Short answer: use traditional automation when the steps are known and the inputs are structured. Consider an AI agent only when the work requires judgement over messy, unstructured information, and when you can afford to check its output.
Two different tools
Traditional automation follows rules you write: when a form is submitted, create a record, notify the owner, schedule a follow-up. It is predictable, cheap to run, and easy to test. When it fails, it usually fails loudly and in the same way every time.
An AI agent works from instructions and context rather than fixed rules. It can read an email, decide what the sender wants, look something up, and draft a response. That flexibility is the point, and also the risk: the same input can produce different outputs, and mistakes can look perfectly plausible.
A simple decision framework
- Can you write the rules down? If a competent new hire could follow a checklist, start with conventional automation.
- Is the input structured? Forms, database fields, and API payloads suit rules. Free-text emails, documents, and conversations are where AI helps.
- What does a mistake cost? Low-cost, easily reversed actions can be automated further. High-impact actions need a person to approve them.
- Can you measure quality? If you cannot tell whether an agent did the job well, you are not ready to rely on it.
The hybrid that usually wins
Most good systems combine both. Rules handle the predictable spine of the workflow: routing, records, notifications, deadlines. AI handles the narrow step that needs interpretation, such as classifying a request or summarizing a document, and hands its result back to the rules with a confidence signal.
This keeps the system observable. You know where AI is involved, what it decided, and where a person can step in.
Risks and limitations
- Agents can act on wrong assumptions. Limit the tools and permissions they have.
- Model behaviour can change when providers update them. Re-run your evaluation set after changes.
- Costs scale with usage in ways rule-based automation does not. Model them early.
Where to start
Map the workflow first. Mark each step as rule-based, judgement-based, or unnecessary. Remove the unnecessary steps, automate the rules, and only then decide whether the remaining judgement steps justify an agent.