Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets

Gagan Bansal, Wenyue Hua, Zezhou Huang, Adam Fourney, Amanda Swearngin, Chinmay Singh, Brendan Lucier, Jake M. Hofman, Markus Mobius, Tyler Payne, Will Epperson, Akshay Nambi, Archana Yadav, Kevin Gao, Maya Murad, Matthew Vogel, Aleksandrs Slivkins, Daniel G. Goldstein, David Rothschild, Hussein Mozannar et al. (4 additional authors not shown)

Keywords: agents, LLMs, communication, delegation, asymmetric information

TL;DR: The Magentic-Marketplace provides a controlled environment to study complex two-sided marketplaces where AI agents discover each other and communicate to transact and negotiate good and services.

Abstract:

As LLM agents advance, they are increasingly mediating economic decisions, ranging from product discovery to transactions, on behalf of users.
Such applications promise benefits but also raise many questions about agent accountability and value for users.
Addressing these questions requires understanding how agents behave in realistic market conditions.
However, previous research has largely evaluated agents in constrained settings, such as single-task marketplaces (e.g., negotiation) or structured two-agent interactions.
Real-world markets are fundamentally different: they require agents to handle diverse economic activities and coordinate within large, dynamic ecosystems where multiple agents with opaque behaviors may engage in open-ended dialogues.
To bridge this gap, we investigate two-sided agentic marketplaces where Assistant agents represent consumers and Service agents represent competing businesses.
To study these interactions safely, we develop Magentic Marketplace -- a simulated environment where Assistants and Services can operate.
This environment enables us to study key market dynamics: the utility agents achieve, behavioral biases, vulnerability to manipulation, and how search mechanisms shape market outcomes.
Our experiments show that frontier models can approach optimal welfare—but only under ideal search conditions. Performance degrades sharply with scale, and all models exhibit severe first-proposal bias, creating 10-30x advantages for response speed over quality.
These findings reveal how behaviors emerge across market conditions, informing the design of fair and efficient agentic marketplaces.

Primary Area: datasets and benchmarks

Submission Number: 23939