Operations | Monitoring | ITSM | DevOps | Cloud

Modern AI agents are now highly advanced, frequently becoming essential components of engineering workflows and deployment pipelines. However, operating these systems often feels like trying to navigate a ship through a dense fog. When an agent errors, slows down, or consumes excessive resources, engineers find themselves adrift, lacking the navigational charts needed to diagnose the problem. The absence of deep insight makes debugging, performance tuning, and cost management unnecessarily difficult.

Today’s AI agents have grown increasingly sophisticated, moving into production environments and becoming integral parts of engineering workflows. But these agents can also be black boxes for engineers, which makes observability more critical than ever. Without proper monitoring, you’re often left feeling like you’re flying blind as you try to debug agent failures, understand performance bottlenecks, and track costs.

Nishad Krishnan is a Software Engineer at Temporal Technologies, where he’s focused on observability and making the “unknown unknowns” slightly less unknown. At Temporal Technologies, our goal is to make it easier for developers to build and operate reliable, scalable applications without sacrificing productivity. Our platform, Temporal, helps ensure that code runs to completion once started, no matter how long it takes or what failures occur along the way.

Grafana is great at hopping between signals in its native backends (Grafana Loki, Grafana Mimir, Grafana Tempo). But your data doesn’t have to live there to get the same smooth workflow. Afterall, we don’t just pay lip service to our “big tent” philosophy—we want to meet all our users’ diverse needs, regardless of what kind of data you have or where you store it.