Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence

2026-02-27

AI Summary

This work addresses the operational complexity challenges in secure and auditable multi-tenant, multi-objective Open RAN deployments. The authors propose an agent-based primitive framework for O-RAN, integrating planning–execution–observation–reflection cycles, skill-as-tool abstractions, memory and evidence modules, and self-managed gating mechanisms to realize an intent-driven, explainable, and self-evolving RAN intelligent control system. The framework focuses on three core tasks: network slice lifecycle management, closed-loop wireless resource management, and security compliance enforcement. Evaluated in a multi-cell simulation environment across three representative slice types, the system reduces average resource overhead by 8.83% compared to conventional baselines and ablation variants, demonstrating its efficacy and compatibility with existing standards.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder to operate multi-tenant, multi-objective RANs in a safe and auditable manner. In parallel, agentic AI systems with explicit planning, tool use, memory, and self-management offer a natural way to structure long-lived control loops. This article surveys how such agentic controllers can be brought into O-RAN: we review the O-RAN architecture, contrast agentic controllers with conventional ML/RL xApps, and organise the task landscape around three clusters: network slice life-cycle, radio resource management (RRM) closed loops, and cross-cutting security, privacy, and compliance. We then introduce a small set of agentic primitives (Plan-Act-Observe-Reflect, skills as tool use, memory and evidence, and self-management gates) and show, in a multi-cell O-RAN simulation, how they improve slice life-cycle and RRM performance compared to conventional baselines and ablations that remove individual primitives. Security, privacy, and compliance are discussed as architectural constraints and open challenges for standards-aligned deployments. This framework achieves an average 8.83\% reduction in resource usage across three classic network slices.

Problem

• Open RAN
• Agentic AI
• Multi-tenant Management
• Explainable AI
• Self-Evolving Systems

Innovation

• Agentic AI
• Open RAN
• Intent-Driven Control
• Self-Evolving Systems
• Explainable AI