We're getting really good at adding AI to things.

Add an LLM here.
Add a copilot there.
Put an agent in the workflow.

Done. “AI-native.” 😅

But building around AI feels like a different problem.

Where does the right information come from?
Which model should handle the job?
What should an agent actually do?
And probably the biggest one — how do we know the result is good?

I've been looking at an idea from AiDOOS around this called RAMP — Retrieve, Agents, Models, Proof.

Still early, but I think there's something worth exploring here.

Maybe being AI-native is less about adding AI...

and more about rethinking how the whole system works.