Why calling every multi-step AI system an "agent" blurs the one distinction that actually matters — who decides what happens next, and when that decision gets made.
A pipeline that fetches data, feeds it to an LLM, and formats the output gets called an "AI agent" almost as often as a system that genuinely plans its own actions. That looseness costs something real: it hides the one design decision that determines how a system behaves when reality doesn't match expectations. A workflow follows a sequence of steps a human laid out in advance, no matter what happens along the way. An agent has an LLM in the loop deciding, at runtime, what the next step even is — based on what just happened. Same building blocks, often the same LLM calls, completely different control flow.