Operations | Monitoring | ITSM | DevOps | Cloud

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.

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!

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.

Insecure system configuration is a textbook example of how neglected settings can create an entry point for attackers targeting your CI/CD pipelines. It’s rarely the cutting-edge zero-day that causes a breach. More often, it’s the unpatched service, the overly permissive role, or the default password that was never changed. While this risk overlaps with CI/CD credential hygiene (covered in Part 6 of our OWASP CI/CD series), the focus here is much broader.

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).

The Configuration Management Database (CMDB) has been the backbone of IT Service Management (ITSM) and IT operations for years. A CMDB is a central repository that stores information about IT assets, configurations, and dependencies, enabling organizations to manage their IT infrastructure more effectively.

Several years ago, while surfing the web, the author of these lines discovered something unexpected… an organization with focus on optical networks he had not heard of before.

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.

Kubernetes excels at managing complex, containerized systems, and one of its most impactful patterns is the sidecar. Sidecar containers extend applications by running supplementary processes in tandem. This modular architecture enables enhanced observability, networking, or security layers — all without changing the core application code. Continuous Integration and Continuous Deployment (CI/CD) practices are key to reliably shipping these configurations.