Operations | Monitoring | ITSM | DevOps | Cloud

AI can write code fast, but it makes mistakes humans often don't. In this session from Ole Lensmar, CTO of Testkube, breaks down the real quality risks of AI-generated code and how engineering teams can build guardrails before those bugs hit production. What you'll learn: Common mistakes LLMs make (and which ones are unique to AI) Whether you're a developer leaning on AI to ship faster or a QA lead trying to keep up with the pace of AI-generated code, this talk gives you a practical framework for staying ahead of quality issues.

Actually useful AI is welcome in observability. AI for the sake of AI is not. In this overview of Grafana Labs’ 4th annual Observability Survey, Marc Chipouras shares what 1,300+ respondents from 76 countries told us about the current state of observability — and what comes next. This year’s survey explores four major themes: The results show strong interest in AI for forecasting, root cause analysis, onboarding, and generating dashboards, alerts, and queries. But when it comes to autonomous action, practitioners are more cautious — and 95% say AI needs to show its work to earn trust.

What happens when you replace legacy network tools with Kentik AI Advisor?

Tom welcomes Mike Lovewell to explore how digital friction continues to shape the modern workplace. From early days of low awareness to today’s complex, AI-influenced environments, Mike shares how friction has evolved in scale rather than cause. They discuss the growing importance of flow state, the measurable business impact of small disruptions, and why adoption—not just technology—is the key to success. AI emerges as both a solution and a new source of friction, depending on trust and usability.