Semantic Observability for Autonomous Telecom Operations Using Graph Reasoning and Multi-Agent Decision Validation

Abstract

The rapid evolution of 5G and emerging 6G networks requires intelligent operational frameworks capable of autonomous monitoring, reasoning, and decision-making. Traditional network observability methods rely on numerical telemetry, which often lacks contextual understanding of complex network events. This paper proposes a Semantic Observability Framework that integrates semantic telemetry, graph reasoning, agentic artificial intelligence (AI), and multi-agent decision validation to improve autonomous telecom operations. The framework transforms heterogeneous telemetry data into a knowledge graph that captures relationships among network resources, services, alarms, and dependencies. A graph reasoning engine identifies root causes, predicts service impacts, and generates actionable insights, while specialized AI agents collaboratively validate operational decisions before execution to ensure policy compliance and operational reliability. The proposed approach enhances explainability, reduces false alarms, accelerates fault resolution, and improves network resilience. The framework provides a scalable and trustworthy solution for intelligent telecom management, supporting efficient, transparent, and autonomous operations in next-generation communication networks.