AI Agents vs AI Workflows (Part - 2/2)

Added: 2026-05-06

[00:00:00]In a workflow, you, the developer, decide the steps in advance. The LLM executes each step, but the sequence, branching, and logic are defined in your code. Your code decides step one, call LLM to classify input. Step two, based on classification, call LLM to generate response. Step three, call LLM to translate response. The LLM is powerful at each step, but it doesn't choose what to do next. Your code does. In an agent, the LLM decides what to do next. It looks at the current situation, picks a tool, uses the result to decide the next action, and keeps going until the task is done. The LLM decides, "I need to check the calendar." Calls calendar tool. "There's a conflict. Let me find alternatives." Calls search tool. "I found an option that works." Generates final response. The developer provides the tools and guardrails. The LLM decides how to use them. Let's put these side by side to see the differences clearly. In a workflow, the developer defines the steps, execution is predictable, and you know exactly how many LLM calls you'll make. In an agent, the LLM decides the steps, execution is dynamic, and the number of calls varies depending on the task. It's not always a clean binary. Most real systems fall somewhere on a spectrum. On the far left, you have a pure workflow, where every step is predefined. Move right, and you start giving the LLM more control. First, it picks a path, routing, then it decides what subtask to create, orchestrator. And at the far right, you have a fully autonomous agent running in a loop with full control.

[00:01:26]Moving right on this spectrum means giving the LLM more control. More control means more flexibility, and but also more cost, more latency, and harder debugging. Ask yourself, "Can I draw a flowchart of every possible path before running it?" Yes, uh in it's a workflow.

[00:01:39]No, it's an agent.