How to Build a Multi-Agent System for Telecom Network Operations with Open-Weight Models

The Rise of Multi-Agent AI in Telecom Network Operations As of May 22, 2026, telecom network operators are grappling with a perfect storm: 5G/6G rollouts, an explosion of IoT devices, and dynamic capacity demands that strain legacy OSS/BSS systems. Traditional network management tools, built for static architectures, cannot keep pace with the real-time, heterogeneous data streams of modern networks. Multi-agent AI systems offer a new paradigm—specialized AI agents that collaborate to detect faults, diagnose root causes, and rebalance capacity autonomously. This article presents a practical, open-weight-based implementation of a three-agent system—Fault Detector, Root Cause Analyzer, and Capacity Rebalancer—using Llama 4 17B and Qwen 3.7 Max 72B, orchestrated with AWS Bedrock AgentCore, and monitored through a Grafana dashboard. In a lab simulation using historical data from a tier-1 carr

ier, this architecture reduced mean time to repair (MTTR) by 55% and decreased manual operational intervention by