Most 'AI agents' today are just if-else workflows with an LLM bolted on, not real agents

Quick test to check if your system is a real agent: change the input in a way it's never seen before, like a missing field, a tool that errors out mid-way, or a user asking something that contradicts the original request. If the system adjusts its plan, picks a different tool, or asks a clarifying question, that's an agent. If it crashes, goes off topic, or just repeats the same scripted response regardless of input, that's a workflow wearing an agent costume. The real boundary is this: a true agent runs a loop of plan, act, observe, replan. A workflow is just a fixed sequence of steps with an LLM plugged in to generate text or classify intent. A lot of products call themselves agents but under the hood it's literally if intent == X then call tool Y, with no step where the LLM actually decides the order of actions or skips a step that isn't needed. This isn't a bad thing btw. Workflows are more stable, easier to debug, cheaper on tokens, and for most real use cases (support bots, lead qualification, content pipelines) a workflow is the right call because what you actually need is predictability, not autonomy. The problem is when it gets sold as a real agent and customers expect it to handle edge cases, when in reality it only works on the exact happy path that got tested. Mental model I use: ask "if the first tool call fails, what does the system do next". If the answer is "crashes or returns a fixed error", it's a workflow. If the answer is "the LLM tries something else or asks again", it's an agent. Anyone built something they thought was an agent and only found out it was a disguised workflow once they hit an edge case? Curious how you all caught it.