Cornell Researcher Proposes “Clearinghouse” Model for Building Trust Between AI Agents

This article examines the growing trust and coordination challenges emerging in autonomous multi-agent AI systems. Centered around the research of Ming-Chang Chiu, the piece introduces the concept of “semantic drift,” where AI agents silently develop divergent understandings while appearing operationally aligned. Drawing parallels to historical financial clearinghouses, the article argues that the next major challenge in agentic AI will not simply be smarter models, but institutional infrastructure capable of verifying commitments, monitoring negotiations, and ensuring reliable settlement across autonomous systems.