We define it plainly: work flows through a series of granular processes. A process is a sequence of actions, and a core activity is the smallest named piece of work that still matters. The point is being clear enough that a person or an agent can follow the path without guessing.
Think about a client file moving across desks in a small office. Someone receives it, someone checks it, someone fills in missing information, someone reviews it, someone sends the finished thing to the client. The file is the same file, but the work changes at each desk. That’s a workflow.
How it shows up
When you work with AI tools, workflows become training material. Say “handle onboarding” and the agent has to invent the desks. Say “receive the signed agreement, extract these fields, create the client folder, draft the welcome email, then stop for review,” and the agent has a path to follow. This is why the best skills are really workflows: not a clever prompt, but a saved way to run a repeatable path. It also tells you when not to use AI. A fixed, rules-based path may only need automation; one that needs language, judgment, and review fits an agent.
Why you care
Workflows expose the places where work breaks: missing input, unclear owner, slow handoff, too much judgment at the wrong moment. AI doesn’t remove those problems, it makes them louder. The practical move is simple: write the path down, run it once with the agent, correct it, and keep tightening until the output is boringly reliable.