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.

I recently heard a song that was written by a girl but sung and composed by AI. It left me confused— I'm not sure if I liked it. I was a bit prejudiced and automatically hated it because it was composed by AI. Similar to many works of AI, it had a rhythm, but it was too repetitive and lacked emotions. This made me think about all the other forms of art that might be created by AI, and it left me with a bitter feeling.

AI agents are quickly becoming part of the enterprise automation conversation because, among other things, they help teams move faster. But there is a major difference between an AI agent that sounds useful in a demo and an AI agent that is ready for production. Production agents need scope. They need to know what they own, which systems they can touch, which workflows they can run, which teams they support, and where the boundaries are.