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：自主式人工智能系统正成为跨行业自动化复杂多步骤任务的强大工具。在电信领域，下一代无线接入网络（RAN）日益增长的复杂性为应用此类系统创造了众多机遇。RAN安全是核心领域之一，尤其需要通过自动化安全合规流程来实现，因为传统方法往往难以跟上不断演进的规范与实时变化。本文提出一种框架，该框架利用基于大语言模型（LLM）的AI智能体，集成检索增强生成（RAG）流水线，实现智能且自主的安全合规执行。初步案例研究表明，该智能体能够评估配置文件是否符合O-RAN联盟与3GPP标准，生成可解释的合规依据，并在必要时提出自动修复建议。我们还重点讨论了模型幻觉、供应商不一致性等关键挑战，以及智能体安全、可解释性与系统可信性等考量因素。最后，我们展望了未来方向，强调需要开发电信专用大语言模型与标准化评估框架。