Building the Brain of 6G: A Tutorial on Large AI Models and Agentic AI for Intelligent Communications

Paper at a Glance

Paper Title: From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications
Authors: Feibo Jiang, Cunhua Pan, Li Dong, Kezhi Wang, Octavia A. Dobre, and Merouane Debbah
Affiliation: Hunan Normal University, Southeast University, Brunel University London, Memorial University, Khalifa University of Science and Technology
Published in: arXiv, 2025
Link to Paper: https://arxiv.org/abs/2505.22311
Project Page: https://github.com/jiangfeibo/ComAgent

The Gist of It: TL;DR

In one sentence: This comprehensive tutorial provides a systematic roadmap for applying Large AI Models (LAMs) and the more advanced Agentic AI to solve the complex challenges of future intelligent communication systems, laying out the technological evolution from model-driven to agent-driven 6G networks.

Why It Matters: The Big Picture

As we move toward the era of 6G, communication networks face an explosion in complexity. We’re talking about ubiquitous connectivity, immersive experiences (AR/VR), massive IoT, and integrated sensing—all demanding unprecedented levels of intelligence, adaptability, and efficiency. Traditional communication systems, which rely on static rules and predefined algorithms, are simply not equipped to handle the dynamic, ever-changing environments of 6G.

This is where AI steps in. While early AI models have shown promise, the recent rise of Large AI Models (LAMs)—like the ones powering ChatGPT—offers a new level of cognitive and generative power. But even these powerful models are largely reactive. The true next step, as this paper argues, is the shift to Agentic AI: systems that can autonomously perceive, reason, plan, and act to manage complex networks. This tutorial bridges the gap between these two concepts, providing a much-needed guide for researchers and engineers on how to build the intelligent “brain” of future 6G systems.

The Core Idea: How It Works

This paper isn’t about one novel algorithm; it’s a structured guide that breaks down the journey from foundational AI models to fully autonomous communication agents.

1. The Foundation: Understanding Large AI Models (LAMs)

Before building agents, we need to understand their core components. The authors provide a clear overview of the key technologies that make up modern LAMs:

Transformers: The backbone architecture that excels at understanding long-range dependencies in data, crucial for both language and other modalities.
Vision Transformers (ViTs), Diffusion Models, and DiTs: These are the specialized tools for visual understanding and high-fidelity image/data generation, essential for tasks like environmental sensing and semantic communication.
Mixture of Experts (MoE): A clever technique to scale up models to trillions of parameters without a proportional increase in computational cost, making massive models more efficient.

The paper categorizes LAMs into types like Large Language Models (LLMs), Large Vision Models (LVMs), and Large Multimodal Models (LMMs), explaining the specific roles each can play in communications.

2. Tailoring LAMs for Communications

A general-purpose LAM like GPT-4 doesn’t inherently understand the nuances of network protocols or spectrum allocation. The paper outlines a two-pronged strategy to instill this domain-specific knowledge:

Internal Learning: This is the traditional approach of training the model itself. It involves creating specialized datasets from sources like 3GPP standards, research papers, and patents. The model is then further trained on this data through pre-training (to learn the domain’s “language”), fine-tuning (to learn specific tasks), and alignment (to ensure its outputs are helpful and accurate).
External Learning: Instead of changing the model’s weights, this approach gives it access to external knowledge on the fly. This is done through Retrieval-Augmented Generation (RAG), where the model retrieves relevant information from a vector database before answering a query, and Knowledge Graphs (KGs), which provide structured information about how different network concepts relate to each other. This makes the system more up-to-date and adaptable.

The overall design pipeline is neatly summarized in Figure 3 of the paper, showing how these learning methods combine to create a communication-savvy LAM.

3. The Leap to Agentic AI

The key difference between a LAM and an AI Agent is autonomy. A LAM responds to a prompt. An agent takes a goal and works proactively to achieve it. The paper proposes a system architecture for an AI agent (shown in Figure 4) built around a core LAM, but with several crucial additions:

Planner: Decomposes a high-level goal (e.g., “Optimize network performance in this sector”) into a sequence of concrete, executable steps.
Knowledge Base: The long-term memory containing both vectorized documents (for RAG) and structured graphs (for KG) about communications.
Tools: A suite of external functions the agent can call upon. These can be general (e.g., a web search) or highly specific (e.g., an algorithm to re-allocate wireless channels or a network simulator like NS-3).
Memory: A short-term and long-term memory to store the results of past actions, enabling the agent to learn from experience and reflect on its performance.

