TL;DR: Half the "AI agents" in production are expensive, fragile reimplementations of workflows. If you know the sequence of steps your business process needs, you do not need an agent. You need a state machine with LLM calls in the right places. Agents are for genuinely open-ended problems. Everything else is a workflow in disguise.
I have spent the last year watching companies build "AI agents" that are really just if/else chains wrapped in a chat loop. They spend six figures on prompt engineering to make a workflow reliable when a graph with three nodes and one LLM call would have done the job for fifty bucks a month.
The agent hype made us forget a basic engineering principle: when you know the steps, do not use a system that has to figure them out.
The Real Test: Do You Actually Have an Open-Ended Problem?
An agent is the right tool when the sequence of steps cannot be known in advance. "Book me the cheapest flight to Berlin next week" is an agent problem. The number of sub-steps is large, the data sources are varied, and the optimal path changes with context.
