Automation or AI agent? How to tell which one you need

"Agent" has become the word for any software that uses AI. That's a problem, because the choice between a fixed workflow and an autonomous agent has real consequences for cost, reliability and risk.

Workflows: predictable steps, one smart part

A workflow follows the same path every time. A new email arrives, AI classifies it, the right template is filled in, a person approves it and the reply goes out. The AI does one thing well, understanding messy input, and everything around it is ordinary, testable logic.

Workflows are cheaper to run, easier to debug and much easier to trust. For most business processes they're the right answer.

Agents: the path isn't known in advance

An agent decides its own next step. Give it a goal, such as "find out why this order is late and tell the customer", and it chooses which systems to check, in what order, and when it's done.

That flexibility is genuinely useful when the path really does vary: research tasks, investigations, multi-step support cases. It also comes with costs: more model calls, less predictable behavior, and a harder time explaining what happened when something goes wrong.

A simple test

Ask yourself: could a new employee follow a checklist to do this? If yes, build a workflow. If the honest answer is "it depends, they'd have to figure it out", an agent may be worth it.

The pragmatic middle

Many of our builds start as workflows and gain small agent-like steps where they're needed, for example letting the system look up one more record when the first match is ambiguous. You get most of the flexibility with far less risk.

Start simple, measure, and add autonomy only where it earns its keep.