tutorial is targeted for the post-graduate students, junior academic researchers and industrial engineers who are interested in the emerging topics of agentic AI, AI-RAN, AI-Core networks, and 6G telecommunication systems. The target participants are expected to have prior mathematical knowledge of basic machine learning algorithms (e.g., deep learning, reinforcement learning, natural language processing, neural networks, etc.). The relevant knowledge of representative telecommunication system, radio access networks (RAN) and mobile communications would be a plus factor but not be required.

Outline and Description of the Tutorial

In concrete, with the title of “Agentic AI, AI-RAN, AI-Core, and Future 6G”, this tutorial mainly includes the following key contents:

Agentic AI development and networked AI agents environment for 6G; (1.0 hour by the first presenter)
MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols for AI-agent communications and networks: achieving a unified interface and standardized interaction process among distributed intelligent agents, and solving the cross-platform collaboration problems; (1.0 hour by the first presenter)
Vision, architecture, and latest technological advancements of AI-RAN; (1.0 hour by the second presenter)
Core concepts and architectural innovations of AI-Core networks; (0.5 hour by the second presenter)
Agentic AI, networked GPT (Generative Pre-trained Transformer) and wireless LLMs based on “semantic bus”: a new communication paradigm that integrates semantic transmission, understanding, and knowledge expression. (0.5 hour by the
third presenter)

Reading List

Deepak Bhaskar Acharya, Karthigeyan Kuppan, and B. Divya, “Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey,” IEEE Access, January 2025.
San Murugesan, “The Rise of Agentic AI: Implications, Concerns, and the Path Forward,” IEEE Intelligent Systems, March-April 2025.
Maxime Elkael, Salvatore D’Oro, Leonardo Bonati, Michele Polese, Yunseong Lee, Koichiro Furueda, and Tommaso Melodia, “AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks,” https://arxiv.org/abs/2508.17778, Aug. 2025.
Kapal Dev, Sunder Ali Khowaja, Keshav Singh, Engin Zeydan, and Merouane Debbah , “Advanced Architectures Integrated with Agentic AI for Next-Generation Wireless Networks,” https://arxiv.org/abs/2502.01089, Feb. 2025.
Yuxuan Chen, Rongpeng Li, Zhifeng Zhao, Chenghui Peng, Jianjun Wu, Ekram Hossain, and Honggang Zhang, “NetGPT: An AI-Native Network Architecture for Provisioning Beyond Personalized Generative Services,” IEEE Network, March 2024.

Vertical

Generative AI Models, AI in Education and Agentic AI

Timeline

4 hours