Plenty of AI workflows are defined. Fewer are governed. The difference is who enforces the process. In an ungoverned workflow, the steps and the checkpoints are text the model reads: "always run the tests", "wait for approval before publishing". The model usually complies, and when it does not, nothing stops it and nothing records the miss. Governance means the controls hold even when the model gets something wrong.
Four properties separate a governed workflow from a well-written prompt. First, the process is structure stored outside the model, so it is the same for every agent and every run. Second, checkpoints are gates: conditions an engine evaluates on each attempt to advance, which return what is missing when they fail. Third, rules are delivered at the step where they apply, instead of buried in a long instruction file. Fourth, the record keeps the agent and the human apart, so a reviewer can tell which automation acted and under whose authority.
Governed does not mean slow. A good governed workflow stops only where a wrong move is expensive, such as a release, a payment or a message sent to a customer, and runs freely everywhere else. Teams usually start with approvals on many steps and remove them as the process earns trust.