11/09/2026
We’ve spent the last two quarters thinking about a simple question:
What does an AI agent actually need to understand to make a useful commerce recommendation?
Take running shoes.
A product catalog can tell an agent the size, colour, material and price.
But a good recommendation may also depend on where someone runs, how far they run, their preferred fit, what they’ve bought before, what’s in stock, what offers apply and whether the shoes can arrive in time for their next race.
That information rarely lives in one place.
It sits across product data, customer systems, inventory, offers, content, policies and the conversation happening with the shopper.
The challenge isn’t simply connecting all this data. It’s helping an agent understand how the pieces relate to each other.
That’s what we mean by a Commerce Context Layer at Yarnit: the layer that gives AI agents the context they need to make commerce useful, not just conversational.
Because the quality of an AI shopping experience depends on more than the agent itself.
It depends on what the agent can understand.
We’ll be exploring this at Shoptalk Fall in Nashville. If you’re building for agentic commerce, let’s compare notes.
