Operations | Monitoring | ITSM | DevOps | Cloud

SREs and platform teams are overwhelmed by the effort of manually troubleshooting ever-more complex cloud-native environments. This pain is driving a breakneck adoption of AI SRE solutions that promise to automate core reliability practices, from root cause analysis to capacity planning. For teams with strong engineering talent, creating a DIY AI SRE seems like a straightforward challenge.

Today Canonical announced the release of Ubuntu 26.04 LTS, codenamed “Resolute Raccoon,” available to download and install from ubuntu.com/download. Resolute Raccoon builds on the resilience-focused improvements introduced in interim releases, with TPM-backed full-disk encryption, improved support for application permission prompting, Livepatch updates for Arm– based servers, and Rust-based utilities for enhanced memory safety.

Key takeaway: AI agents and RAG pipelines only reach production-grade accuracy when they are developed against byte-level clones of real production data. Without environment parity, the "repro gap" leads to inevitable AI failure.

Your SLI query shows 100% availability as No Data. Here's why PromQL returns empty results instead of zero — and the label-preserving fix. 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.

We've been building the broader incident.io platform for several years now, and one thing we've learned is that UX matters more here than almost anywhere else. When an incident fires, there's no room for poorly designed interfaces or fumbling through features you haven't touched in a while — every second of the incident response lifecycle counts. The product has to be ergonomic: easy to pick up, easy to navigate, with the right things at your fingertips at exactly the right moment.

Everyone is using AI to help with post-mortems now. The pitch is obvious: post-mortems are time-consuming, the blank page is brutal, and AI is very good at producing structured, confident-sounding documents quickly. We're not here to push back on that. We've built AI into our own post-mortem experience, pulling your Slack thread, timeline, PRs, and custom fields together and giving your team a meaningful starting point in seconds. We think that's genuinely valuable, and the teams using it agree.

Good agent infrastructure is less about exposing more endpoints and more about exposing a small set of composable, self-describing abstractions that minimize context overhead while encapsulating repetitive integration work.

Most engineers can tell you exactly how many PRs they merged last quarter. Far fewer can tell you what any of it did for the business. The best engineering leaders can. They draw a straight line from their team's work to ARR: which reliability investment protected revenue, which migration unblocked a strategic customer, which operational improvement reduced churn. They lead with outcomes, not story points.