Magentic Marketplace: An Open-Source Environment for Studying Agentic
  Markets
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.
69042d902c556835fa67f0d9
Gagan Bansal
69042d902c556835fa67f0da
Wenyue Hua
69042d902c556835fa67f0db
Zezhou Huang
69042d902c556835fa67f0dc
Adam Fourney
69042d902c556835fa67f0dd
Amanda Swearngin
69042d902c556835fa67f0de
Will Epperson
69042d902c556835fa67f0df
Tyler Payne
claimed_verified
2025-11-03T20:54:47.245Z
64c5bd945cd9506edf494318
https://cdn-avatars.huggingface.co/v1/production/uploads/64c5bd945cd9506edf494318/hxYeAWnzn8n-hLv9ls43l.jpeg
Tyler Payne
tylerpayne
user
tylerpayne
69042d902c556835fa67f0e0
Jake M. Hofman
69042d902c556835fa67f0e1
Brendan Lucier
69042d902c556835fa67f0e2
Chinmay Singh
69042d902c556835fa67f0e3
Markus Mobius
69042d902c556835fa67f0e4
Akshay Nambi
claimed_verified
2025-11-27T10:15:32.501Z
64aba383fddf117e6e5ba818
/avatars/ee7d25d865b34be5902872d060ad9153.svg
Akshay  Nambi
akshaynambi
user
akshaynambi
69042d902c556835fa67f0e5
Archana Yadav
69042d902c556835fa67f0e6
Kevin Gao
claimed_verified
2025-10-31T15:23:17.457Z
67b189c9e11e644479e55296
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/2UIQNHv4f6zQBFtBGTmWV.png
Kevin Gao
kevinleogao
user
kevinleogao
69042d902c556835fa67f0e7
David M. Rothschild
69042d902c556835fa67f0e8
Aleksandrs Slivkins
69042d902c556835fa67f0e9
Daniel G. Goldstein
69042d902c556835fa67f0ea
Hussein Mozannar
claimed_verified
2025-12-05T08:30:31.197Z
63f62dfcb607296857b63e53
https://cdn-avatars.huggingface.co/v1/production/uploads/63f62dfcb607296857b63e53/NIICUK4It6JNI5qt5iqjq.jpeg
Hussein Mozannar
hsseinmz
user
hsseinmz
69042d902c556835fa67f0eb
Nicole Immorlica
69042d902c556835fa67f0ec
Maya Murad
69042d902c556835fa67f0ed
Matthew Vogel
69042d902c556835fa67f0ee
Subbarao Kambhampati
69042d902c556835fa67f0ef
Eric Horvitz
69042d902c556835fa67f0f0
Saleema Amershi
Magentic Marketplace: An Open-Source Environment for Studying Agentic
  Markets
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.
Research investigates the behavior of AI agents in simulated two-sided marketplaces to understand their performance, biases, and vulnerabilities in realistic economic conditions.
LLM agents agent accountability value for users single-task marketplaces structured two-agent interactions real-world markets diverse economic activities large dynamic ecosystems Assistant agents Service agents Magentic-Marketplace utility agents behavioral biases vulnerability to manipulation search mechanisms market outcomes optimal welfare first-proposal bias