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

Xianyang Liu, Shangding Gu, Dawn Song

Keywords: Multi-Agent LLMs, Negotiation Systems, Buyer–Seller Transactions, Large Language Models

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 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.

Track: Long Paper

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Submission Number: 65