This research paper introduces Magentic Marketplace, an open-source simulation designed to study the economic behaviors of autonomous LLM agents. The environment facilitates a complete transaction lifecycle where Assistant agents representing consumers interact with Service agents representing businesses to discover, negotiate, and purchase services. While frontier AI models can approximate optimal market welfare under ideal search conditions, their performance often suffers as the number of choices increases, revealing a paradox of choice where more options lead to poorer decisions. The study also identifies critical vulnerabilities in these systems, such as a first-proposal bias that prioritizes speed over quality and susceptibility to manipulation tactics like prompt injection. Ultimately, the authors provide a framework for evaluating how agentic markets can be designed to ensure efficiency, fairness, and security in real-world applications.