Operations | Monitoring | ITSM | DevOps | Cloud

Let’s face it: the modern enterprise network is a leviathan. No longer just a collection of routers and switches, today’s networks span multiple clouds, hundreds of SaaS applications, and countless IoT devices—supporting a workforce that could be anywhere.

AutoCon3 in Prague offered important takeaways on network automation’s evolution, from hands-on learning and design principles to the impact of AI and the power of community. Read Justin Ryburn’s recap to learn about key insights from the event, showing why network automation is now a core competency you’ll want to understand.

Twelve months ago, we shipped Edwin AI with a specific hypothesis that AI agents could handle the operational drudgery slowing down ITOps teams. It was a deliberate bet against the cautious consensus that AI should act only as a copilot, limited to offering suggestions. Most AIOps tools still follow that script. They’re stuck surfacing insights and stop short of action. Edwin was built differently. It was designed to make decisions, correlate events, and execute fixes.

For enterprises embracing Kubernetes, managing these intricate environments can pose significant challenges. Thankfully, monitoring of Kubernetes clusters is readily achievable using the Universal Monitoring Agent (UMA) in conjunction with DX Operational Observability (DX O2).

AI adoption is accelerating across nearly every industry. According to McKinsey’s 2025 State of AI report, 78% of organizations now use AI in at least one business function, up from just 55% the year prior. From real-time analytics to generative tools and process automation, AI is becoming a fundamental part of how modern businesses operate and compete.

AI discourse these days is all over the place. Depending on who you talk to, AI’s are absolute flash-in-the-pan junk, or they’re the best thing since sliced bread. I want to cut through the noise, though, and see for myself what someone can do out here on the bleeding edge. Thus, I’m setting myself a challenge: write a usable—and useful—application with Claude Code, from soup to nuts. Here are the rules: With our ground rules established, let’s figure out our app!

In Grafana Cloud, we offer a range of AI capabilities to support your observability needs, including a feature for forecasting on any of your metrics and coupling it with Grafana Alerting. This is critical functionality if you want to make the switch from reactive to proactive alerting, as troubleshooting a problem before it arises is an important part of modern observability.

Kubernetes makes it easy to scale applications. But when it comes to CPU resource management, a poorly tuned cluster can quickly become unstable or inefficient. For network engineers, setting CPU requests and limits correctly—and understanding the deeper implications—is essential for keeping workloads efficient, costs predictable, and noisy neighbors in check.