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By Jack Gordley
Observing fast-growing agentic workloads is no small feat, especially if you try to build your own monitoring stack or rely solely on tools built for a time before LLMs. At Grafana Labs, we know this all too well.
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By Mat Ryer
Thank you for spending AI Week with us. We’re thrilled by the reaction and we all enjoyed replying to your questions. Thanks for engaging.
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By William Dumont
Continuous integration and continuous delivery (CI/CD) have dramatically changed how we ship software. But once code reaches production, the operational work is still surprisingly manual. Engineers continually monitor systems, investigate unexpected behavior, and decide which issues require action. And that is where the next opportunity for AI-driven automation lies. For example, in today's CI/CD workflows, someone refreshes the pipeline page to see whether the queue has moved.
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By Dafydd Thomas
You’re about to click "Merge" on a PR, but you feel more anxious about it than you used to. Why?
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By Sven Großmann
It's Monday afternoon and that feature you've been working on is mostly done. There's just one item still sitting untouched at the bottom of the ticket: "Add monitoring." You know you should. You also know the sprint ends tomorrow, nobody on the team is an observability expert, and figuring out what to measure—let alone how to write the PromQL for it—feels like a project all on its own. So it gets the same treatment it always does: "We'll add it when it breaks.".
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By Mat Ryer
Observability has traditionally been tacked on after your code hits production, but with agentic operations on the rise, that's no longer sustainable. Agents have dramatically increased the rate of change as they write more code, ship more changes, and operate more systems—all at a speed that compounds scale and complexity.
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By Logan Smith
As AI agents accelerate software development and spin up applications at scale, visibility into what's happening behind the scenes, including query performance and database health, has never been more important. Gaining that level of insight requires observability that can keep pace.
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By Pablo Angulo
If your network restricts inbound or outbound traffic, you likely maintain an allowlist of Grafana Cloud IP addresses so your systems and Grafana Cloud can talk to each other. Today we're introducing a new allowlists design: a single, structured API that replaces the collection of per-product lists we've published until now. If you don't use IP allowlisting—or you connect to Grafana Cloud over private connectivity such as AWS PrivateLink—nothing changes for you, and no action is needed.
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By Arun Ulagappan
Knowing what you're spending on observability is useful. Knowing which team, service, or project is driving that spend is what actually lets you act on that information. Cost attribution is a core part of how Grafana Cloud approaches cost management and optimization.
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By Priscilla Lam
You've optimized your Largest Contentful Paint. Your Time to First Byte is under 200ms. Your Lighthouse scores are green. And yet, your checkout conversion rate is quietly dropping. A segment of users in Southeast Asia is churning. Your support team is fielding tickets about a form that "just doesn't work" and you have no idea which one. Traditional frontend performance monitoring tells you whether your application is fast. It doesn't tell you whether people are actually succeeding when using it.
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By Grafana
Friday, the last day of AI Week, is all about collaboration. Here's what we're announcing today! Check out grafana.ai for more details on everything we announce this week.
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By Grafana
Senior Developer Advocate Nicole van der Hoeven explains how we're thinking about observability and AI at Grafana Labs. She talks about how you can use AI with observability across the entire SDLC and the different tools you can use for both AI for observability and observability for AI.
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By Grafana
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.
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By Grafana
Senior Software Engineer William Dumont demonstrates how we test Assistant Investigations by pitting two versions of the same agent against each other to correctly identify and remediate a production incident we've already resolved.
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By Grafana
Thursday is about testing and evaluation. Here's what we're announcing today! Check out grafana.ai for more details on everything we announce this week.
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By Grafana
In this video, our Senior Director for AI Engineering, Mat Ryer, issues a challenge: Can k6 agentic testing in Grafana Cloud be instructed at a high level to just beat Wordle? Staff Software Engineer Joan López de la Franca Beltran sets up that scenario using agentic testing (currently experimental), yielding some surprising results.
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By Grafana
Staff Software Engineer Alexander Sniffin demonstrates how you can use Assistant Investigations to automatically start an investigation for you when an alert fires in Grafana. When you receive an alert, Investigations can do the work to figure out the root cause so that you don't have to spend your time doing so. Assistant Investigations is now generally available for Grafana Cloud.
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By Grafana
Wednesday is about operations and maintenance. Here's what we're announcing today! Check out grafana.ai for more details on everything we announce this week.
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By Grafana
Tuesday is about Building and implementation. Here's what we're announcing today! Check out grafana.ai for more details on everything we announce this week.
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By Grafana
Your AI agents are only as good as the context it has. Without access to what's happening in production, it can only make educated guesses. Chapters: In this video, you'll meet GCX. The bridge between AI coding agents like Claude Code, Codex, Cursor, and your Grafana observability stack. You will learn how GCX securely gives AI agents access to metrics, logs, traces, dashboards, and other production telemetry so they can investigate issues, answer questions, and help you debug with real operational context.
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Grafana provides a powerful and elegant way to create, explore, and share dashboards and data with your team and the world. Grafana is most commonly used for visualizing time series data for Internet infrastructure and application analytics but many use it in other domains including industrial sensors, home automation, weather, and process control.
Grafana has a robust plugin architecture built for extensibility. Visualize data from more than 40 data sources, including commercial databases and web vendors, and add new graph panels with rich data visualization options. There is built in support for many of the most popular time series data sources. It works with Graphite, Elasticsearch, Cloudwatch, Prometheus, InfluxDB and more.
Grafana Labs is the company behind Grafana, the leading open source software for visualizing time series data. Grafana Labs helps users get the most out of Grafana, enabling them to take control of their unified monitoring and avoid vendor lock in and the spiraling costs of closed solutions.