Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance

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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. Along the way, we also uncovered some of the challenges that still stand in the way of reliable AI-assisted debugging, from chasing the wrong signals to confidently making things up and producing inconsistent answers.We’re still early, but our findings point to an important idea. The industry’s shorthand right now is that a bigger context window will lead to better outputs. Our findings suggest it’s not just about more context; it’s about structuring your data well enough to serve the right context. Here’s a look at what we’ve learned so far, as we continue to experiment out in the open and bring you along, the Grafana Labs way.Giving an agent access to telemetry is just the beginning Give a current-generation model like Opus 4.8 access to your raw telemetry during a live incident, and it genuinely starts to figure things out: querying metrics and logs, forming a hypothesis, and checking it. We have watched it work on our own incidents, and it does it affordably. But if you run software at scale, where uptime is business-critical and large teams share the responsibility, a better model alone doesn’t …

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