Operations | Monitoring | ITSM | DevOps | Cloud

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.

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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.

You’re about to click "Merge" on a PR, but you feel more anxious about it than you used to. Why?

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.".

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.