As telecom networks become increasingly complex and large-scale, effective management, operation, and optimization are becoming more challenging. Although Artificial Intelligence (AI) has been applied to various telecom tasks, existing models are often narrow in scope, require large amounts of labeled data, and struggle to generalize across heterogeneous deployments. Consequently, network troubleshooting continues to rely heavily on Subject Matter Experts (SMEs) to manually correlate various data sources to identify root causes and corrective actions.
To address these limitations, we propose a Multi-Agent System (MAS) that employs an agentic workflow, utilizing Large Language Models (LLMs) to coordinate multiple specialized tools for fully automated network troubleshooting. Once faults are detected by AI/ML-based monitors, the framework dynamically activates agents such as an orchestrator, solution planner, executor, data retriever, and root-cause analyzer to diagnose issues and recommend remediation strategies within a short time frame.
A key component of this system is the solution planner, which generates appropriate remediation plans based on internal documentation. To enable this, we fine-tuned a Small Language Model (SLM) on proprietary troubleshooting documents to produce domain-grounded solution plans. Experimental results demonstrate that the proposed framework significantly accelerates troubleshooting automation across both Radio Access Network (RAN) and Core network domains.
Blogger's Review: The proposed Multi-Agent System combined with fine-tuned Small Language Models offers an efficient automation solution for telecom network troubleshooting. This innovative approach not only enhances the speed of fault response but also reduces reliance on manual intervention, showcasing the immense potential of AI in practical applications. With further model optimization and data accumulation, this system is expected to be applied more widely in the future.