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

• URL: http://arxiv.org/abs/2508.17778v1
• Date: Mon, 25 Aug 2025 08:18:10 GMT
• Title: AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks
• Authors: Maxime Elkael, Salvatore D'Oro, Leonardo Bonati, Michele Polese, Yunseong Lee, Koichiro Furueda, Tommaso Melodia,
• Abstract summary: We introduce AgenRAN, an AI-native, Open RAN-aligned framework that generates and orchestrates a fabric of distributed AI agents based on Natural Language (NL) intents.<n>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.<n>A central innovation is the AI-RAN Factory, an automated pipeline that observes agent interactions and continuously generates new agents embedding improved control algorithms.
• Score: 14.358601770321235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Open RAN movement has catalyzed a transformation toward programmable, interoperable cellular infrastructures. Yet, today's deployments still rely heavily on static control and manual operations. To move beyond this limitation, we introduce AgenRAN, an AI-native, Open RAN-aligned agentic framework that generates and orchestrates a fabric of distributed AI agents based on Natural Language (NL) 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, an automated synthesis pipeline that observes agent interactions and continuously generates new agents embedding improved control algorithms, effectively transforming the network from a static collection of functions into an adaptive system capable of evolving its own intelligence. We demonstrate AgentRAN through live experiments on 5G testbeds where competing user demands are dynamically balanced through cascading intents. By replacing rigid APIs with NL coordination, AgentRAN fundamentally redefines how future 6G networks autonomously interpret, adapt, and optimize their behavior to meet operator goals.

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