Position: AI-Agent Pricing Should Become More Outcome-Dependent: An Economic Perspective

Abstract: AI agents have advanced rapidly and are increasingly sold as services, yet dominant pricing models still reward observable usage rather than the outcomes users actually value. This position paper argues that AI-agent pricing should become more outcome-dependent as markets mature, especially in domains where success can be measured objectively. Our position is motivated by three factors. First, pricing is a risk-sharing mechanism: when users are more risk-averse than AI-agent providers, outcome-dependent contracts can improve welfare by reallocating risk more efficiently. Second, token-based pricing under-incentivizes hidden effort, such as verification, tool use, and model selection, thereby creating moral hazard. Third, outcome-dependent pricing is often difficult to implement because performance measures may be noisy, delayed, or strategically manipulated. Using stylized principal-agent models from economics, we show how these forces shape the case for outcome-dependent pricing and why hybrid contracts that combine usage-based charges with outcome-dependent terms are often more practical than either token-only or pure pay-for-performance schemes. Overall, we argue that moving beyond token-only pricing is necessary for allocating risk more efficiently, aligning incentives, and building more trustworthy AI-agent markets.