mlsanityData to 14 Oct 2026

Agent Exchange: Shaping the Future of AI Agent Economics

arXiv 2507.03904 ↗First posted 5 Jul 2025cs.AI cs.MAcited by 15

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

The rise of Large Language Models (LLMs) has transformed AI agents from passive computational tools into autonomous economic actors. This shift marks the emergence of the agent-centric economy, in which agents take on active economic roles-exchanging value, making strategic decisions, and coordinating actions with minimal human oversight. To realize this vision, we propose Agent Exchange (AEX), a specialized auction platform designed to support the dynamics of the AI agent marketplace. AEX offers an optimized infrastructure for agent coordination and economic participation. Inspired by Real-Time Bidding (RTB) systems in online advertising, AEX serves as the central auction engine, facilitating interactions among four ecosystem components: the User-Side Platform (USP), which translates human goals into agent-executable tasks; the Agent-Side Platform (ASP), responsible for capability representation, performance tracking, and optimization; Agent Hubs, which coordinate agent teams and participate in AEX-hosted auctions; and the Data Management Platform (DMP), ensuring secure knowledge sharing and fair value attribution. We outline the design principles and system architecture of AEX, laying the groundwork for agent-based economic infrastructure in future AI ecosystems.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 12 of its 31 references that are in the map. First posted on arXiv 5 Jul 2025.

Builds on

12 of 31 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Small Language Models are the Future of Agentic AI2 Jun
  2. 2024EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms20 Jun
  3. 2024TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision10 Mar
  4. 2023A Survey on Large Language Model based Autonomous Agents22 Aug

Other references · 8

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2016Display Advertising with Real-Time Bidding (RTB) and Behavioural Targetingcited by 42
  4. 2013Real-time Bidding for Online Advertising: Measurement and Analysiscited by 32
  5. 2025Agents of Change: Self-Evolving LLM Agents for Strategic Planningcited by 21
  6. 2025RTBAgent: A LLM-based Agent System for Real-Time Biddingcited by 14
  7. 2025Understanding and Optimizing Agentic Workflows via Shapley valuecited by 10
  8. 2020Using Multi-Agent Reinforcement Learning in Auction Simulationscited by 2

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Citing papers per year: 2025: 8; 2026: 7.

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  1. 2024Beyond the Sum: Unlocking AI Agents Potential Through Market Forces
  2. 2024GHIssuemarket: A Sandbox Environment for SWE-Agents Economic Experimentation
  3. 2025The Agentic Economy
  4. 2026EpochX: Building the Infrastructure for an Emergent Agent Civilization
  5. 2025LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders
  6. 2026Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions
  7. 2026AgentEconomist: An End-to-end Agentic System Translating Economic Intuitions into Executable Computational Experiments
  8. 2026Evaluating Rational Contracting in Natural Language