AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions
By: Xianyang Liu, Shangding Gu, Dawn Song
Published: 2026-02-06
View on arXiv →Abstract
AgenticPay proposes a multi-agent large language model (LLM) negotiation system designed for buyer-seller transactions. This system could revolutionize e-commerce by automating and optimizing negotiation processes, leading to more efficient and personalized transactions.
Impact
transformative
Topics
5
💡 Simple Explanation
Imagine if you had a smart digital assistant that could talk to a store's digital assistant to haggle for a better price and then buy the item for you automatically. AgenticPay is a system that builds these assistants and connects them to a payment system so they can close the deal without you needing to click buttons.
🎯 Problem Statement
Online transactions are currently static (fixed prices) or require high human effort to negotiate. Current automated systems lack the flexibility to handle complex, multi-variable bargaining effectively.
🔬 Methodology
The authors utilize a multi-agent simulation environment where LLMs (GPT-4 based) are assigned 'Buyer' and 'Seller' roles with private valuation constraints. They employ a structured dialogue protocol combined with a deterministic payment trigger that executes only when both agents output a specific 'ACCEPT' token sequence matching agreed terms.
📊 Results
The system demonstrated a 15% increase in total social welfare (combined buyer/seller utility) compared to fixed-price baselines and reduced negotiation time by 40% compared to human chat benchmarks. AgenticPay agents successfully executed payments in 92% of converging scenarios without hallucinating bank details.
✨ Key Takeaways
Autonomous economic agents are feasible and can optimize market efficiency. The key innovation is tightly coupling the conversational output of the LLM to a secure payment execution layer, turning talk into action.
🔍 Critical Analysis
AgenticPay presents a compelling vision of the future of e-commerce but glosses over significant trust and security hurdles. While the technical negotiation capability of LLMs is well-documented, the 'bridge' to actual payment is fraught with risk. The paper assumes a level of robustness in LLMs that currently does not exist regarding adversarial attacks. However, as a framework for *assisted* negotiation (Human-in-the-loop), it is highly promising.
💰 Practical Applications
- Transaction fee (small %) on every successful automated negotiation.
- Subscription model for 'Pro' agents with better negotiation tactics.
- Enterprise licensing for B2B supply chain platforms.