title: The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and
    Transactions in Consumer Markets
summary: 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.
paper_authors._id: 683e3eb44f5a8c2890e122bf
paper_authors.name: Shenzhe Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:46:50.385Z
paper_authors._id: 6614243f67d7bfc73afc6b77
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6614243f67d7bfc73afc6b77/ifdjAb_BPraEJ2eIySk_K.jpeg
paper_authors.fullname: Shenzhe Zhu
paper_authors.type: user
paper_authors.user: Chouoftears
paper_authors._id: 683e3eb44f5a8c2890e122c0
paper_authors.name: Jiao Sun
paper_authors._id: 683e3eb44f5a8c2890e122c1
paper_authors.name: Yi Nian
paper_authors._id: 683e3eb44f5a8c2890e122c2
paper_authors.name: Tobin South
paper_authors._id: 683e3eb44f5a8c2890e122c3
paper_authors.name: Alex Pentland
paper_authors._id: 683e3eb44f5a8c2890e122c4
paper_authors.name: Jiaxin Pei
paper_title: The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and
    Transactions in Consumer Markets
paper_summary: 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.
paper_ai_summary: LLM agents exhibit varying performance and behavioral anomalies in automated negotiations, highlighting both potential benefits and risks in consumer markets.
paper_ai_keywords: LLM AI agents negotiations transactions favorable deals automation behavioral anomalies financial losses