Cloud Microservices: An Empirical Study of MCP-Orchestrated LLM Agents for Incident Triage
TL;DR
Empirical evidence is provided that MCP-orchestrated LLM agents can support root cause analysis in cloud-native environments and offer practical guidance for model selection in AIOps/SRE workflows.
Abstract
Context: The growing complexity of cloud microservices imposes significant challenges for Site Reliability Engineering (SRE), contributing to delayed incident resolution and increased operational effort. Objective: This study evaluated the effectiveness of autonomous agents based on Large Language Models (LLMs), orchestrated via the Model Context Protocol (MCP), for root cause analysis in a cloud-native setting. Method: We conducted a controlled Randomized Complete Block Design (RCBD) experiment in Kubernetes with automated fault injection, covering three distinct failure scenarios and multiple LLM configurations across 360 executions. Results: A high-performing configuration (Gemini 2.5 Flash at low temperature) achieved a 71.1% root-cause identification success rate, substantially above a random-chance baseline (≈ 0.91%). Smaller models exhibited higher token and step volatility (CV = 2.17) and more repeated tool-call cycles, challenging the assumption that lower-parameter models are inherently more cost-effective for SRE workflows. Conclusion: The results provide empirical evidence that MCP-orchestrated LLM agents can support root cause analysis in cloud-native environments and offer practical guidance for model selection in AIOps/SRE workflows.