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Multi-layer cross-domain multi-agent system: Building a "dark-factory" processing mode for network faults management

URN C25.5.859

Topics Autonomous networks

This project uses AI and Multi-Agent to automate transport network fault management, ensuring full alerts, faster fixes, and efficient networks.

Project companies

Communication Service Providers (CSPs) and vertical industries are increasingly demanding automated, intelligent fault management in transport networks. Today, fragmented systems and poor OSS-OMC integration result in manual-heavy, inefficient fault handling processes. As one top CSP leader shared: "We urgently need end-to-end automation in fault management to achieve true service continuity. Manual intervention not only slows down resolution but also increases operational risks." The current challenges — including inaccurate fault identification due to rule-based limitations, slow diagnosis due to reliance on manual tools, and delayed repair due to inefficient communication — severely impact service quality and operational costs. Solving these issues through a multi-layer, cross-domain agent architecture powered by large language models and digital twin technologies will enable accurate fault recognition, rapid root-cause analysis, and secure, automated resolution. This transformation will drastically reduce MTTR (Mean Time to Repair), improve network resilience, and lower dependency on individual expertise, enabling more scalable and intelligent operations. Project Focus: Solving transport network fault handling issues. Traditional methods rely on manual/fixed-rule alarm identification, causing missed/redundant work orders. Cross-domain systems lack coordination; OSS and OMCs don’t interact effectively, hindering automated closed loops, with inefficient manual intervention. Diagnosis takes over 30 minutes (hours for complex faults) via manual/standalone tools, limited by experience transfer. Repairs involve frequent communication or manual operations, with low safety/efficiency. The project uses LLM, Agent tech and small-model algorithms to build a multi-layer cross-domain Multi-Agent system covering network devices. It enables end-to-end automated fault handling via Agent interaction and digital twin simulation. Zhejiang Mobile’s pilot showed 100% alarm coverage, reduced redundant WOs, faster diagnosis, lower costs, and enhanced efficiency. O&M Copilot cuts resolution to 40 minutes, minimizing outages, boosting stability/experience, and driving intelligent, efficient transport network operations.