AI agent or workflow? Start with the decision, not the demo

Practical guide. Before choosing an AI agent, write down which decisions the system must make without you. A process with predictable steps may need a workflow rather than a model that chooses its own next action.

A useful architectural distinction

Anthropic distinguishes predefined workflows from agents that direct their own tool use. Its engineering guidance recommends starting with simpler designs and adding complexity when justified. This is architectural guidance, not evidence that a particular agent will improve your business. Read the engineering article.

Apply the distinction to a real task

Consider processing a supplier invoice. Extracting fields, checking a purchase-order number and sending exceptions for review can follow explicit steps. Letting a model decide which supplier to pay adds a materially different responsibility. We would prototype the first process before granting the second permission.

For a customer-support assistant, separate finding a policy from issuing a refund. The assistant might retrieve information freely while a refund requires a validated order, a permitted amount and an explicit approval rule. Those boundaries belong in code, not only in a prompt.

Write an acceptance test before expanding autonomy

• Define the completed task in business terms.
• Include missing documents and contradictory inputs in the test set.
• Record tool calls, retries, cost and unresolved cases.
• Decide which actions are reversible and which require approval.

Our suggested first comparison is the existing manual process against a small workflow using the same cases. Expand autonomy only if the additional flexibility solves a measured failure. A convincing demonstration alone does not establish reliability.

For the next step, use our AI pilot measurement checklist.

Put this into practice with the free AI evaluation worksheet.