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This research introduces ACWorld, a benchmarking environment that evaluates AI agents performing natural language-driven buying and selling tasks within an auditable market protocol. By validating agent actions before state updates, the study demonstrates how process-level evidence is critical for assessing reliability in complex multi-agent commerce scenarios involving hundreds of thousands of listings. The results highlight significant performance variations across models while exposing specific bottlenecks in large-catalog search capabilities that require further optimization.
Surse care acoperăarXiv, LessWrong, SiliconANGLE, Latent Space
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Agentic Commerce World: An Auditable and Verifiable Environment for Vibe Commerce

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.02441v1 Announce Type: new 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.

Authors:Shicheng Fan, Mingdai Yang, Duohao Wang, Canyu Chen, Yongfeng Zhang, Hua Wei, Manling Li, Julian McAuley, Kun Zhang, Philip S. Yu, Kejing Yu, Zhiwei Liu

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.

Artificial Intelligence (cs.AI)

arXiv:2608.02441 [cs.AI]

(or arXiv:2608.02441v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.02441 arXiv-issued DOI via DataCite (pending registration)

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