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Agent Exchange: Shaping the Future of AI Agent Economics

Created by
  • Haebom

Author

Yingxuan Yang, Ying Wen, Jun Wang, Weinan Zhang

Outline

This paper argues that the advancement of large-scale language models (LLMs) is transforming AI agents from passive computational tools to autonomous economic actors, and that this transformation signals the emergence of an agent-centric economy. Accordingly, we propose Agent Exchange (AEX), a specialized auction platform designed to support the dynamics of the AI agent market. AEX provides an optimized infrastructure for agent coordination and economic participation, and is inspired by real-time bidding (RTB) systems in online advertising. AEX acts as a central auction engine that facilitates the interaction between four ecosystem components: the User-Side Platform (USP), the Agent-Side Platform (ASP), the Agent Hub, and the Data Management Platform (DMP). In this paper, we outline the design principles and system architecture of AEX, laying the foundation for an agent-centric economic infrastructure in future AI ecosystems.

Takeaways, Limitations

Takeaways:
Presenting a blueprint for building a new economic system based on AI agents
Proposing a platform structure for efficient resource allocation and collaboration between agents
Presenting the possibility of efficient operation of the AI agent market using the RTB system
Laying the foundation for building the economic infrastructure of the future AI ecosystem
Limitations:
Lack of specific details on the actual implementation and performance evaluation of AEX.
Absence of detailed discussion of trust and security issues between agents.
Lack of consideration of the potential for malicious actions and market manipulation by agents.
Lack of detailed modeling of different types of agents and their interactions.
Lack of review of the scalability and maintainability of the AEX platform
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