Agentic Diagnostic Reasoning over Telecom and Datacenter Infrastructure
Nicolas Tacheny

TL;DR
This paper presents an agentic diagnostic framework using a Large Language Model to autonomously investigate and analyze failures in telecom and datacenter infrastructure, improving flexibility and safety over traditional rule-based methods.
Contribution
It introduces a novel LLM-based agentic diagnostic approach with a structured investigation protocol, enabling autonomous, reproducible, and safe root cause analysis in complex infrastructure systems.
Findings
LLM-based agent can autonomously navigate infrastructure models
Structured investigation protocol improves grounding and reproducibility
Framework lays foundation for autonomous incident resolution
Abstract
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…
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Taxonomy
TopicsSoftware System Performance and Reliability · Mobile Agent-Based Network Management · Software-Defined Networks and 5G
