Operations | Monitoring | ITSM | DevOps | Cloud

An Avantra SAP security dashboard gives SAP operations teams one view of SAP security KPIs, covering SAP Notes and HotNews status, system hardening, user access risk, and audit compliance across on-premises, hyperscaler, and RISE with SAP systems. Security teams have their own tools: a SIEM, a SOC console, vulnerability scanners. SAP operations teams usually don’t.

At Grafana Labs, observability is what we do. So as we started building AI agents, we naturally reached for the same instincts we bring to every system: measure it, set targets, and make reliability something you can reason about instead of hope for. That instinct led us somewhere unexpectedly useful. It turns out one of the oldest ideas in reliability engineering, the error budget, maps beautifully onto one of the newest problems in software: how do you know if an AI agent is actually any good?

When something goes wrong in production, you do not want to spend the first five minutes rearranging charts. You want the error rate, throughput, queue depth, memory, and view metrics that show when customers are having issues while using your app.

Alert routing often starts simple. A team creates a few contact points, adds some label matchers, and builds a notification policy tree that sends each alert to the right destination. But alerting configurations rarely stay simple. As an organization grows, its notification policy tree must accommodate more teams, services, and routing requirements. Changes for one team still require editing a global configuration, making ownership less clear and independent provisioning harder.

When something breaks in production, the questions that matter most are also the toughest to answer from metrics alone: who was affected, what did they actually see, and is this worth waking someone up for? Answering those questions requires a fuller picture of the issue and its impact on your users. That’s where Digital Experience Monitoring (DEM) in Grafana Cloud comes in.

Labels are a powerful way to organize telemetry and define policies across Grafana Cloud, helping to streamline alerting, attribution, access control, and more. But traditionally, custom labels in Synthetic Monitoring have worked a little differently: they only lived on a single sm_check_info metric, and Grafana Cloud prefixed each one with label_.

If your Cypress suite has tests that fail more often or run slower, you know it can be hard to figure out the pattern from a single job. It could be one spec that slowed down, or a single test that fails, or maybe the entire suite is trending slower. The root cause could be a bug in the app, or a flaky test, or something else.

If you've looked at SquaredUp and thought "I don't need this, I'll just use Claude", I understand completely. I've thought it too. Here's what changed my mind. I use Claude constantly. I've also spent the last few months building the parts of SquaredUp that let it in: our MCP server, the object graph and correlation, a stack of plugins. So this isn't a dashboard vendor being sniffy about AI. I've watched Claude pull from several sources and produce something genuinely useful in under a minute.