Operations | Monitoring | ITSM | DevOps | Cloud

Getting started with observability in a modern, fast-moving environment is harder than it should be. Open-standards-based observability promises flexibility and vendor neutrality, but in practice it often introduces significant complexity and delays meaningful coverage by months or even years. Each layer of the stack requires its own instrumentation approach, and every technology, runtime, and library version comes with unique setup steps, tradeoffs, and rough edges.

2025 was the year we at Grafana Labs went all-in on AI—and boy, what a year it was. Not only did we establish and start to execute our overarching strategy (build actually useful AI), we also took one of our most exciting new features (Grafana Assistant) from idea to general availability in just nine months! Yes, there's no shortage of articles singing the praises of AI these days, but let's dispense with the hyperbole and focus on some actually useful content.

When you integrate Grafana-managed alert rules with ServiceNow, you can automatically capture and process alerts in ServiceNow’s events table—a common entry point for incident workflows, escalations, and ticket creation. And if you configure ServiceNow to receive Grafana Alerting payloads using ServiceNow’s scripted REST API, you can parse Grafana’s JSON alert payloads and insert them into a ServiceNow table.

Note: A version of this post originally appeared on the CSS Electronics blog. Martin Falch, co-owner and head of sales and marketing at CSS Electronics, is an expert on CAN bus data. Martin works closely with end users, typically OEM engineers, across diverse industries, including automotive, maritime, and industrial. He is passionate about data visualization and AI—and he’s been working extensively with Grafana Assistant.

For Grafana Labs, 2025 was a year defined by innovation, growth, and the power of our community. We celebrated the release of Grafana 12 at our 10th annual GrafanaCON event, and marked major milestones across open source projects, including Mimir, k6, Beyla, Faro, and Alloy. It was also a year of taking bold steps forward in how teams interact with their systems and data.

Cloudflare Workers is a developer platform for deploying serverless functions, frontends, containers, and databases to a global network, spanning 330+ cities around the world. However, as your application scales, it becomes crucial to have the right observability tools to investigate issues, monitor performance, and get alerts when issues arise. Last month, Cloudflare Workers announced support for exporting OpenTelemetry logs and traces, letting you send this data directly to Grafana Cloud.

Whether it’s for an email or that upcoming presentation, many Grafana users like to share their favorite dashboards or panels outside of Grafana itself. The Grafana Image Renderer is a backend service for Grafana that helps you do just that by rendering panels and dashboards as images, such as PNGs and PDFs, via a headless browser. It’s commonly used to support Grafana features like exporting dashboards, generating images for alert notifications, and creating PDF reports.

As developer advocates at Grafana Labs, we’re always looking for new ways to help our users better understand and learn observability. You might remember our previous project that brought learning to life through an adventure-style game, and now we’re really excited to share something else we’ve been working on: Interactive Learning, a new way to get the technical help you need directly in Grafana.

Today’s engineering organizations are built around service ownership. Service owners are accountable for keeping their services reliable, performant, and ready to scale. But no service operates in isolation; every team depends on others, and those dependencies form a complex web that can be hard to see, let alone understand. To truly deliver reliable systems, you need visibility not only into how your own service performs, but also how it affects others.

Modern AI agents are now highly advanced, frequently becoming essential components of engineering workflows and deployment pipelines. However, operating these systems often feels like trying to navigate a ship through a dense fog. When an agent errors, slows down, or consumes excessive resources, engineers find themselves adrift, lacking the navigational charts needed to diagnose the problem. The absence of deep insight makes debugging, performance tuning, and cost management unnecessarily difficult.