Cross-System Constraint Collisions: The Governance Gap in Enterprise Agentic AI [pdf] (himalaian.com)

A new paper by Michael Mallon introduces CrAIg™ (Continuous Runtime AI Governance), a framework that addresses the emerging governance gap in enterprise environments using autonomous AI agents. As AI agents transition from merely providing insights to executing decisions across multiple independent systems—like CRMs, financial platforms, and compliance tools—the potential for cross-system conflicts escalates. The paper emphasizes that while individual systems operate correctly, their interactions can lead to unforeseen contradictions that current governance frameworks are ill-equipped to detect. This framework is significant for the AI/ML community as it highlights the need for a unified approach to governance that spans system boundaries, promoting operational safety in AI-integrated workflows. CrAIg™ aims to monitor actions at each execution step across interconnected platforms, detecting constraint violations before they can lead to irreversible consequences. By focusing on the entire transaction path—rather than just individual systems—CrAIg™ could substantially mitigate risks associated with the rapidly evolving role of autonomous AI in enterprise operations, ultimately enhancing the integrity of decision-making processes in complex, automated environments.