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

• URL: http://arxiv.org/abs/2602.06008v1
• Date: Thu, 05 Feb 2026 18:50:36 GMT
• Title: AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions
• Authors: Xianyang Liu, Shangding Gu, Dawn Song,
• Abstract summary: Large language model (LLM)-based agents are increasingly expected to negotiate, coordinate, and transact autonomously.<n>We introduce AgenticPay, a benchmark and simulation framework for multi-agent buyer-seller negotiation driven by natural language.
• Score: 49.49718899185783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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