MX-AI: Agentic observability and control platform for open and AI-RAN

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 & Orchestration (SMO) layer, and (iii) exposes both observability and control functions for 6G RANresources 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 secs end-to-end latency when backed by GPT-4.1. Thus, it competes human experts 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&t = 285s&abchannel = BubbleRAN.