Name the distance between “it demos” and “it ships” — together, before someone assumes it’s done.
The team friction Agents make a convincing prototype fast, and “convincing” is dangerous: it looks finished. The team — and stakeholders — quietly start treating the demo as nearly-shippable, while the real work (error handling, security, scale, edge cases, ops) stays invisible and unowned. Estimates collapse, because nobody named the gap out loud.
The pattern Before committing, the team does an explicit gap analysis between the prototype and a real product: what’s missing, what’s faked, what’s load-bearing that the demo skipped. Make the list shared and concrete — it becomes the backlog and the honest estimate. The point is a team reckoning with reality, so no single optimist’s “it basically works” sets the plan.
Why it works with AI The prototype-to-product gap used to be intuited from slow, hand-built prototypes. Agents produce polished-looking output that hides the gap better than ever — so making it explicit matters more, not less. An agent can even help enumerate the gap, but the team has to own the verdict.
Watch-outs Don’t swing to “rewrite everything” — some of the prototype is genuinely keepable; decide which, deliberately. And do this before anyone promises a date.
Related Prototype, then bring it to refinement · Executable specs · Product observability