Operations | Monitoring | ITSM | DevOps | Cloud

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.

Today’s AI agents have grown increasingly sophisticated, moving into production environments and becoming integral parts of engineering workflows. But these agents can also be black boxes for engineers, which makes observability more critical than ever. Without proper monitoring, you’re often left feeling like you’re flying blind as you try to debug agent failures, understand performance bottlenecks, and track costs.