MI9: An Integrated Runtime Governance Framework for Agentic AI

Charles L. Wang; Trisha Singhal; Ameya Kelkar; Jason Tuo

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arXiv · 2508.03858

MI9: An Integrated Runtime Governance Framework for Agentic AI

Charles L. Wang·Trisha Singhal·Ameya Kelkar·Jason Tuo

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Abstract

Agentic AI systems capable of reasoning, planning, and executing actions present fundamentally distinct governance challenges compared to traditional AI models. Unlike conventional AI, these systems exhibit emergent and unexpected behaviors during runtime, introducing novel agent-related risks that cannot be fully anticipated through pre-deployment governance alone. To address this critical gap, we introduce MI9, the first fully integrated runtime governance framework designed specifically for safety and alignment of agentic AI systems. MI9 introduces real-time controls through six integrated components: agency-risk index, agent-semantic telemetry capture, continuous authorization monitoring, Finite-State-Machine (FSM)-based conformance engines, goal-conditioned drift detection, and graduated containment strategies. Operating transparently across heterogeneous agent architectures, MI9 enables the systematic, safe, and responsible deployment of agentic systems in production environments where conventional governance approaches fall short, providing the foundational infrastructure for safe agentic AI deployment at scale. Detailed analysis through a diverse set of scenarios demonstrates MI9's systematic coverage of governance challenges that existing approaches fail to address, establishing the technical foundation for comprehensive agentic AI oversight.

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BibTeX RIS

Charles L. Wang, Trisha Singhal, Ameya Kelkar, Jason Tuo. 2025-11-18. MI9: An Integrated Runtime Governance Framework for Agentic AI. https://arxiv.org/abs/2508.03858

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