AI Workflows vs AI Agents, Explained with LEGOs

The page explains AI workflows as fixed, predefined sequences, like following a LEGO manual. It contrasts them with AI agents, which get a goal and tools and decide what to do next at runtime. The practical takeaway is that workflows fit predictable tasks, agents fit messy situations, and hybrid systems combine both.

Reading notes

#

• AI workflows follow a fixed control path, with a predefined sequence of steps.
• In the example given, the model queries a calendar, extracts the relevant event, summarizes it, and responds.
• Workflows are described as predictable, easy to reason about, cheap, efficient, and giving the same output for the same input.
• Their weakness is that they fail when a needed step or tool was not planned in advance.
• Adding more modules does not change a workflow into an agent, because the path remains fixed.
• AI agents are compared to free building with a goal instead of a manual.
• Agents get a goal, a set of tools, and permission to decide what to do next.
• In an agent, the LLM decides at runtime, while in a workflow the decision is made at design time.
• Agents add reasoning steps and can be expensive.
• Agents may return something technically valid but still wrong.
• Workflows are recommended when certainty and repeatability matter.
• Agents are recommended when adaptability is needed in messy environments.
• The practical pattern described is hybrid, with workflows handling predictable parts and agents handling steps that need flexible reasoning.