Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting

Telecom networks are rapidly increasing in scale and complexity, making management, operation, and optimization increasingly challenging. Although Artificial Intelligence (AI) has been applied to various telecom tasks, existing approaches are often limited in scope, require extensive labeled data, and struggle to generalize across heterogeneous deployments. Consequently, network troubleshooting still relies heavily on Subject Matter Experts (SMEs) to manually correlate multiple data sources and determine root causes and corrective actions. In this paper, we propose a Multi-Agent System (MAS) that leverages an agentic workflow in which Large Language Models (LLMs) coordinate specialized tools to enable auto-mated network troubleshooting. Upon fault detection by AI/ML-based monitoring systems, the framework dynamically activates multiple agents—including an orchestrator, solution planner, executor, data retriever, and root-cause analyzer—to diagnose issues and recommend remediation strategies in near real time. A key component is the solution planner, which generates executable remediation plans grounded in internal operational documentation. To enable this capability, we fine-tune a Small Language Model (SLM) using proprietary troubleshooting documents to produce domain-specific solution plans. Experimental results demonstrate that the proposed framework significantly improves troubleshooting automation and efficiency across both Radio Access Network (RAN) and Core network domains.