If a task has a fixed set of steps that never change — when X happens, do Y, then Z — a traditional automation tool is faster to build, cheaper to run, and easier to debug than an AI agent. Don't reach for an LLM to move a row between two spreadsheets.
Agents earn their cost when the steps themselves depend on judgment: reading an unstructured email and deciding which of twelve possible actions it warrants, or negotiating between two systems that don't agree on the same customer record. That's not a rules engine problem. It's a reasoning problem.
In practice, the systems we ship are usually both: a deterministic pipeline handles the parts that are always the same, and an agent sits at the one or two decision points that actually require judgment. Most of the value, and most of the risk, concentrates at that handoff — which is exactly where we spend the design effort.
If someone offers to 'agentify' your entire workflow end to end, ask which parts are actually judgment calls. Usually it's fewer than they're pitching, and that's fine — a smaller, well-scoped agent beats an oversized one that hallucinates its way through steps that didn't need a model at all.