The core distinction between AI agents isn't the model, but how they're controlled. Workflow agents follow a strict, predefined sequence, operating like state machines where each step depends on the previous one. This ensures predictability and safety for critical systems like payment pipelines or booking flows.

Autonomous agents, conversely, are given a goal and decide the steps themselves. They coordinate sub-agents dynamically, adapting their path based on outcomes. This goal-driven approach excels in creative or exploratory tasks where the order of operations is flexible, such as content creation or research.

Most real-world systems use a hybrid approach. An autonomous agent might orchestrate high-level tasks, while invoking workflow agents for specific, critical sub-processes. Understanding this control flow difference is key to designing reliable and effective AI systems, moving beyond simple prompts to structured intelligence.