Open Source Summit + Embedded Linux Conference North America 2026

This talk explores using Large Language Models (LLMs) to automate Linux kernel crashdump analysis at scale. At Cloudflare, we operate Linux across hundreds of thousands of servers. At this scale, kernel crashes are inevitable.
Typical crash analysis requires deep kernel expertise and significant time investment, slowing down time to resolution.
I'll share our journey building an LLM-powered agent that performs initial crash analysis autonomously. First, I'll cover our infrastructure for collecting and managing crashdumps across our fleet. Then I'll explain crashdump analysis fundamentals: using the crash utility, interpreting stack traces, identifying common failure patterns, and correlating crashes with kernel subsystems.
This talk focuses on teaching an LLM agent to replicate expert analysis workflows. I'll show how we structured prompts and created skills. I'll show examples of the agent analyzing real crashes.