4. Multi-Agent Collaboration for 6G

Many 6G problems are too complex for a single agent. The authors propose a multi-agent framework (Figure 5) where different agents collaborate. This system features three key modules:

Multi-agent Data Retrieval (MDR): Agents that specialize in securely and efficiently finding the right information.
Multi-agent Collaborative Planning (MCP): A team of “planner” agents that propose different strategies to solve a problem.
Multi-agent Evaluation and Reflection (MER): “Evaluator” agents that assess the proposed plans, provide feedback, and help the system learn and improve over time.

This collaborative, reflective process allows the system to tackle complex, open-ended tasks with greater robustness and intelligence.

Key Applications and Roles in 6G

This is a tutorial, so instead of novel experimental results, it provides a comprehensive survey of applications where these technologies are making an impact.

LAMs are being used as:
- Data Generators: Creating synthetic but realistic channel state information (CSI) to train other models without compromising user privacy.
- Knowledge Organizers: Powering semantic communication systems that transmit the meaning of a message rather than just the raw bits, dramatically improving efficiency.
- Resource Managers: Intelligently scheduling network resources like spectrum and power in real-time.
Agentic AI is being applied to:
- Wireless Communication: Agents that autonomously plan flight paths and allocate resources for a fleet of UAVs.
- Network Management & Optimization: Agents that act as autonomous network administrators, diagnosing faults, managing resources, and optimizing performance without human intervention.
- Network Security: Agents that proactively detect threats, generate defense strategies, and dynamically adapt security policies.

A Critical Look: Strengths & Limitations

Strengths / Contributions

Comprehensive Scope: This tutorial is a fantastic “one-stop-shop,” systematically connecting the dots from foundational AI architectures to applied, autonomous agent systems within the specific context of communications.
Structured Frameworks: The paper provides clear, actionable frameworks for both designing communication-specific LAMs (Figure 3) and constructing multi-agent systems (Figures 4 & 5). This is incredibly valuable for researchers and engineers looking for a starting point.
Forward-Looking Vision: It synthesizes the current state-of-the-art to provide a clear vision for the future of intelligent networks, identifying key challenges and proposing concrete research directions.
Exceptional Clarity and Organization: The paper is extremely well-structured, making a complex and rapidly evolving field accessible to a broad technical audience.

Limitations / Open Questions

High-Level by Necessity: As a broad tutorial, it cannot delve deeply into the specific, gritty challenges of implementing each component. For instance, the practical difficulties of real-time RAG in a low-latency network are mentioned but not explored in depth.
The Deployment Gap: While the frameworks are well-defined, the paper focuses more on the “what” and “why” than the “how” of deployment. The immense computational and energy costs of running these models in resource-constrained edge devices remain a major practical hurdle.
Emphasis on Potential: Many of the applications discussed are still in the research or proof-of-concept phase. The paper excellently maps out the potential, but the road to robust, scalable, and secure deployment in real-world networks is still long.

Contribution Level: Foundational Tutorial. This paper’s strength is not in a single novel algorithm but in its masterful synthesis of a vast and critical research area. It provides a foundational text and a clear roadmap for anyone working at the intersection of AI and 6G communications, and it will almost certainly become a go-to reference in the field.

Conclusion: Potential Impact

This tutorial serves as both a comprehensive introduction and a call to action. It clearly articulates the technological shift from networks that are simply managed by AI models to networks that are run by autonomous AI agents. For PhD students and researchers, it lays out a rich landscape of open problems in model design, agent collaboration, and system evaluation. For industry engineers, it provides a vision of what the next generation of intelligent, self-optimizing, and self-healing networks could look like.

By bridging the concepts of Large AI Models and Agentic AI, the authors have provided an invaluable guide for building the autonomous brain that will power the ambitious vision of 6G.