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This article focuses on the practical application of Large Language Models in incident response workflows, specifically addressing their utility in log analysis versus limitations in root-cause determination. It provides engineering leaders with actionable insights on integrating AI into on-call rotations while maintaining essential human oversight.
Presentation: Can Claude Fix Itself? Using LLMs for Incident Response
Anthropic reliability engineer Alex Palcuie shares practical lessons on using LLMs for real-world incident response. He explains where AI acts as a superhuman for observing logs and traces, why it still struggles with causation versus correlation during root-cause analysis, and how engineering leaders can integrate AI into on-call workflows without eroding human expertise. By Alex Palcuie
Alex Palcuie: I'm Alex. I'm on the AI reliability team at Anthropic, which means my job is keeping Claude up. I've been using LLMs as part of actual incident response, and I'd like to share a candid and humorous discussion about what works and what doesn't. I'm one of the two people that joined the reliability team in London. I did on-call for Claude's serving stack, Solo, for my first three months, which is a very effective and fast way, but really stressful, to learn about the system. Then I onboarded everyone else so I could stop being on-call forever. Before that, I was at Google, SRE on Google Cloud's compute product, GCE. I was on the SRE for SRE team, which is the escalation layer for when an outage is bad enough that even normal SREs want to back up. I've been carrying pages for a while. I do have opinions of what makes an incident response process good and what makes one bad. While naturally skeptical in the beginning, since about January this year, I've started doing something that feels slightly transgressive to admit, which is, I start reaching out for Claude before I reach out to my…
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