What actually separates a workflow from a real AI system?

Hey everyone 👋

As I’ve been going through discussions here, I’m starting to notice a pattern:

There’s a big difference between:

- simple workflows

- and actual AI systems

At first, I was mostly building workflows (input → LLM → output)

But now it feels like real systems involve:

- structure (context, modular parts)

- memory (logs, learnings)

- feedback loops (improving over time)

- multiple roles instead of a single call

So instead of just calling an LLM, you're designing how the system thinks and evolves.

Still learning, but this shift in thinking feels important.

Curious — what do you think is the biggest difference between a workflow and a real AI system?

3 comments

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Jake Van Clief, giving you the Cliff notes on the new AI age.

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