Operations | Monitoring | ITSM | DevOps | Cloud

On the product team here at Grafana Labs, we consider AI agents our users, too. That’s why we set out to test how well agents can debug incidents across the full stack, and how much better they perform with Grafana Cloud’s Knowledge Graph vs. using raw telemetry alone. Our early results are promising. In one real incident we replayed 16 times each way, an agent with Knowledge Graph context found the correct root cause 15 times, compared with just once using raw telemetry alone.

Say you get a support escalation about a page in the app that won’t load. But when you pull up your synthetic checks, they're all green: 100% uptime, probes are passing. Something's not adding up, but which one do you trust? If you’ve run Grafana Cloud Synthetic Monitoring, you’ve been on both sides of this. Sometimes it's the ticket: real users hit a wall on the path but your checks pass cleanly. Other times, it’s the inverse.

Grafana Cloud Frontend Observability helps engineering teams quantify the end user experience by bringing metrics, logs, traces, and user session context to client-side web applications. Teams can monitor application health and performance over time, triage errors, and correlate frontend signals with backend telemetry to investigate issues across the stack.

As testing workflows become more AI-assisted, fast access to accurate documentation matters more than ever. Whether you're writing a new load test, troubleshooting an issue, or having an AI agent generate a script for you, you need reliable guidance that keeps pace with the way you work. But most documentation still lives in a browser. Every time you or your agent needs to verify an API or look up a best practice, you're forced to leave your terminal or editor and interrupt your workflow.

Tracing is one of the richest observability signals, but it's also noisy and susceptible to data bloat. In a busy system, the vast majority of traces describe the same healthy, fast, successful request over and over, so most organizations downsample their traces to cut costs. But that approach has consequences, since the sampling strategy you choose determines whether you get a faithful picture of your whole system, or just a smaller, blurrier copy of your busiest endpoints.

Here's a scenario that will likely sound familiar: You’re building an executive overview dashboard that you would put on a wall-mounted screen so the whole room can see how the business is doing at a glance. It’s for a Shopify online store, and displays a mix of business and application signals, including latency panels, error-rate panels, and a big stat panel for revenue-per-week. It looked great. But something is missing.

When a Terraform run feels slow, most teams are flying blind. The run log in HCP Terraform tells you what happened, but not what took so long—and it certainly doesn't roll up across hundreds of runs so you can spot a trend.

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.

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.

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.