The actual difference
A workflow is a path you defined, with a model doing the judgment at specific steps. An agent decides the path itself, choosing what to do next and when to stop. The question is not which is more advanced — it is whether you already know the sequence.
If you know the steps, don’t make it guess them
Most business processes have a known shape: receive, classify, look up, decide, respond, record. Handing that to an agent to rediscover on every request buys you unpredictability, higher cost, and harder debugging, in exchange for nothing you needed.
Letting a model choose the path is only valuable when you genuinely don’t know it. Most of the time, you do.
Workflows are easier to evaluate
A fixed path means each step can be scored independently, which is what makes an evaluation set possible to write. Agent runs vary between attempts, so you end up judging outcomes without knowing which decision caused the result.
Where agents genuinely earn it
Open-ended research, exploratory data work, and tasks whose steps depend on what earlier steps discover. The common thread is that enumerating the path in advance is genuinely impossible, not merely tedious.
The hybrid is usually right
A fixed outer workflow with one agentic step inside it captures most of the flexibility and keeps the predictability. It also contains the blast radius, which matters because an autonomous step that is confidently wrong is much harder to catch than a wrong answer at a known stage.
Cost scales differently
Agents make an unpredictable number of calls per task, so their cost is a distribution rather than a number, with a long tail. That tail is where budgets go missing, and it compounds the drift that makes AI features expensive after launch.