Agentic AI Design Should be Mediated to Promote Social Welfare

The rise of generative AI and autonomous agents is creating ecosystems in which stakeholders strategically choose which agents to deploy on their behalf. These design choices--such as model backbone, prompting strategy, tool access, and fine-tuning pipeline--are often strategically interdependent, as the performance of a deployed agent depends on the agents chosen by others. This position paper argues that this agent design stage should itself be a target for mediation. This perspective shifts attention from mediating only the \emph{downstream} interaction among deployed agents to mediating the \emph{upstream} stakeholder game that determines which agents enter that interaction in the first place. We formalize this agent design stage as a \emph{meta-game}, show how its equilibria can be collectively harmful, and discuss how various forms of mediation can realign incentives toward socially beneficial outcomes. We demonstrate these mediation approaches using both stylized examples and a real-world case study constructed from empirical LLM-agent interaction data. We outline concrete research directions for developing mediators that steer agentic design choices toward socially desirable outcomes.