Most shopping-agent demos stop after recommending a product. The difficult part in a real system comes next: Which item should it select? Should the order proceed or be reviewed? Which warehouse should fulfill it? Should a return be approved, escalated, or rejected? We built Commerce-1 to handle these bounded decisions. You give it the current state and a list of possible actions. It returns the selected action and a probability for every option, instead of generating prose that the application has to parse. Commerce-1 is a 27B open-weight decision model built for agentic commerce. It supports up to 255 candidate actions and generates zero output tokens. Try it yourself: Interactive demo: https://huggingface.co/spaces/infercrane/commerce-1-arcade Model weights: https://huggingface.co/infercrane/Commerce-1 Source code: https://github.com/infercrane/commerce-1 A few limitations: The arcade uses synthetic workflow replays. It doesn't execute real orders or move money. The current BF16 weights are approximately 54 GiB, and our qualified deployment uses a single NVIDIA H200. I'd love feedback from you.   submitted by   /u/yasintoy [link]   [comments]