Agentic AI systems are emerging as powerful tools for automating complex, multi-step tasks across various industries. One such industry is telecommunications, where the growing complexity of next-generation radio access networks (RANs) opens up numerous opportunities for applying these systems. Securing the RAN is a key area, particularly through automating the security compliance process, as traditional methods often struggle to keep pace with evolving specifications and real-time changes. In this article, we propose a framework that leverages LLM-based AI agents integrated with a retrieval-augmented generation (RAG) pipeline to enable intelligent and autonomous enforcement of security compliance. An initial case study demonstrates how an agent can assess configuration files for compliance with O-RAN Alliance and 3GPP standards, generate explainable justifications, and propose automated remediation if needed. We also highlight key challenges such as model hallucinations and vendor inconsistencies, along with considerations like agent security, transparency, and system trust. Finally, we outline future directions, emphasizing the need for telecom-specific LLMs and standardized evaluation frameworks.

翻译：智能体AI系统正成为自动化跨行业复杂多步骤任务的强大工具。电信行业便是其中之一，其中下一代无线接入网日益增长的复杂性为应用这些系统开辟了众多机遇。保障RAN安全是一个关键领域，特别是通过自动化安全合规流程，因为传统方法往往难以跟上不断演进的规范和实时变化。本文提出一个框架，利用基于大语言模型的AI智能体与检索增强生成流程集成，以实现智能且自主的安全合规执行。一项初步案例研究表明，智能体如何评估配置文件是否符合O-RAN联盟和3GPP标准，生成可解释的合规理由，并在需要时提出自动化修复方案。我们还强调了关键挑战，如模型幻觉和供应商不一致性，以及智能体安全性、透明度和系统信任等考量因素。最后，我们概述了未来方向，强调了对电信专用大语言模型和标准化评估框架的需求。