Open Radio Access Networks (O-RAN) promise flexible 6G network access through disaggregated, software-driven components and open interfaces, but this programmability also increases operational complexity. Multiple control loops coexist across the service management layer and RAN Intelligent Controller (RIC), while independently developed control applications can interact in unintended ways. In parallel, recent advances in generative Artificial Intelligence (AI) are enabling a shift from isolated AI models toward agentic AI systems that can interpret goals, coordinate multiple models and control functions, and adapt their behavior over time. This article proposes a multi-scale agentic AI framework for O-RAN that organizes RAN intelligence as a coordinated hierarchy across the Non-Real-Time (Non-RT), Near-Real-Time (Near-RT), and Real-Time (RT) control loops: (i) A Large Language Model (LLM) agent in the Non-RT RIC translates operator intent into policies and governs model lifecycles. (ii) Small Language Model (SLM) agents in the Near-RT RIC execute low-latency optimization and can activate, tune, or disable existing control applications; and (iii) Wireless Physical-layer Foundation Model (WPFM) agents near the distributed unit provide fast inference close to the air interface. We describe how these agents cooperate through standardized O-RAN interfaces and telemetry. Using a proof-of-concept implementation built on open-source models, software, and datasets, we demonstrate the proposed agentic approach in two representative scenarios: robust operation under non-stationary conditions and intent-driven slice resource control.

翻译：开放无线接入网络（O-RAN）通过解耦的软件驱动组件和开放接口，为实现灵活的6G网络接入提供了可能，但这种可编程性也增加了运营复杂性。多个控制环路共存于服务管理层和RAN智能控制器（RIC）中，而独立开发的控制应用可能以非预期的方式相互影响。与此同时，生成式人工智能（AI）的最新进展正推动从孤立的AI模型向智能体AI系统转变，这类系统能够解读目标、协调多个模型与控制功能，并随时间调整其行为。本文提出了一种面向O-RAN的多尺度智能体AI框架，将RAN智能组织为跨越非实时（Non-RT）、近实时（Near-RT）和实时（RT）控制环路的协调层次结构：（i）非实时RIC中的大语言模型（LLM）智能体将运营商意图转化为策略并管理模型生命周期；（ii）近实时RIC中的小语言模型（SLM）智能体执行低延迟优化，并可激活、调优或禁用现有控制应用；（iii）分布式单元附近的无线物理层基础模型（WPFM）智能体在靠近空口处提供快速推理。我们阐述了这些智能体如何通过标准化的O-RAN接口与遥测技术进行协作。基于开源模型、软件和数据集构建的概念验证实现，我们在两个代表性场景中展示了所提出的智能体方法：非平稳条件下的鲁棒运行以及意图驱动的切片资源控制。