An AI workflow is a chain of steps, often several model calls and tools wired together, that turns an input into a finished output, with checkpoints where a human can review along the way.
Where an agent decides its own next move, a workflow is a defined pipeline: research → outline → draft → edit → publish, for example. Each stage has a clear job and hands its output to the next. This makes the result more predictable and easier to debug than a fully open-ended agent, which is why teams reach for workflows when they ship the same kind of output repeatedly.
Workflows shine for content pipelines, release notes, data cleanup and code review, anything with a repeatable shape and points where a person should sign off before the process continues.
Most are open source and drop into your existing setup, so you can adapt a stage rather than build the whole pipeline from scratch.
Use a workflow when the task has a repeatable shape and you want predictable, reviewable stages. Use an agent when the path to the goal is open-ended.