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arXiv · 2602.06008

AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions

Xianyang Liu·Shangding Gu·Dawn Song
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Abstract

Large language model (LLM)-based agents are increasingly expected to negotiate, coordinate, and transact autonomously, yet existing benchmarks lack principled settings for evaluating language-mediated economic interaction among multiple agents. We introduce AgenticPay, a benchmark and simulation framework for multi-agent buyer-seller negotiation driven by natural language. AgenticPay models markets in which buyers and sellers possess private constraints and product-dependent valuations, and must reach agreements through multi-round linguistic negotiation rather than numeric bidding alone. The framework supports a diverse suite of over 110 tasks ranging from bilateral bargaining to many-to-many markets, with structured action extraction and metrics for feasibility, efficiency, and welfare. Benchmarking state-of-the-art proprietary and open-weight LLMs reveals substantial gaps in negotiation performance and highlights challenges in long-horizon strategic reasoning, establishing AgenticPay as a foundation for studying agentic commerce and language-based market interaction. Code and dataset are available at the link: https://github.com/SafeRL-Lab/AgenticPay.

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BibTeXRIS

Xianyang Liu, Shangding Gu, Dawn Song. 2026-02-05. AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions. https://arxiv.org/abs/2602.06008

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