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.

翻译：人工智能智能体正越来越多地代表用户进行交易——委托任务、支配预算，并与陌生的对手方谈判。从技能市场到纯智能体集市，这些新兴平台的经济基础设施正在临时构建，但早期设计选择往往会锁定路径；理解它们所产生的动态亟需解决。我们提出\diagon系统，这是一个可编程市场系统，旨在为近未来智能体认知劳动市场的制度设计提供参考。\diagon由使用工具的异构智能体组成，使得从职位发布、投标、谈判、执行、支付到声誉积累的完整周期端到端可观测且可实验操控。我们实例化一种市场形式以演示\diagon。我们发现，市场交换产生的财富是自给自足智能体的3.2倍，但这些收益强烈依赖于制度结构；例如，身份透明度和更强的竞争选择等干预措施反而可能降低市场绩效。这些发现突出了智能体时代经济基础设施的具体设计要求。代码和数据见https://github.com/assassin808/diagon。