There is a real difference under the word "agent", and it is worth knowing because it decides what a system costs and whether it can be trusted.
In a workflow, the steps are written in advance. Read the document, pull out the fields, check them against the order, draft the bill, ask a person if it is over the limit. A model does the work inside each step, but the order never changes.
In an agent, the model decides the next step itself. It chooses which tool to use, when to use it and when it is finished.
Which one a job needs
Ask one question: can you write the steps down?
For almost every job in a business, the answer is yes. A new enquiry goes through the same stages every time. An invoice is checked the same way. A booking is made the same way. The content varies, and that is where the model earns its place, but the sequence does not.
When the steps are known, a workflow wins on every count. It is predictable. Each step can be tested on its own. It is cheaper to run, because the model is called for a few hard moments rather than for every decision. And when it goes wrong, the log says exactly which step went wrong.
When an agent is right
Sometimes the steps cannot be listed. Open-ended research. Troubleshooting a problem nobody has seen before. Those are agent-shaped jobs, and even then we give the agent a small set of tools, actions that can be undone, a log of everything it did and a person approving anything that matters.
What this means for you
If someone proposes an agent for a job you could describe in five bullet points, ask why. The usual answer is that agents are the thing being sold this year. The better answer is a workflow with a model inside it, and it is the one we build nearly every time.
A longer version of this is on the blog: AI agents and workflows.