Use predictable workflows for repeatable tasks and agents only when flexible decisions are genuinely useful.
The core idea
A workflow follows a predefined sequence: validate input, call a service, transform a result, and return an output. An AI agent can choose among tools or steps based on the current situation. That flexibility is useful, but it introduces more uncertainty.
A practical way to approach it
List the decisions your application must make. If the paths can be written as clear rules, begin with a workflow. If the application must interpret varied requests or select tools dynamically, consider an agent with a small, explicit toolset. Add limits on steps, time, and cost.
Try it in a small project
For a student project, build a support-ticket triage workflow first. Add a model to classify ambiguous messages, but keep routing and destructive actions deterministic until you have measured the classifier’s errors.
Common mistakes to avoid
Do not give an agent broad permissions simply because it can use tools. Require confirmation for actions such as sending messages, changing records, or spending money.
Key takeaway
Keep the first version small, make your assumptions explicit, and verify the result with a repeatable test. For production use, check the current official documentation for the tools and versions you choose.