Operations | Monitoring | ITSM | DevOps | Cloud

In the June edition of the Kubernetes Monitoring Helm chart office hours, we discuss the version 4.1 release, the upcoming 4.2 feature release, and we discuss the deprecation of the 1.x and 2.0 versions.

In this tutorial you'll learn how to use Uptrace and Grafana together. Uptrace exposes a Prometheus-compatible HTTP endpoint, so you can add it as a data source in Grafana and reuse your existing dashboards without changing metric names or rewriting queries.

Can you actually trust an AI agent? In this pre-recorded episode of The Context Window, Nicole van der Hoeven sits down with Yas Ekinci, an engineer on the Grafana AI team, to talk about evals — how Grafana measures the quality and reliability of the AI it ships. They get into the difference between online and offline evals, why reviewing AI-generated code has become the real bottleneck, the "final answer problem" of plausible-but-wrong outputs, and o11y-bench, Grafana's open benchmark for observability agents. Along the way.

Learn the two main ways to get data into Grafana Cloud. In this video, we break down how Grafana Cloud connects to over 150 external data sources (like Salesforce, Postgres, and CloudWatch) where your data stays in place, and how you can send raw telemetry into Grafana’s fully managed databases for logs, metrics, traces, and profiles.

Grafana 13.1 cuts down on GitOps pain by making Git Sync stronger — import dashboards to Git with a click, see your repo READMEs inside Grafana, and sign every commit for security-strict teams.

Trust is everything when AI gets personal. Golden Grot Award winner and NeoSapien co-founder and CEO Dhananjay Yadav shares how his team uses Grafana Assistant to ensure the privacy-first AI wearable delivers a seamless, reliable experience without compromising its mission. Because when AI moves closer to our everyday lives, teams need to know what’s happening — and users need to trust that it’s working as intended.

The Grafana AI team (Engineers Ivana Huckova and Sonia Aguilar) share what's new in AI Observability this week: a new way to instrument and visualize agent workflows, plus a neat trick for jumping straight from a metric spike to the exact conversation that caused it using Prometheus exemplars. In this episode: We're showing parts of our team meetings to build in public in some small way and give you a sneak preview of what's to come. But not all features we show may make it to production! You've been warned. :)

Our distributed tracing journey from the inception of Tempo to 3.0. Can't comment in the chat? You may need to create a channel. Grafana Cloud is the easiest way to get started with Grafana dashboards, metrics, logs, traces, and profiles.

Want to try Grafana without installing anything? Jump into Grafana Play, our free sandbox environment where you can explore dashboards, experiment with features, and see Grafana in action, no login or setup required.

Asimov's Three Laws of Robotics are missing one — and when it comes to testing and observing AI, Nicole van der Hoeven argues that missing rule changes everything: before a robot can avoid harm, obey orders, or protect itself, there has to be a Zeroth Law: a robot must be observable. Because if you can't see what a system is doing, you have no way of knowing whether it's following any rule at all.

The 2026 Observability Survey from Grafana Labs heard from over 1,300 engineers and leaders across 76 countries on the real-world role of AI in observability. The data reveals a sharp distinction between intelligence and autonomy — and a critical blind spot most teams have.

Learn how to create and customize dashboards on Uptime.com, including setting up the General and Display Settings tabs, configuring check cards, filtering by state, setting sort orders, displaying metrics, managing alerts, and creating multiple dashboards.

Grafana AI Observability is our new database and platform for observing AI Agents. Over the past year at Grafana Labs, we built Agents and we needed a way to understand how they are performing, what are the costs associated with them, what's the error rate or time to the first token as well as how they are behaving. Grafana Staff Engineer, Ivana Hučková provides a deep dive demo on how Grafana AI Observability connects our experience building Agents with our experience building observability systems.

Context Offloading is a pipeline solution for managing Observability with AI Agents. If you are building AI Agents that work with real data, the context window can very easily get filled with bloated context that the Agent does not really need. Sven demonstrates "Context Offloading", a solution that stores the JSON result and sends only the summary of the JSON blob, making the LLM loop performance much quicker and keeping your context window small.

MCP vs CLI: which one should your AI agent actually use? We get into it with Grafana's cloud and OSS MCP servers and gcx.

Grafana Assistant is going places you might not expect — including healthcare. Golden Grot winner Oren Lion from TeleTracking reveals how Grafana Cloud supports their systems that help keep patient care moving — and how Assistant enables teams to get from “what happened?” to “here’s why” faster. From moon landings to patient care, Grafana is everywhere. Congratulations to Oren, Chris Johnson, Mark Munson, and the entire TeleTracking team on winning this year's Golden Grot Award for Pioneering AI in Observability!

This is an excerpt from a real AI team weekly meeting where we talk about the stuff we build and occasionally also demo them! In this one, Principal Software Engineer Sven Großmann demos how he integrated AI Observability into the OTel demo, complete with the guards feature he introduced last week, and Principal Software Engineer Yas Ekinci gives a rare glimpse of LLMSpec, the internal counterpart of the o11ybench benchmark that we use to evaluate Assistant.

Tempo 3.0 introduces a major architectural shift that decouples the read and write paths, with Kafka handling durability on the write side and a new live store serving recent traces on the read side. Blocks are now written at a replication factor of one instead of three, significantly reducing storage overhead. This release also brings TraceQL metrics to general availability, adds comparison operators for filtering metric results at query time, and introduces a new Tempo CLI redact command for removing sensitive trace data on demand without waiting for retention to expire.