Larissa Dornelles

A case for evaluating MCP AI agents in ops hold clear promise: more adaptive, context-aware and autonomous operations. Powered by the Model Context Protocol (MCP), these agents can retrieve real-time data — logs, metrics, traces and runbooks — and take action. But as agent deployments scale, so do the risks: hallucinated outputs, compliance gaps, security vulnerabilities, performance issues and untraceable decision-making.

Crafting these resolutions often requires drawing from diverse domains of knowledge, including system design, observability best practices and service-level objectives. While this traditional approach is structured and thorough, it remains time-consuming — especially when dealing with low-level system signals and fragmented toolchains. To address these challenges, we began adopting AI agents to support our incident workflows.