Enterprise AI Readiness Report
AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions
This analysis explores AgenticPay, a benchmark for evaluating LLM-based agents in language-mediated buyer-seller negotiations. It highlights the potential for autonomous commerce and the current capabilities and limitations of state-of-the-art models.
Executive Impact Snapshot
AgenticPay reveals critical insights into LLM negotiation performance, identifying key areas for enterprise AI adoption.
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Top GlobalScore (Claude Opus 4.5)
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Negotiation Tasks Covered
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Llama-3.1-8B Timeout Rate
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Buyer-Seller Score Gap
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Key Findings from AgenticPay Evaluation
Proprietary LLMs significantly outperform open-weight models in negotiation tasks, achieving higher deal rates and efficiency. Asymmetric performance between buyer and seller roles is noted, with sellers generally achieving better outcomes. Increased market liquidity (more buyers/sellers) improves overall negotiation outcomes. Financial asset negotiations reveal limitations in current LLMs regarding strategic reasoning and risk assessment. Personality traits of agents heavily influence negotiation results. The study highlights persistent challenges in long-horizon strategic reasoning and negotiation efficiency across all models.
86.9%
Highest GlobalScore achieved by Claude Opus 4.5 across all tasks.
| Feature | Proprietary LLMs | Open-Weight LLMs |
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| Timeout Rate |
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| Overflow Rate |
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AgenticPay Methodology Overview
AgenticPay formalizes buyer-seller negotiation as a language-mediated strategic interaction, supporting diverse market structures and offering principled evaluation metrics for deal feasibility, efficiency, and welfare. The system allows for robust benchmarking across various LLM policies.
Enterprise Process Flow
Buyer/Seller Agent States
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Product & Market Context
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Dialogue-Based Negotiation
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Action Parsing & Termination
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Evaluation Objectives
Calculate Your Potential ROI with Agentic AI
Understand the economic impact of deploying advanced LLM agents for negotiation in your enterprise. Tailor the parameters below to see estimated annual savings and reclaimed human hours.
Estimated Annual Savings
Estimated Annual Hours Reclaimed
Your AI Negotiation Implementation Roadmap
A structured approach to integrating AgenticPay's insights into your operations, ensuring a smooth transition and maximum benefit.
Discovery & Strategy Alignment
Assess current negotiation processes, identify key pain points, and define strategic objectives for AI integration. This phase includes understanding specific market dynamics and agent roles within your enterprise.
Pilot Program & Customization
Implement AgenticPay in a controlled pilot environment. Customize LLM agent policies, negotiation parameters, and integration points with existing systems based on your unique business scenarios.
Performance Monitoring & Optimization
Launch broader deployment, continuously monitor negotiation outcomes, and refine agent strategies using AgenticPay's metrics. Iteratively improve efficiency and welfare across diverse market interactions.
Ready to Revolutionize Your Negotiations?
Leverage the power of autonomous AI agents to enhance efficiency, achieve better outcomes, and scale your negotiation capabilities. Our experts are ready to guide you.
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