Operations | Monitoring | ITSM | DevOps | Cloud

Atlassian’s audit records are data-rich, but messy. In this data-driven deep dive, Eddy Gurney from NetScout shares what it took to get them into Graylog. He walks through four pipeline approaches and why each fell short, then shows how moving parsing to the edge with Filebeat unlocked Graylog. With clean, flattened events flowing in, alerts and dashboards turn “noise” into operational visibility. You’ll also see how Sidecars makes config rollout easy, plus what changes to make if you’re on Atlassian Cloud instead of Data Center.

Ubuntu on @qualcomm platforms simplifies access to Qualcomm’s advanced AI accelerators and capabilities, empowering developers and enterprises to innovate with confidence. Learn about the work we’re doing to bring Ubuntu to Arduino and other Qualcomm-powered boards. The Qualcomm and Canonical partnership provides developers with a reliable, security-focused, high performance operating system. In this video, Qualcomm breaks down how upstreaming, open standards, and Canonical solutions help developers move faster, from first boot to production.

Agentic AI is moving fast, but expectations are moving faster. In this episode of Agents of IT, Resolve CCO Sean Heuer and Ari Stowe, Resolve COO, cut through the noise around agentic AI, AIOps, and automation in modern IT environments. They react to recent articles from Forbes, TechCrunch, and others to unpack what’s real, what’s hype, and what actually works today.

High-volume IT issues slow teams down, drain resources, and delay progress toward autonomous operations. In this Resolve webinar, we break down how to start your autonomous journey the right way by focusing on high-impact automations that deliver the fastest ROI.

How does one of the world's leading AI companies keep its infrastructure reliable while shipping new models constantly? In this webinar, Devon Mizelle, Senior SRE at Mistral AI, shares the real story. Devon walks through how Mistral built an automated system that generates synthetic checks for every model the moment it goes live—no manual configuration, no forgotten monitors, no inconsistent alerting. Using monitoring as code, his team eliminated the toil of maintaining hundreds of checks across a rapidly evolving model ecosystem.

AI now writes 30% of Big Tech’s code, but the resulting surge in defects is crashing platforms like AWS and GitHub. Manual testing can no longer keep up with this velocity; it's time to deploy AI Quality Agents to save our systems. Is AI speed worth the decline in code quality, or are we headed for a breaking point? Let me know if you’ve noticed more bugs in your workflow lately. Video collab with @ScottMooreConsultingLLC.