MX-AI: Agentic Observability and Control Platform for Open and AI-RAN
Ilias Chatzistefanidis, Andrea Leone, Ali Yaghoubian, Mikel Irazabal, Sehad Nassim, Lina Bariah, Merouane Debbah, Navid Nikaein
TL;DR
MX-AI is an innovative AI-native 6G RAN platform that uses autonomous agents and natural language to manage and control network resources with high accuracy and low latency, validated on a live testbed.
Contribution
The paper introduces MX-AI, the first comprehensive agentic system for 6G RANs that integrates live instrumentation, LLM-powered agents, and natural language control, advancing AI-native network management.
Findings
Achieves 4.1/5.0 answer quality on operational queries
Attains 100% decision-action accuracy
Operates with only 8.8 seconds latency using GPT-4.1
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.