Agentic AI represents a new paradigm for automating complex systems by using Large AI Models (LAMs) to provide human-level cognitive abilities with multimodal perception, planning, memory, and reasoning capabilities. This will lead to a new generation of AI systems that autonomously decompose goals, retain context over time, learn continuously, operate across tools and environments, and adapt dynamically. The complexity of 5G and upcoming 6G networks renders manual optimization ineffective, pointing to Agentic AI as a method for automating decisions in dynamic RAN environments. However, despite its rapid advances, there is no established framework outlining the foundational components and operational principles of Agentic AI systems nor a universally accepted definition. This paper contributes to ongoing research on Agentic AI in 5G and 6G networks by outlining its core concepts and then proposing a practical use case that applies Agentic principles to RAN optimization. We first introduce Agentic AI, tracing its evolution from classical agents and discussing the progress from workflows and simple AI agents to Agentic AI. Core design patterns-reflection, planning, tool use, and multi-agent collaboration-are then described to illustrate how intelligent behaviors are orchestrated. These theorical concepts are grounded in the context of mobile networks, with a focus on RAN management and optimization. A practical 5G RAN case study shows how time-series analytics and LAM-driven agents collaborate for KPI-based autonomous decision-making.

翻译：智能体人工智能代表了一种通过使用大型人工智能模型来提供具备多模态感知、规划、记忆与推理能力的人类级认知水平，从而实现复杂系统自动化的新范式。这将催生新一代人工智能系统，能够自主分解目标、长期保持上下文、持续学习、跨工具与环境操作并动态适应。5G及未来6G网络的复杂性使得人工优化效率低下，这指向了智能体人工智能作为在动态无线接入网环境中实现决策自动化的方法。然而，尽管其发展迅速，目前尚无既定的框架来概述智能体人工智能系统的基础组件与运行原则，也缺乏普遍接受的定义。本文通过概述其核心概念，进而提出一个将智能体原则应用于无线接入网优化的实际用例，为5G和6G网络中智能体人工智能的持续研究做出贡献。我们首先介绍智能体人工智能，追溯其从经典智能体的演变，并讨论从工作流和简单人工智能智能体到智能体人工智能的进展。随后描述了核心设计模式——反思、规划、工具使用与多智能体协作——以阐释智能行为如何被编排。这些理论概念立足于移动网络背景，重点关注无线接入网的管理与优化。一个实际的5G无线接入网案例研究表明了时间序列分析与大型人工智能模型驱动的智能体如何协作，实现基于关键性能指标的自主决策。