Operations | Monitoring | ITSM | DevOps | Cloud

At Honeycomb, we’ve been using Claude Code across our engineering team for a while. Anecdotally, I had a sense of who the power users were, and I had seen some examples of complex usage. But I wanted to be able to confidently answer questions, like: Claude Code supports OpenTelemetry out of the box, which means sending telemetry to Honeycomb takes just a few minutes of configuration.

If you’ve ever tried to debug a request that touched five services, a queue, and a database you don’t own, you already know why monitoring distributed systems is hard. Logs live in different places, requests disappear halfway through a flow, and when something breaks in production, you’re reconstructing what happened from fragments. Microservices make this worse by design. A single request fans out across small, independently deployed services, often communicating asynchronously.

Your hero image takes 5 seconds to show up. Your headline sits invisible while JavaScript churns away. Your users? They’ve already hit the back button. That’s the cost of a slow Largest Contentful Paint, and it’s killing your conversions and search rankings. LCP is one of Google’s Core Web Vitals, which means it directly impacts how Google ranks your website. A slow LCP doesn’t just frustrate users, it actively hurts your SEO.

Teams can use Datadog Real User Monitoring (RUM) and RUM without Limits to get full visibility into the frontend health of their applications while retaining only the sessions that contain critical problems that affect the end-user experience. But application errors or slowness often result from backend issues, such as database bottlenecks. To diagnose these issues, you need to correlate the frontend data from RUM with the backend data from Datadog Application Performance Monitoring (APM).

Efficient monitoring delivers fast results: alerts fire within seconds, dashboards refresh continuously, and teams know the moment something changes. Understanding arrives later. An alert may show that a value shifted, but it does not explain why it shifted, how far the impact will spread, or which components truly matter. Teams see the signal, not the system behavior behind it. This gap defines the limit of traditional monitoring. Detection has improved, but explanation has not kept pace.

This is the eighth edition of the SRE Report. Eight years of tracing reliability's arc, from uptime obsession to experience, from toil to intelligence, from systems to people. This year's report is also the first since Catchpoint joined LogicMonitor. We want to acknowledge their support in keeping this work going. They get what this report means to the reliability community, and that matters. We made a deliberate choice this year to say less.

You shouldn’t have to understand the care behind this report, unless it’s missing. For the past eight years, this research has focused on all things related to reliability and resilience. How systems behave under stress. How teams respond when things break. And how the practices continue to evolve. Reaching the eighth edition of The SRE Report attests to that and gives me pause. You can read the full report here and you can find a summary of the key findings here.

Modern systems don't fail because engineers lack skills; they fail because teams can't see why systems are failing at all or can’t see why they’re failing fast enough. Often, the problem isn't a lack of tools — it's a lack of clear, connected visibility across data, teams, and systems. This is where observability transforms how organizations operate. It's no longer just about keeping systems running.

The distributed tracing landscape has evolved from “observability add-on” to core production infrastructure. In 2026, distributed tracing is no longer optional for engineering teams operating microservices, Kubernetes, or AI-driven workloads. It is now tightly coupled with incident response, cost optimization, and AI-assisted debugging.