Building the first version of an AI workflow is usually easy. Connect an LLM to a few tools. Add some instructions. Let the model decide what to do next. Run the demo. It works. The problem starts later, when that workflow becomes part of a real process. Suddenly the important questions are not about the prompt anymore. They are about reliability. What happens when a tool fails ? What happens when the model retries the wrong thing ? What happens when the workflow changes state but the agent still claims failure ? What happens when the agent claims success but no tool actually ran ? What happens when one agent hands bad context to another agent ? This is where AI workflows stop being prompt engineering. They become Systems Engineering . The Demo Is Not The System A lot of AI workflow demos optimize for the happy path. The user asks for something. The agent thinks. The agent calls a tool. The tool returns a result. The agent summarizes the result. Everyone claps.…