What is Agent Mining?

Agent Mining applies Process Mining methods to the activity of AI agents: their calls, tool invocations, and decisions are captured as event data and analyzed like a process. This reveals which paths an agent actually took, where it fails, loops, or performs unnecessarily expensive steps. Agent Mining relates to AI agents the way Task Mining relates to human desktop work.

Why does this matter?

As soon as agents take part in productive workflows, they become part of the process themselves – with runtimes, costs, error rates, and variants. Without Agent Mining their behavior remains a black box that can neither be optimized deliberately nor evidenced to business owners and auditors.

What does this look like in practice?

An agent handles 300 requests. Agent mining shows that in 42 of them it queries the same data source twice before answering, and in 9 it skips a clarification step its instructions require. These are process findings about the agent — produced with the same means as findings about a human workflow.

Not to be confused with

Not to be confused with model monitoring, which measures output quality and drift. Agent mining looks at the sequence of steps and how often they occur, not at the quality of any single answer.