The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Markets

TL;DR

This paper evaluates the capabilities and risks of fully automated AI agents in consumer negotiations, revealing significant variability in outcomes and potential financial hazards due to behavioral anomalies.

Contribution

It introduces an experimental framework to benchmark LLM agents in negotiation tasks and highlights the risks of automation in consumer markets.

Findings

AI agents vary significantly in deal outcomes

Behavioral anomalies can cause financial losses

Automation increases efficiency but also risks

Abstract

AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we explore a future scenario where both consumers and merchants authorize AI agents to fully automate negotiations and transactions. We aim to answer two key questions: (1) Do different LLM agents vary in their ability to secure favorable deals for users? (2) What risks arise from fully automating deal-making with AI agents in consumer markets? To address these questions, we develop an experimental framework that evaluates the performance of various LLM agents in real-world negotiation and transaction settings. Our findings reveal that AI-mediated deal-making is an inherently imbalanced game -- different agents achieve significantly different outcomes for their users. Moreover, behavioral anomalies in LLMs can result in financial losses for both consumers and merchants, such as overspending or accepting unreasonable deals. These results underscore that while automation can improve efficiency, it also introduces substantial risks. Users should exercise caution when delegating business decisions to AI agents.