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

ACL ARR 2026 January Submission91 Authors

Keywords: LLM Agent, Agent Negotiation, LLM for social good, Trustworthy LLM

Abstract: AI agents are increasingly used in consumer applications for product search, negotiation, and transactions. We investigate a setting where both consumers and merchants authorize AI agents to automate negotiations and transactions. We address two questions: (1) Do different LLM agents exhibit varying performance when making deals for users? (2) What are the risks when using AI agents to fully automate negotiations in consumer settings? We design an experimental framework to evaluate AI agents' capabilities in real-world negotiation scenarios, experimenting with various open-source and closed-source LLMs. Our analysis reveals that deal-making with LLM agents is an inherently imbalanced game. Furthermore, LLMs' behavioral anomalies might lead to financial losses for both consumers and merchants through overspending or unreasonable deals. While automation can enhance efficiency, it poses significant risks to consumer markets. Users should be cautious when delegating business decisions to LLM agents.

Paper Type: Long

Research Area: Computational Social Science, Cultural Analytics, and NLP for Social Good

Research Area Keywords: LLM Agent, Agent Negotiation, LLM for social good, Trustworthy LLM

Contribution Types: NLP engineering experiment, Data analysis

Languages Studied: English

Submission Number: 91