Could you find a polo shirt in blue, in size L as a present for my brother? I need it in the next 8 days, so standard shipping works for me.
CartTrace-100k
101,416 trips to the store.
We sent an AI agent shopping, all the way to the till, and wrote down every step. Then we counted where it got lost.
An open dataset from General Machines, explored by Machine Commerce.
The shopper is a program. It keeps some of what it wants to itself.
Like a person, it doesn't say everything up front. It answers when the agent asks, and never makes a detail up.
1,044 stores that don't exist.
No agent may pay on a real store. So we screened 2,339 real ones, learned how each kind of shop sells, and built stand-ins that sell the same way. Their shelves come from open product catalogs. Prices are made up.
Each square is a stand-in store, coloured by its kind of shop.
One trip, from the request to the receipt.
Every search, every page read, every question, the cart, the checkout and the payment, through the protocols agents use to buy today. Test tokens only.
Send a hundred. Watch where they fall off.
Every loss happens before the till.
The till was never the problem.
On journeys that went on to pay, almost every failed check happened before the cart. Building it, checking out and paying: under one percent each, on every protocol.
Asking helps where the shopper held something back.
With two needs held back, the agent ended right 62% of the time when it asked and 12% when it didn't. Below: the same task compared with itself.
Sometimes it said nothing fits, when something did.
The shopper leaves. The store never learns why.
Ask the same thing again.
Of 24,549 tasks run three times or more, almost a third both succeeded and failed. Which store it was explains under 2% of the outcome. One test doesn't tell you much.
A shirt needs a size. A drill needs a voltage. A gift needs a guess.
Twenty-five kinds of shop, about four thousand trips each. The hardest and the easiest:
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