AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions
Xianyang Liu, Shangding Gu, Dawn Song

TL;DR
AgenticPay introduces a comprehensive multi-agent negotiation benchmark using natural language, enabling evaluation of LLMs in complex economic interactions with diverse tasks and realistic market scenarios.
Contribution
This work presents a novel benchmark and simulation framework for multi-agent negotiation with natural language, covering diverse market tasks and providing structured evaluation metrics.
Findings
State-of-the-art LLMs show significant gaps in negotiation performance.
Long-horizon strategic reasoning remains a key challenge.
AgenticPay enables studying language-based market interactions.
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…
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · Auction Theory and Applications · Language and cultural evolution
