{"ID":2878452,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17778","arxiv_id":"2508.17778","title":"AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks","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.","short_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 la...","url_abs":"https://arxiv.org/abs/2508.17778","url_pdf":"https://arxiv.org/pdf/2508.17778v2","authors":"[\"Maxime Elkael\",\"Salvatore D'Oro\",\"Leonardo Bonati\",\"Michele Polese\",\"Yunseong Lee\",\"Koichiro Furueda\",\"Tommaso Melodia\"]","published":"2025-08-25T08:18:10Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.NI\"]","methods":"[\"Large Language Model\",\"Generative Adversarial Network\"]","has_code":false}