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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.

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.