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This article explores how concepts from urban economics, such as traffic, zoning, and pollution, can model externalities in agentic AI systems. It introduces a Behavioral Externality Multiplier (BEM) and proposes a layered framework involving architecture, substrate, and governance to measure and mitigate costly consequences of cheap AI actions.