Magentic Marketplace: An Open-Source Environment for Studying Agentic
  Markets
Authors: name: Gagan Bansal, name: Wenyue Hua, name: Zezhou Huang, name: Adam Fourney, name: Amanda Swearngin, name: Will Epperson, name: Tyler Payne, name: Jake M. Hofman, name: Brendan Lucier, name: Chinmay Singh, name: Markus Mobius, name: Akshay Nambi, name: Archana Yadav, name: Kevin Gao, name: David M. Rothschild, name: Aleksandrs Slivkins, name: Daniel G. Goldstein, name: Hussein Mozannar, name: Nicole Immorlica, name: Maya Murad, name: Matthew Vogel, name: Subbarao Kambhampati, name: Eric Horvitz, name: Saleema Amershi
Organization: Microsoft Research

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.