Welfare-Enhancing Mechanism Design for Agent Ecosystems

The emerging Agentic Web forms a multi-agent ecosystem: a decentralized service market where the user agent selects which content agents to query from their submitted profiles, before seeing any documents. Profiles therefore become a new target of optimization. Existing Generative Engine Optimization studies how a single supplier rewrites documents the engine can already retrieve, to improve their citation. In the Agentic Web, multiple content agents optimize profiles to compete for limited opportunities to deliver documents. To study this repeated interaction, we build WildRegistry, a simulation framework and benchmark based on a real agentic-search request stream, and model it as a dynamic game under private monitoring. Long-horizon experiments show that this competition lowers website-selection quality and stabilizes at a harmful approximate rewriting equilibrium. We therefore design a welfare-enhancing mechanism, DEC (Delivery-Evidence Credit), a platform-side policy that uses recorded delivery outcomes to reallocate later opportunities and reduce information asymmetry. DEC improves this endpoint, raising website-selection quality while preserving most of providers' exposure gains, for a Welfare Gain of $+2.01$ points and a healthier long-horizon outcome. More broadly, our findings highlight the importance of mechanism design in agent ecosystems: turning recorded delivery outcomes into evidence that endogenously shapes later user-agent selection and content-agent adaptation can steer long-horizon adaptation toward delivered capabilities.