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Agentic Commerce World: An Auditable and Verifiable Environment for Vibe Commerce

Authors

Do you know Shicheng Fan?You can claim authorship or link another user.Do you know Mingdai Yang?You can claim authorship or link another user.Do you know Duohao Wang?You can claim authorship or link another user.Do you know Canyu Chen?You can claim authorship or link another user.Do you know Yongfeng Zhang?You can claim authorship or link another user.Do you know Hua Wei?You can claim authorship or link another user.Do you know Manling Li?You can claim authorship or link another user.Do you know Julian McAuley?You can claim authorship or link another user.Do you know Kun Zhang?You can claim authorship or link another user.Do you know Philip S. Yu?You can claim authorship or link another user.Do you know Kejing Yu?You can claim authorship or link another user.Do you know Zhiwei Liu?You can claim authorship or link another user.

Abstract

In vibe coding, people describe software in natural language and delegate implementation to AI agents. By analogy, vibe commerce allows people to express buying or selling goals in natural language and delegate the corresponding tasks to agents. Commerce, however, requires independently controlled Buyer and Merchant agents to interact in a shared market while preserving their private objectives and distinct authority. We introduce Agentic Commerce World (ACWorld), an environment for evaluating such agents across ongoing transactions. Through its Vibe Commerce Protocol (VCP), ACWorld validates agent actions before updating shared transaction state and records the resulting interactions, making agent behavior auditable and evaluation reproducible. The ACWorld Benchmark contains a 200-task capability-coverage track and a 60-task large-catalog track that searches 785,022 transactable listings. Across ten models, mean scores range from 65.9% to 85.6% and from 56.1% to 91.4%, respectively. Our analysis shows that process-level evidence is necessary: final state alone can miss evaluated errors, incomplete trajectories still retain useful process signals, and large-catalog tasks expose bottlenecks across stages.

Community

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