MX-AI: Agentic Observability and Control Platform for Open and AI-RAN
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915443425411072 |
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| author | Chatzistefanidis, Ilias Leone, Andrea Yaghoubian, Ali Irazabal, Mikel Nassim, Sehad Bariah, Lina Debbah, Merouane Nikaein, Navid |
| author_facet | Chatzistefanidis, Ilias Leone, Andrea Yaghoubian, Ali Irazabal, Mikel Nassim, Sehad Bariah, Lina Debbah, Merouane Nikaein, Navid |
| contents | 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&t=285s&ab_channel=BubbleRAN |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09197 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MX-AI: Agentic Observability and Control Platform for Open and AI-RAN Chatzistefanidis, Ilias Leone, Andrea Yaghoubian, Ali Irazabal, Mikel Nassim, Sehad Bariah, Lina Debbah, Merouane Nikaein, Navid Networking and Internet Architecture Artificial Intelligence 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&t=285s&ab_channel=BubbleRAN |
| title | MX-AI: Agentic Observability and Control Platform for Open and AI-RAN |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2508.09197 |