Operations | Monitoring | ITSM | DevOps | Cloud

The first stage of AI SRE maturity is a laptop, a throwaway cluster, and zero production access. Here’s how to set it up, and what to watch for. AI SRE (Site Reliability Engineering) agents are AI-powered systems that automate the most time-consuming parts of incident response: triaging alerts, correlating logs and metrics, generating root-cause hypotheses, and proposing remediation steps.

As of today, I’ve drafted this post upwards of 10 times – it’s old enough that the version I first started working on was called “Reflections on 1 Year of SRE Management” (I’m currently at 2.5 years). But everything I learned during that first year became critical for the next.

ClickHouse swallows high-cardinality telemetry at ingest, then breaks at query time weeks later. Here is what fails, and how we keep it fast in production. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

In reliability engineering, being ‘mostly right’ is a liability. An AI SRE that sometimes misses the root cause or gives a confident, wrong answer at 2:17 AM has no place in an enterprise cloud environment. In this context, silence is better than noise. That’s the bar Klaudia is built to clear: genuine reliability that you can trust in production. The kind of reliability that earns a place alongside your best engineers. Getting there requires more than just a capable model.