The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and
  Transactions in Consumer Markets
Authors: name: Shenzhe Zhu, name: Jiao Sun, name: Yi Nian, name: Tobin South, name: Alex Pentland, name: Jiaxin Pei

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.