Turned an autonomous AI content pipeline from mostly-unusable output into a mostly-passing one, not by changing the model, but by encoding success criteria as machine-readable intent specs and putting a scored maker-and-judge gate in front of everything that shipped.
Context and stakes
Autonomous AI content generation usually fails the same way: it produces a lot, and most of it is not good enough to use. The interesting question is not "can the model write," it is "can you get trustworthy quality at volume without reading every output by hand."
Problem
Out of the box, most autonomous output was not usable. The failure was not the model, it was the absence of a measurable definition of "good": no encoded success criteria, no scoring, and no automatic rejection of weak output, so quality was a vibe and did not scale.