AI Alert Assistant: How n8n + LLM Replace Routine Diagnostics

Anyone who has dealt with keeping services running knows how exhausting and unpredictably time-consuming incident diagnostics and resolution can be. Over the years, I've watched the evolution of incident response processes — from "whoever spots the problem first owns it" to strictly defined 24/7 on-call rotations, SLA-driven response times, runbook adherence, and separation of responsibility across platforms. One thing has remained constant: Gathering data from multiple sources 1.1. Metrics 1.2. Logs 1.3. Traces 1.4. Release and maintenance timelines Analysis based on personal knowledge and experience Formulating possible solutions If you have a documented procedure for every situation, that simplifies things somewhat — but it doesn't teach the investigative mindset needed for real troubleshooting. Writing and maintaining a runbook for every alert is tedious work, which is exactly why an experienced engineer will always outperform a library of hundreds of runbooks. But what if an engin