Operations | Monitoring | ITSM | DevOps | Cloud

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.

In July, we introduced powerful new Honeycomb Telemetry Pipeline features that helped teams take control of their observability data with safe sampling, flexible rehydration, and a visual pipeline builder. Since then, we’ve built on that foundation. Today, we’re introducing the latest enhancements to Honeycomb Telemetry Pipeline, which give teams deeper visibility into pipeline health, more efficient access to archived telemetry data, and reduced operational complexity.

More and more enterprises are shifting toward private cloud and hybrid deployments for control, data residency, and security. At the same time, observability is no longer a “nice to have” tool. It's mission-critical for teams driving rapid change across cloud-native, multi-service architectures. Leaders are realizing they need deep visibility and rapid debugging everywhere their systems run.

Observability works best when everyone who needs visibility can get it without the risk of unintentional changes. Honeycomb’s role-based access control system helps teams strike that balance with a selection of Owner, Member, and Read-Only member roles. This control gives teams more flexibility in how they share access across their organization, helping you scale visibility safely without sacrificing control.

A common question and concern I hear about AI agents has to do with accuracy. This makes sense, I think. We’re accustomed to think of software systems as entirely deterministic—unpredictable results are a sign of bugs or logic errors.