Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management

2026-02-15

AI Summary

This work addresses the inefficiencies in autonomous network management caused by unintended interactions among multiple control loops and independent applications due to the programmability of O-RAN. To this end, the paper proposes a multi-scale agent-based AI framework that deploys coordinated intelligent agents across O-RAN’s non-real-time, near-real-time, and real-time layers. It presents the first integration of hierarchical large language models (LLMs), small language models (SLMs), and wireless physical foundation models (WPFMs) to establish an intent-driven, cross-timescale autonomous architecture. Cross-layer coordination is achieved through standard O-RAN interfaces and telemetry data, and a prototype system is implemented using open-source models. The framework demonstrates end-to-end autonomous network control capabilities in two scenarios: robust operation under non-stationary environments and intent-driven network slicing resource orchestration.

Technology Category

• Multiagent Systems: Modeling other Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• O-RAN
• network control
• operational complexity
• control loops
• RAN Intelligent Controller

Innovation

• Agentic AI
• Multi-scale Framework
• O-RAN
• Large Language Model (LLM)
• Wireless Physical-layer Foundation Model (WPFM)