AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks

TL;DR

AgentRAN introduces an AI-native, agent-based framework for autonomous control of open 6G networks, enabling natural language-driven, self-organizing, and continuously improving network management.

Contribution

It presents a novel agentic architecture with an AI-RAN Factory that generates and evolves network control agents from operational data, moving beyond static, manual configurations.

Findings

Demonstrated dynamic adaptation in live 5G experiments

Achieved transparent and auditable decision-making processes

Enabled continuous self-improvement of network control agents

Abstract

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.