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The latest News and Information on Observabilty for complex systems and related technologies.

MCP = Observability + Code, a Real-life Example

Our bot is hitting an error. We can see it in the distributed trace. Here, see what happened when we noticed it: Austin fired up Claude Code (hooked up to Honeycomb with its MCP tool) and got it to find the error, fix it, deploy, and check that the fix worked. It got a little overconfident at first, but the ending is happy. IRL this took 22 minutes; the video speeds up the AI agent interactions and cuts out waiting. This video includes Austin Parker, Jessica Kerr, and Ken Rimple.

Beyond Shift Left: Engineering Leaders Increase Speed and Resilience With Observability

We recently had the privilege of hosting several industry experts and technology executives across platform strategy, SRE, and engineering enablement for breakfast at our Observability Day in London. We noted that they’re all facing the same fundamental tension: deliver faster, scale smarter, stay resilient, and somehow get ahead of what’s coming next. But how do you move fast without breaking things? And how do you prove the value of the things you don’t break?

Top 5 Observability Tools DevOps Teams Should Know

Observability and monitoring are the cornerstone of resilient, high-performing applications. Nearly every IT or software engineering leader we come into contact with emphasizes the importance of the ability to understand and diagnose what is going on with their applications at all times. Having clear and concise visibility into your applications is no longer optional.

Working with GPUs on Kubernetes and making them observable

GPUs are everywhere powering LLM inference, model training, video processing, and more. Kubernetes is often where these workloads run. But using GPUs in Kubernetes isn’t as simple as using CPUs. You need the right setup. You need efficient scheduling. And most importantly you need visibility. This post walks through how to run GPU workloads on Kubernetes, how to virtualize them efficiently, and how Coroot helps you monitor everything with zero instrumentation or config.

Inside the Wins: Real Stories of Transforming Azure Observability into Business Value

Azure environments are growing fast, and so are the challenges of monitoring them at scale. In this blog, part of our Azure Monitoring series, we look at how real ITOps and CloudOps teams are moving beyond Azure Monitor to achieve hybrid visibility, faster troubleshooting, and better business outcomes. These real-life customer stories show what’s possible when observability becomes operational. Want the full picture? Explore the rest of the series.

Real-Time Observability with ClickHouse, Coroot, and GlassFlow

Coroot is excited to feature an editorial from GlassFlow for our first Open Source Spotlight. We hope to improve the workflow of our global community of SREs and DevOps professionals by sharing exciting projects like Glassflow, which make innovation accessible for everyone through the freedom of open source. If you have an open source or open core project you’d like to see on our blog next, send us a message!

How to Improve Uptime and Achieve Root Cause Analysis (with Open Source!)

Observability doesn’t begin and end at telemetry or your ELK stack: most open source or vendor tools require configuration, dashboard customization, and may not actually pinpoint the data you need to mitigate system risks. Coroot was designed to solve the problem of time-consuming root cause analysis: it handles the full observability journey — from collecting telemetry to turning it into actionable insights. We also strongly believe that simple observability should be an innovation everyone can afford to benefit from: which is why our software is open source.

A Developer's Framework for Selecting the Right Tracing Vendor

Distributed tracing tracks requests as they flow through microservices, revealing bottlenecks, failures, and performance patterns. Without proper tracing, debugging production issues becomes guesswork—especially in complex architectures with dozens of services. Modern applications generate millions of traces daily. The right vendor helps you extract actionable insights without drowning in data or breaking your budget.