Alhena AI vs Pictory

Alhena AI's feed is a research blog, not a changelog, but it is unusually structured for one: since late July it has run a single continuing study in which 15 live AI shopping agents are tested as ordinary shoppers on real storefronts. The findings are consistent and unflattering to the category - all 15 can answer questions, 9 can sell, 4 can complete a return or order change, and 1 remembers a shopper across sessions. Recent instalments break the results down by 11 verticals and by a specific task, foundation shade matching from a selfie, where five agents ignored the image entirely.

The blog is building a capability ladder - Answer, Recommend, Sell, Act, Remember - and using it to argue that architecture, not category difficulty, decides where an agent stops. That framing does competitive work: it defines the axis on which agents are compared, places memory and task completion at the top, and reports that almost nothing on the market reaches them. Nothing here describes Alhena's own product releases, so the feed shows the argument the company is making rather than what it is shipping.

The benchmark series looks set to continue with further vertical and task cuts against the same 15-agent panel. A refreshed run showing movement on the Act and Remember rungs would be the natural next instalment, though these entries do not say when it is due.