Not every automation needs a language model. Often a few clear rules do the job faster, cheaper and more reliably. Agents earn their place where the input is unpredictable.
Traditional automation
Rule-based automation follows steps you define. When a form arrives with these fields, create this record and send that message. It is predictable, testable and inexpensive to run.
It struggles when inputs vary a lot, such as free-text emails or documents in many formats.
AI agents
An agent can interpret unstructured input and decide which of several actions to take, using tools you give it. That flexibility comes with less predictability, so it needs limits, logging and review steps.
- Use rules when inputs and outcomes are well defined
- Use an agent when inputs vary and judgement is needed
- Add human review where mistakes are costly
Often the answer is both
A common design uses an AI step to read and classify messy input, then hands clean data to rule-based automation for the reliable part. That keeps the unpredictable component small and contained.
This topic is part of our AI Development & Automation work.