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

arXiv: https://arxiv.org/abs/2506.00073 · HTML: https://mlsanity.com/paper/2506.00073/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 29 May 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 17 of 17)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks](https://mlsanity.com/paper/2412.14161/) (2024)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method](https://mlsanity.com/paper/2402.17193/) (2024)
- [The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey](https://mlsanity.com/paper/2404.11584/) (2024)
- [Decoupling Strategy and Generation in Negotiation Dialogues](https://mlsanity.com/paper/1808.09637/) (2018)
- [How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis](https://mlsanity.com/paper/2402.05863/) (2024)
- [Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation](https://mlsanity.com/paper/2309.17234/) (2023)
- [Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements](https://mlsanity.com/paper/2502.12904/) (2025)
- [ChatShop: Interactive Information Seeking with Language Agents](https://mlsanity.com/paper/2404.09911/) (2024)
- [Negotiating with LLMS: Prompt Hacks, Skill Gaps, and Reasoning Deficits](https://mlsanity.com/paper/2312.03720/) (2023)
- [Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning](https://mlsanity.com/paper/2505.16557/) (2025)
- [ACE: A LLM-based Negotiation Coaching System](https://mlsanity.com/paper/2410.01555/) (2024)
- [Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer](https://mlsanity.com/paper/1508.05328/) (2015)

## Built on by (most cited first, 15 of 15)

- [Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards](https://mlsanity.com/paper/2604.09855/) (2026)
- [Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach](https://mlsanity.com/paper/2507.20796/) (2025)
- [The Price of Thought: A Multilingual Analysis of Reasoning, Performance, and Cost of Negotiation in Large Language Models](https://mlsanity.com/paper/2510.08098/) (2025)
- [Emergent Risks in Generative Multi-Agent Systems](https://mlsanity.com/paper/2603.27771/) (2026)
- [Don't Trust Stubborn Neighbors: A Security Framework for Agentic Networks](https://mlsanity.com/paper/2603.15809/) (2026)
- [Auditing Preferences for Brands and Cultures in LLMs](https://mlsanity.com/paper/2603.18300/) (2026)
- [Agentic Inequality](https://mlsanity.com/paper/2510.16853/) (2025)
- [Counterparty Modeling is Not Strategy: The Limits of LLM Negotiators](https://mlsanity.com/paper/2605.16575/) (2026)
- [Simulating Human Memory with Language Models](https://mlsanity.com/paper/2605.25680/) (2026)
- [Reasonably reasoning AI agents can avoid game-theoretic failures in zero-shot, provably](https://mlsanity.com/paper/2603.18563/) (2026)
- [SovereignNegotiation-Bench: Evaluating User-Owned Personal Agents In Delegated Bargaining Under Privacy, Consent, Evidence, And Institutional Pressure](https://mlsanity.com/paper/2607.02814/) (2026)
- [The Agentic Web Requires New Normative Infrastructure](https://mlsanity.com/paper/2606.10711/) (2026)
- [From Passive Delegates to Strategic Negotiators: Reinforcing Social Reasoning in Small Language Models with SocialRL](https://mlsanity.com/paper/2608.13787/) (2026)
- [Parallelism or Concession? Concurrency-Aware Procurement Negotiation for Agentic Commerce](https://mlsanity.com/paper/2610.06017/) (2026)
- [BazaarBench: Delegation Safety in Decentralized C2C Marketplaces Run by LLM Agents](https://mlsanity.com/paper/2610.06748/) (2026)

## Abstract (arXiv)

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.