Agent readiness is not a vibe
Deepanshu PalA store does not feel agent-ready. It scores agent-ready. How a scorecard turns agent readiness into checks that can be repeated and evidence that can be inspected.
A feeling is not a measurement
Ask a store whether it is ready for AI shopping agents and the answer is usually a feeling. The site looks clean. The checkout seems simple. Nothing has complained. None of that tells you whether a machine customer can finish a purchase.
What a scorecard contains
A store does not feel agent-ready. It scores agent-ready. A scorecard has three parts: a score out of 100, a letter grade, and a list of checks that shows what passed, what failed and why.
Each check is a task a machine customer must be able to complete, from reading a price to finishing a checkout.
- Read a pricePassedTrace of the read
- Choose a variantFailedStep and page state where it stopped
- Keep a cartPassedTrace of the cart
- Finish checkoutFailedStep and page state where it stopped
Two rules that keep it honest
First, every check is reproducible. Another run, on the same store under the same conditions, gives the same result within tolerance. A check that cannot be repeated is not a measurement.
Second, every failure comes with evidence: the trace, the step, and the state the agent could not get past. A store owner should never have to take anyone's word for it.
What happens when a store changes
If a store fixes a failure, rerunning the same checks changes the score. If a rerun disagrees with the earlier result, find out why before trusting either one.
What is a sample
Any scorecard shown on this site that is not a real result is labeled as a sample. A sample shows the format, not a result about a real store.
A failure taxonomy for machine-mediated shopping.
Seven classes of failure a shopping agent can meet in a store. The taxonomy is versioned and open.
Read the taxonomy