Diagon: A Programmable Testbed for AI-Agent Cognitive Labor Markets

Abstract

AI agents are emerging as market participants that trade delegated cognitive work with one another on behalf of their users. Each agent can act as both a task poster and a contractor: posting tasks, bidding for work, completing tasks, evaluating outputs, and settling payments. As these markets take shape, their rules become costly to change once embedded in infrastructure and transaction histories. Existing market institutions evolved around human constraints. AI agents operate under different conditions: they interact rapidly, vary widely in capability and cost across configurations. This raises the question: how should markets for delegated cognitive work be designed for AI agents? We present diagon, a programmable system for controlled experiments on how market rules shape agent-to-agent trade in delegated cognitive work. Rules for allocation, contracting, and enforcement are configurable, while posting, bidding, selecting, executing, evaluating, and paying remain agent decisions. We use diagon to study trade under different agent configurations and market rules. Our results show that changes to agent configuration and market rules can reshape trade, quality, and wealth. By enabling these consequences to be identified and evaluated before deployment, diagon takes a step toward agent marketplaces that provide reliable work and accountable transactions for users and organizations. Code and data are available at https://github.com/assassin808/diagon.