{"ID":2884718,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09197","arxiv_id":"2508.09197","title":"MX-AI: Agentic Observability and Control Platform for Open and AI-RAN","abstract":"Future 6G radio access networks (RANs) will be artificial intelligence (AI)-native: observed, reasoned about, and re-configured by autonomous agents cooperating across the cloud-edge continuum. We introduce MX-AI, the first end-to-end agentic system that (i) instruments a live 5G Open RAN testbed based on OpenAirInterface (OAI) and FlexRIC, (ii) deploys a graph of Large-Language-Model (LLM)-powered agents inside the Service Management and Orchestration (SMO) layer, and (iii) exposes both observability and control functions for 6G RAN resources through natural-language intents. On 50 realistic operational queries, MX-AI attains a mean answer quality of 4.1/5.0 and 100 % decision-action accuracy, while incurring only 8.8 seconds end-to-end latency when backed by GPT-4.1. Thus, it matches human-expert performance, validating its practicality in real settings. We publicly release the agent graph, prompts, and evaluation harness to accelerate open research on AI-native RANs. A live demo is presented here: https://www.youtube.com/watch?v=CEIya7988Ug\u0026t=285s\u0026ab_channel=BubbleRAN","short_abstract":"Future 6G radio access networks (RANs) will be artificial intelligence (AI)-native: observed, reasoned about, and re-configured by autonomous agents cooperating across the cloud-edge continuum. We introduce MX-AI, the first end-to-end agentic system that (i) instruments a live 5G Open RAN testbed based on OpenAirInterf...","url_abs":"https://arxiv.org/abs/2508.09197","url_pdf":"https://arxiv.org/pdf/2508.09197v1","authors":"[\"Ilias Chatzistefanidis\",\"Andrea Leone\",\"Ali Yaghoubian\",\"Mikel Irazabal\",\"Sehad Nassim\",\"Lina Bariah\",\"Merouane Debbah\",\"Navid Nikaein\"]","published":"2025-08-08T12:15:47Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false}