Operations | Monitoring | ITSM | DevOps | Cloud

An alert tells you what changed. It rarely tells you why. That answer usually lives somewhere else: the pull request that shipped minutes earlier, the incident already open in PagerDuty, the runbook sitting in Confluence. Netdata AI can now reach those systems directly. Through the Model Context Protocol, it connects to the tools your team already runs and reads from them while it investigates. It pulls that context alongside the metrics, logs and anomalies Netdata detects on every node, then reasons across both.

Sentry brings errors, logs, replays, spans, and metrics together to find and fix the worst issues in your application.

Tracing does a lot more than help you monitor performance. It's what ties errors, logs, and session replay together in Sentry, so you, and your agents actually know what happened. It's included in every Sentry plan.

What does "good" actually look like for a fiber-to-the-home network? According to two of the people who build them, it looks like nothing at all. No complaints. No support calls. No coffee-talk the next morning about the stream that buffered. Just a light switch that turns all the way on, every time.

Explore how to leverage observability insights to reduce metric costs with Span Observability. Led by Tomasz Romaniuk and Martyna Karbownik from Cisco, this tech talk covers the significance of metric time series in impacting cardinality and consequently driving metric costs. After a theoretical introduction, the presentation dives into practical real-life use cases demonstrating how to effectively utilize available tools for cost reduction. The session concludes with an overview of future developments and a Q&A segment for audience inquiries.

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.

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.