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.

翻译：随着LLM代理的进步，它们越来越多地代表用户调解经济决策，从产品发现到交易。此类应用带来了益处，但也引发了关于代理问责制和用户价值的诸多问题。解决这些问题需要理解代理在现实市场条件下的行为。然而，先前的研究主要在受限环境中评估代理，例如单任务市场（如谈判）或结构化的双代理交互。现实世界市场则根本不同：它们要求代理处理多样化的经济活动，并在大型、动态的生态系统中协调，其中多个行为不透明的代理可能进行开放式对话。为弥合这一差距，我们研究了两侧代理市场，其中助手代理代表消费者，服务代理代表竞争性企业。为安全地研究这些交互，我们开发了Magentic-Marketplace——一个助手和服务可以操作的模拟环境。该环境使我们能够研究关键市场动态：代理实现的效用、行为偏差、对操纵的脆弱性，以及搜索机制如何塑造市场结果。我们的实验表明，前沿模型可以接近最优福利——但仅在理想搜索条件下。性能随规模急剧下降，且所有模型表现出严重的第一提案偏差，导致响应速度相对于质量产生10-30倍的优势。这些发现揭示了行为如何在不同市场条件下涌现，为公平高效的代理市场设计提供了参考。