When Agent Markets Arrive

TL;DR

This paper introduces \\diagon, a programmable market system for AI agents, to study economic dynamics and design principles in emerging agent markets, revealing how institutional choices impact wealth and performance.

Contribution

The paper presents \\diagon, a novel experimental platform for analyzing agent market dynamics and institutional design effects in AI-driven economic systems.

Findings

Market exchange yields 3.2x the wealth of self-sufficient agents.

Institutional structures like transparency and competition can both improve or degrade market performance.

Experimental results inform design requirements for future agent economic infrastructure.

Abstract

AI agents are increasingly transacting on behalf of users -- delegating tasks, spending budgets, and negotiating with unfamiliar counterparties. From skill marketplaces to agent-only bazaars, the economic infrastructure of these emerging platforms is being built ad-hoc, yet early design choices tend to lock in; understanding what dynamics they produce is urgent. We present \diagon, a programmable market system designed to inform the institutional design of near-future agent cognitive-labour markets. \diagon is populated by heterogeneous tool-using agents, making the full cycle of job posting, bidding, negotiation, execution, payment, and reputation accumulation end-to-end observable and experimentally manipulable. We instantiate one market form to demonstrate \diagon. We find that market exchange generates \(3.2\times\) the wealth of self-sufficient agents, but these gains depend strongly on institutional structure; for example, interventions such as identity transparency and stronger competitive selection can degrade market performance rather than improve it. These findings highlight concrete design requirements for the economic infrastructure of the agent era. Code and data are available at https://github.com/assassin808/diagon.