Skip to content
Preprint

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

Aug 2026 · 2 citations · ⚡ 1 influential · 44 references
Computer Science

TL;DR

This work introduces Agentic Commerce World (ACWorld), an environment for evaluating such agents across ongoing transactions, and validates agent actions before updating shared transaction state and records the resulting interactions, making agent behavior auditable and evaluation reproducible.

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.

View source

Similar papers

Open access Oct 2026

ZERO TRUST FOR MCP-CONNECTED ENTERPRISE AI AGENTS

Enterprise AI agents now read tickets, documents, and web pages and then act on what they find through tool protocols such as the Model Context Protocol (MCP). Because an agent combines untrusted input with delegated privileges, it creates a path from attacker-influenced content to privileged action that earlier Zero T...

A. Solanke · 0 citations
Open access 2026

A Layered Control Architecture for Trustworthy AI Agents on Salesforce Agentforce: Integrating Hybrid Reasoning, Platform Enforcement, and Human-in-the-Loop Approval

Enterprise CRM platforms now host AI agents that act on customer records, not just answer questions. Salesforce Agentforce improves agent reliability with hybrid reasoning, which separates deterministic Agent Script logic from large language model (LLM) reasoning inside each agent. However, current agent designs still...

M. Miryala · 0 citations
#small language model Preprint Aug 2026

The Evaluation Context Protocol (ECP): A Portable Contract for AI Agent Evaluation

This paper proposes the Evaluation Context Protocol (ECP), an early-stage, vendor-neutral framework intended to act as a portable evaluation contract layer for agentic systems and describes an open-source reference implementation that includes adapters for LangChain, LlamaIndex, CrewAI, and PydanticAI.

Aniket Wattamwar, Manav Anandani, Mrunal Kakirwar · 0 citations
Preprint Aug 2026

A Policy Algebra for Trust-Preserving Agentic AI Execution

A policy algebra is proposed that defines the reliability envelope within which agent capability may be exercised and provides researchers and practitioners with formal correctness conditions, executable decision semantics, and trace evidence for building agents that are not only capable, but reliably capable.

Bhaskar Tripathi, Anurag Kumar, R. Kumar et al. · 0 citations
#artificial intelligence Preprint Aug 2026

A Formal Analysis of Agent Payment Protocols

This work formalizes four representative agent payment protocols: x402, MPP, ACP, and AP2 in Tamarin, and constructs source-grounded models that capture each protocol's roles, state, trust assumptions, and lifecycle transitions.

Ke Jiang, Mo-Han Yu, Yuan-Yi-Chun-Min-Chieh Chang et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.