MI9: An Integrated Runtime Governance Framework for Agentic AI

TL;DR

The paper tackles runtime governance for agentic AI, addressing how emergent, sequential behaviors during execution create risks not captured by pre-deployment checks. It proposes MI9, a framework-agnostic, integrated runtime governance architecture with six components: Agency-Risk Index (ARI), Agentic Telemetry Schema (ATS), Continuous Authorization Monitoring (CAM), Real-Time Conformance Engine (FSM-based), Behavioral Drift Detection, and Graduated Containment. The key contributions include a formal ARI-based governance tiering, a semantically rich telemetry layer for governance events, dynamic permission management, sequence-aware policy enforcement, goal-conditioned drift analysis, and agent-aware containment strategies across single and multi-agent setups. The research demonstrates MI9’s effectiveness in synthetic scenarios, showing high detection rates, strong causal-clarity, and proactive intervention capabilities, with practical implications for deploying agentic AI safely at scale across heterogeneous ecosystems.

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.

Figures (2)

• Figure 1: MI9 Framework Pipeline
• Figure 2: Finite state machine states for an agentic workflow