Introduction
Teams running distributed workloads face a persistent operational challenge: when something breaks, the information needed to resolve it is scattered across logs, deployment pipelines, configuration histories, and third-party monitoring tools. A Site Reliability Engineer (SRE) responding to a 2 AM page must manually correlate telemetry from multiple sources, trace dependencies across services, and form hypotheses — a process that routinely takes hours. As systems grow in complexity, the need for an AI-powered operational teammate — an SRE agent — has become increasingly clear.
The Do It Yourself (DIY) path and its limits
Teams exploring this space often start by using their favorite AI coding tools to help during an investigation, a thin wrapper over an large language model (LLM). On-call engineers wake up and looks at the incident details, tickets, give coding tools access to logs, monitoring tools and ask it to launch investigation….
