People often ask for an AI agent when what they need is a dependable sequence of steps. The reverse also happens: a rigid workflow built for a job full of exceptions. The choice is easier if you look at how predictable the work is.
Use plain automation when the steps are known
If you can write the process on one page, such as "when a form is submitted, create a record, send a confirmation and notify the team", build it as a deterministic workflow. It will do the same thing every time, cost little to run, and be easy to test. Adding an AI model to a process that did not need one adds cost and variance.
Use an agent when the input is open-ended
Where the input is free text, the right next step depends on interpretation, and the cases vary widely, an agent can handle breadth that rules cannot. Typical examples are triaging support messages, answering questions over a body of documents, or drafting replies for a person to approve.
Often the answer is a hybrid
Use AI for the part that needs interpretation, such as reading an email and extracting a request, then hand the result to a fixed workflow that performs the action. This keeps the unpredictable part small and the consequential part controlled.
What to ask the specialist
- How will we know it works? Ask for example inputs and expected outputs, and for the test set to be shared with you.
- What does it do when it is unsure, and when does it hand over to a person?
- What is it not allowed to do? Limits on actions, spending and data access should be explicit.
- Where does our data go, and which model providers are involved?
- What does it cost to run per month at our volume, and what changes that cost?
Start with a person in the loop
For anything that sends messages, changes records or spends money, begin with the agent proposing actions and a person approving them. Remove approvals only for the actions that have proved reliable. The listing's work-mode label should tell you how much human involvement to expect; see how to choose between the labels.