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This article introduces ShopGym, a framework that converts live e-commerce storefronts into reproducible sandboxes for training and evaluating shopping agents. By generating consistent tasks grounded in real product catalogs and policies, it addresses the challenge of maintaining stable evaluation environments amidst dynamic web conditions. The system validates its utility by demonstrating strong correlation between agent performance on synthetic shops and their actual behavior in live settings.
Sources coveringShopify Engineering Blog, arXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

ShopGym: Realistic, reproducible sandboxes for shopping agents

Shopify Engineering Blog6 min read

A shopping agent might help you find a jacket within your budget, choose the right size, check the return policy, and add it to your cart. That part feels easy, but training and evaluating these agents is challenging because products, prices, and store layouts keep changing. Bot detection and throttling can also interrupt runs, while hand-built test stores offer control but capture only a limited range of shopping experiences. This led us to ask: Can we make shopping environments repeatable without losing the complexity of real stores? We built ShopGym to answer that question. It turns live storefronts into self-contained, resettable sandbox shops and generates shopping tasks tied to their products,…

Excerpt shown under the publisher's RSS terms · 111 words · full text and figures are on the publisher's site

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