I don’t use plan.md docs either, but I recognise the underlying idea: you need a way to keep agent output constrained by reality.

My workflow is more like scaffold -> thin vertical slices -> machine-checkable semantics -> repeat.

Concrete example: I built and shipped a live ticketing system for my club (Kolibri Tickets). It’s not a toy: real payments (Stripe), email delivery, ticket verification at the door, frontend + backend, migrations, idempotency edges, etc. It’s running and taking money.

The reason this works with AI isn’t that the model “codes fast”. It’s that the workflow moves the bottleneck from “typing” to “verification”, and then engineers the verification loop:

  -keep the spine runnable early (end-to-end scaffold)

  -add one thin slice at a time (don’t let it touch 15 files speculatively)

  -force checkable artifacts (tests/fixtures/types/state-machine semantics where it matters)

  -treat refactors as normal, because the harness makes them safe

If you run it open-loop (prompt -> giant diff -> read/debug), you get the “illusion of velocity” people complain about. If you run it closed-loop (scaffold + constraints + verifiers), you can actually ship faster because you’re not paying the integration cost repeatedly.

Plan docs are one way to create shared state and prevent drift. A runnable scaffold + verification harness is another.