AI agent or workflow automation? Use the simplest reliable approach

Not every process needs an AI agent. Reliable implementations use the least complex approach that can handle the real operating requirement.

Use conventional automation for predictable paths

If an event should always trigger the same validated steps, rule-based workflow automation is usually easier to test, explain, monitor, and recover. Examples include synchronising a confirmed record, sending an approved notification, or routing an invoice by a defined authority matrix.

Add AI for bounded interpretation

AI can improve a deterministic workflow when one step involves classifying text, extracting information, summarising a document, identifying a topic, or drafting content for review. The surrounding workflow still controls inputs, outputs, approvals, and system actions.

Use an agent when the route depends on context

An agent becomes useful when a bounded task requires gathering information, selecting among authorised tools, deciding which step should come next, and adapting to variable but understood situations. The agent should still have clear goals, permissions, stopping conditions, budgets, and escalation rules.

Hybrid designs are often strongest

A service-resolution agent may interpret a request and collect context, while deterministic logic applies customer tier, policy, approval, and record-update rules. This combines flexibility where it is useful with predictability where control matters.

Ask five questions before choosing

1. Is the correct path known in advance?
2. Does the task require interpretation or only data transfer?
3. What happens if the system makes a poor decision?
4. Can the result be evaluated against representative examples?
5. Which actions must remain behind explicit human approval?

If a simpler workflow can meet the requirement, use it. Complexity is justified only when it creates material operating value.