I disagree. Ask ChatGPT to recommend a product in your niche, then ask why it chose that product, then ask why it recommended that over your product. It'll give you a pretty clear answer. It likes structured content on the product page, it likes knowing exactly who this product is for, it likes knowing how it compares to other products in your lineup (good, better, best), it likes hard number descriptors over marketing copy ("holds 120lbs" rather than "extra strong"). There are plenty of real things you can do to your product pages to encourage AI agents to reference it when sourcing info. I've been making these updates over the last couple months and have seen a measurable uptick in ChatGPT traffic and sales. Shopify's Agentic features don't answer those questions, it's something you have to include yourself.

This is exactly what our scan data shows. The three things you mentioned — llms.txt, variant selectors, JSON-LD — are the right levers. In order of impact: 1. JSON-LD is the most critical. Without it, agents can't machine-read your price or availability. One brand we scanned ($50-75M revenue) had zero structured data — scored 26/100. Adding a single JSON-LD block would more than double their score. 2. Variant selectors are where most brands break. Custom JavaScript size/color pickers look great for humans but give agents nothing to interact with. Across 21 brands, the Add-to-Cart flow failed \~85% of the time — almost always because of non-semantic variant selectors. 3. llms.txt is the easiest win. Only a third of brands we scanned had one. It takes 15 minutes to write and gives agents context they'd otherwise have to guess at. The uncomfortable part: agents fail silently. No abandoned cart metric, no error log, no customer complaint. They just leave and try the next store. **Most brands don't know they're losing this traffic because there's no signal that it was ever there.** [github.com/.../agent-a](github.com/.../agent-a)