Despite the programmable architecture of Open RAN, today's deployments still rely heavily on static control and manual operations. To move beyond this limitation, we introduce AgentRAN, an AI-native, Open RAN-aligned agentic framework that generates and orchestrates a fabric of distributed AI agents based on natural language intents. Unlike traditional approaches that require explicit programming, AgentRAN's LLM-powered agents interpret natural language intents, negotiate strategies through structured conversations, and orchestrate control loops across the network. AgentRAN instantiates a self-organizing hierarchy of agents that decompose complex intents across time scales (from sub-millisecond to minutes), spatial domains (cell to network-wide), and protocol layers (PHY/MAC to RRC). A central innovation is the AI-RAN Factory, which continuously generates improved agents and algorithms from operational data, transforming the network into a system that evolves its own intelligence. We validate AgentRAN through live 5G experiments, demonstrating dynamic adaptation to changing operator intents across power control and scheduling. Key benefits include transparent decision-making (all agent reasoning is auditable), bootstrapped intelligence (no initial training data required), and continuous self-improvement via the AI-RAN Factory.

翻译：尽管开放无线接入网络（Open RAN）具备可编程架构，但当今的部署仍严重依赖静态控制和人工操作。为突破这一局限，我们提出了AgentRAN——一个AI原生、与Open RAN对齐的智能体框架，它能够基于自然语言意图生成并编排分布式AI智能体网络。与传统需要显式编程的方法不同，AgentRAN中由大语言模型驱动的智能体能够解析自然语言意图，通过结构化对话协商策略，并在全网范围内编排控制环路。AgentRAN实例化了一个自组织的智能体层级结构，该结构能够将复杂意图分解到不同时间尺度（从亚毫秒级到分钟级）、空间域（从小区到全网）和协议层（从物理层/媒体接入控制层到无线资源控制层）。其核心创新在于AI-RAN工厂，该模块能够持续从运营数据中生成改进的智能体与算法，从而将网络转变为能够自主进化其智能的系统。我们通过现网5G实验验证了AgentRAN，展示了其在功率控制和调度场景中动态适应运营商意图变化的能力。关键优势包括透明的决策过程（所有智能体推理均可审计）、自举式智能（无需初始训练数据）以及通过AI-RAN工厂实现的持续自我改进。