"Agents" are the most hyped word in AI right now, and that hype is pushing teams to build autonomous, self-directing systems for problems a simple pipeline would solve better. Both approaches use LLMs; they differ in who's in control. Choosing correctly is one of the highest-leverage decisions in an AI feature — it determines your reliability, cost, and how much you'll debug at 2am.
The real distinction
The difference isn't intelligence; it's who decides the next step.
In a workflow, you define the steps. The LLM does the language-heavy work at fixed points, but the control flow is code you wrote. It runs the same way every time.
In an agent, the model decides what to do next. You give it tools and a goal, and it loops — reasoning, acting, observing — until it thinks it's done. The path varies run to run.
