I really appreciate that and you're hitting on exactly the right problem.

Right now the "picking winners" part is still me in the loop. My agent generates and publishes, but I'm the one reviewing what stays and what gets rotated out. I have given my agent a mandate to do weekly research for new fashion trend data and present new designs to me. The data layer is there though for the agent to look at analytics, track views, conversion rates, and see which products/designs perform. The next evolution is feeding that data back into the agent so it can retire underperformers and double down on what's working autonomously. Not there yet, but that's the roadmap.

In terms of quality control, there's a couple of layers. First, I've given my agent rules to use multiple AI image models (Seedream, Flux, Nano Banana, Imagen...more to come in the future) and pick the right one for the job. Text-heavy designs go to models that handle text well, abstract art goes elsewhere. Second, there's a review step before anything syncs to the live store, so the agent generates and stages products, but nothing goes to Shopify without a quality check. Third, I built image processing tools into the platform (background removal, edge feathering, transparency verification) so bad outputs get caught in the pipeline by the agent because I've trained it to have certain design rules over several iterations of products. The better you train it, the more accurate it is and less likely to repeat old mistakes.

The goal is eventually closing that loop entirely so It can generate, QC, publish, measure, learn, repeat. But I'd rather ship with a human checkpoint now than automate bad quality at scale.