Operations | Monitoring | ITSM | DevOps | Cloud

So you've built a client application and you've started sending telemetry. The information sent back by this client is vital to you, and one of the first things you care about is capturing and reporting errors. There are at least two ways to report error details in OpenTelemetry. Web applications generally place exceptions in trace spans as span events, and mobile applications send exceptions as log messages instead.

If there’s one thing clear from our AI-powered observability webinar, it’s that observability has officially graduated from a “nice-to-have” to a business-critical discipline, and AI is helping lead that charge. Our webinar brought together guest speaker Stephen Elliott, Group VP at IDC, and Ranbir Chawla, former SVP of Engineering at RB Global, for an hour of insights that mixed data, experience, and hard-won lessons from the trenches.

When troubleshooting complex software, it’s important to be able to gain insight via its telemetry quickly and precisely. No one wants to waste time switching between tools or worrying about how to interact with different types of data. At Honeycomb, all your data is available in one place, accessible via our fast query engine. But what does that look like in practice?

SaaS products that are built for engineers power critical workflows, yet their designs are often afterthoughts. SaaS products often assume that technical audiences will figure out their way through a complex experience, or just forgive them for the paper cuts on the way. A foundational design system can be perceived as a layer of polish rather than an infrastructure investment, especially in the early stages of a startup.

AI agents can interact with the world using tools. Those tools can be generic or specific. For example: Generic: Specific: The most general ones, like “run a bash command” and “read and write files” are built into the agent. More specific ones are provided through Model Control Protocol (MCP) servers. Every tool provided to the agent comes with instructions sent as part of the context.

Non-deterministic UIs are the future and will replace your dashboards, but they’re not here yet. So until then, we’re stuck with conversational interfaces. In an effort to try and describe what I consider the future of UIs to look like, I wrote about how you (and I) have been designing dashboards wrong. The core insight was that we've been designing for static representations of data that sit on a TV in the office, when the actual use case is someone at a desk using them to debug an issue.

When we introduced Canvas in beta, our goal was to reimagine how teams explore and collaborate around their observability data without requiring manual querying. Canvas has quickly become the AI-guided workspace that helps teams transform raw telemetry into meaningful, shared understanding faster than ever before. And today, we’re thrilled to announce that Canvas is now Generally Available (GA) for all Honeycomb users.

Okay, I'm going to say something that would make 2016 Charity want to throw her laptop across the room: we're making a major investment in metrics at Honeycomb. I know, I know. "But Charity, you literally called them ‘shit salad!’" I did. Also "nerfed dimensions." I said they would "fucking kneecap you." For most of the past decade, I've been social media’s most reliable anti-metrics evangelist. Have I repented? No.