Operations | Monitoring | ITSM | DevOps | Cloud

Engineers Want AI in Observability - With One Catch: 4th Annual Observability Survey by Grafana Labs

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.

Flow State in an AI Workplace - Digital Friction 1:1 with Mike Lovewell

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.

How agentic AI for ITOps overcomes observability tool gaps

As enterprise ITOps teams monitor increasingly complex, cloud-based, containerized systems, traditional observability practices are struggling to keep up. As IT infrastructure complexity increases, the typical response is to layer on more monitoring, logging, and instrumentation.

Buy vs Build in the Age of AI (Part 3)

In Part 1, we looked at how AI has reduced the cost of building monitoring tools. Then in Part 2, we explored the operational and economic burden of owning them. Now we need to talk about something deeper. Because the real shift isn’t just economic; it’s structural. AI isn’t just helping engineers write code faster. It’s accelerating the entire software ecosystem; including how monitoring tools are built, maintained, and trusted.

How Local-First AI Agents Are Reshaping IT Operations Automation

IT operations teams have spent the last decade embracing automation - from auto-scaling rules and CI/CD pipelines to AIOps platforms that correlate alerts across sprawling infrastructure. Yet a fundamental tension remains unresolved: the most powerful AI automation tools require you to route sensitive operational data through external cloud services you do not control.

My Room Still Looked Wrong - Until I Tried an AI Home Design Generator

I didn't expect much when I first tried an AI Home Design Generator and an AI Image to Image Generator. At that point, I wasn't trying to redesign anything seriously. I just knew my room looked... off. Not terrible, just never quite right. Every time I took a photo, something felt wrong - the layout, the lighting, maybe both.

From Data Chaos to Results: The New Data Strategy for the Agentic Era

The world is generating data at a pace that defies the human ability to draw insights and comprehend. By 2028, we’ll reach almost 400 zettabytes of global data—with over 55% of it coming from machines talking to machines. For enterprises, this isn’t just a storage problem; it’s an existential challenge.

Knowledge Graphs: The Backbone of AI-First Software Delivery | Harness Blog

--- ‍Key Takeaways --- AI can generate code in seconds. It still can’t ship software safely. That gap isn’t about model quality or prompt engineering. It’s about context, and most software organizations don’t have a system that accurately reflects how pipelines, services, environments, policies, and teams actually relate to each other. Without that context, AI doesn’t automate delivery. It amplifies risk.