Most broken automations I've audited come down to one fundamental mistake: treating an agent like a workflow, or treating a workflow like an agent. They are not the same thing. A workflow is a train track. It is deterministic. Every trigger, condition, and API call is hardcoded in advance. Node A connects to Node B. If the payload matches, it routes to Node C. There is zero hallucination. It runs the exact same way every single time at near-zero cost. An AI agent is a driver with a map. It is non-deterministic. It evaluates unstructured inputs, decides which tools to call at runtime, and adjusts its plan based on intermediate feedback. Here is the problem: When people use an agent for a task that belongs on a train track, they waste money, introduce latency, and invite random errors When they use a rigid workflow for a task that requires semantic judgment, the system breaks at the very first edge case The rule I follow is simple: - Use workflows for routing, formatting, and state changes. - Use agents only when the path from input to output cannot be predicted by an if-then rule. - Deterministic software holds the track. The agent only steps in when the terrain turns unstructured. Where are you currently using an AI agent where a simple 3-node deterministic rule would actually be 10x more reliable?