Operations | Monitoring | ITSM | DevOps | Cloud

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.

There’s a viral saying folks use about flaky crushes, spouses, and forgetful friends: "if he wanted to, he would." The idea is straightforward: when someone cares, they make the effort. As it turns out, the same principle applies surprisingly well to AI. Systems, like people, have things they "want" to do. Each model has patterns of reasoning and synthesis it performs naturally.

If you caught our recent Introducing Honeycomb MCP: Your AI Agent’s New Superpower webinar, you know it was a lively mix of big ideas, demos, and a few laughs about the messy, fast-moving world of AI. Hosted by Austin Parker, Morgante Pell, and James Bland from AWS, the conversation explored how Honeycomb’s new Model Context Protocol (MCP) is changing the way developers and AI agents interact with data.

A data analyst opens a new AI tool and spends 30 seconds generating a complex visualization of quarterly revenue trends. Chart duly generated, they have to decide: could they present this chart directly to their CEO?

Honeycomb’s Jessica Kerr and Fred Hebert recently hosted a webinar with Courtney Nash of The VOID where they dug into one of the biggest questions in tech right now: How do we build systems (and teams) that actually learn with AI, not just use it? The conversation was surprisingly optimistic about what happens when we stop treating AI as a productivity tool and start seeing it as a teammate. You can watch the full webinar here, or read on below for a quick recap.

A 200 OK doesn't always mean everything is okay. You've probably seen it: your health check endpoint returns success, but your users are staring at an error page. Maybe the database connection pool is exhausted, or a critical downstream service is timing out, but your API dutifully returns 200 because technically it responded. This is the reality of monitoring HTTP endpoints in production—status codes alone don't tell the whole story.

We've just released Refinery 3.0, a performance-focused update which significantly improves Refinery's CPU and memory efficiency. Refinery has a big job: it performs dynamic, consistent tail-based sampling that maintains proportions across key fields, adjusts to changes in throughput, and reports accurate sampling rates.