Agentic Diagnostic Reasoning over Telecom and Datacenter Infrastructure
This work addresses the challenge of maintaining and scaling diagnostic systems for large-scale infrastructure operators, though it appears incremental as it builds on existing LLM and tool-based approaches.
The paper tackles the problem of root cause analysis in telecom and datacenter infrastructures by introducing an agentic diagnostic framework using a Large Language Model (LLM) to autonomously navigate infrastructure models through tools, aiming to replace costly traditional methods like hard-coded graph traversal algorithms.
Large-scale telecom and datacenter infrastructures rely on multi-layered service and resource models, where failures propagate across physical and logical components and affect multiple customers. Traditional approaches to root cause analysis(RCA) rely on hard-coded graph traversal algorithms or rule-based correlation engines, which are costly to maintain and tightly coupled to the infrastructure model. In this work, we introduce an agentic diagnostic framework where a Large Language Model (LLM) performs step-wise investigation using a constrained tool space exposed through the Model Context Protocol (MCP). Instead of embedding causal logic or traversal algorithms into the application, the agent autonomously navigates the infrastructure model by invoking tools for service lookup, dependency retrieval, structured and unstructured data, and event analysis, and impact discovery. We define an investigation protocol that structures the agent's reasoning and ensures grounding, reproducibility, and safe handling of missing or ambiguous information. This work lays the foundation for autonomous incident resolution and change impact mitigation. Future systems will not only diagnose and remediate infrastructure failures, but also predict the impact of planned changes on services and customers, enabling operators to mitigate risks before executing maintenance operations.