What Is Agent Observability? Traces, Loop Rate, Tool Errors, and Cost per Successful Task

Engineers love shipping agents… right up until the first production incident. Databricks found that tool-calling accuracy can swing by as much as 10 percent on parts of BFCL just by changing generation settings like temperature. That's a friendly reminder that agents can behave "correctly" one day and drift the next, even when nothing obvious changes. A tool-calling agent can return the "correct" final answer while doing five retries behind the scenes, calling the wrong tool twice, and quietly burning your budget. Or it can fail, recover, and still look "fine" if you only judge it by the last message it prints. That is why agent observability matters. Agent observability is the ability to understand, measure, and debug an agent's decisions over time, not just its final answer . It means you can answer questions like Why did the agent pick that tool? Where did the first wrong decision come from? Did it loop, retry, or deviate from the plan? Why did this run cost 10x more than usual? Tha