MI9 -- Agent Intelligence Protocol: Runtime Governance for Agentic AI Systems (2508.03858v2)

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.

• The paper presents MI9, a runtime governance framework for agentic AI systems that scales oversight using an agency-risk index and adaptive monitoring.
• It employs continuous authorization monitoring and an FSM-based conformance engine to enforce dynamic policy compliance in real time.
• Evaluation on synthetic agent scenarios showed a 99.81% detection rate, highlighting its effective real-time intervention capabilities.

MI9 -- Agent Intelligence Protocol: Runtime Governance for Agentic AI Systems

Introduction to MI9 Framework

MI9 is a comprehensive runtime governance framework designed specifically for agentic AI systems. These systems encapsulate AI agents capable of reasoning, planning, and executing complex actions autonomously. The framework addresses unique governance challenges that emerge in such systems during runtime, which cannot be fully mitigated through traditional pre-deployment oversight.

MI9 introduces an integrated set of tools and strategies for real-time governance, consisting of six key components: the agency-risk index for scaling oversight intensity, agent-semantic telemetry capture for monitoring cognitive events, continuous authorization monitoring to maintain contextual permissions, FSM-based conformance engines for policy enforcement, goal-conditioned drift detection to identify behavioral anomalies, and graduated containment strategies for targeted intervention.

Figure 1: MI9 Framework Pipeline.

Core Components and Their Implementation

Agency-Risk Index

The Agency-Risk Index (ARI) is pivotal to scaling governance requirements based on agent capabilities. It considers autonomy, adaptability, and continuity as three orthogonal dimensions contributing to the overall risk profile. Each dimension comprises criteria scored to yield an aggregate index, subsequently mapped to governance tiers. This tier system enables variable governance measures proportional to the assessed risk.

Agentic Telemetry Schema

The ATS is designed to overcome the blind spots of traditional observability frameworks by capturing agent-semantic events. These include cognitive processes, action executions, and coordination activities, which are critical to runtime behavioral monitoring. ATS extends OpenTelemetry conventions, providing structured event capture relevant to governance decisions.

Continuous Authorization Monitoring

CAM introduces dynamic permission management that adapts based on agent behavior context, extending beyond static authorization models. It incorporates goal-awareness into permission evaluation, tracking delegation chains and adjusting access rights in real-time to prevent unauthorized escalation and maintain secure operational integrity.

Real-Time Conformance Engine

The MI9 conformance engine utilizes FSMs to enforce temporal sequence constraints within agent workflows. This tool enables organizations to detect and address patterns indicative of policy violations spanning multiple steps. The FSM model achieves this through deterministic, efficient evaluation of ATS streams configured for pattern recognition.

Figure 2: Finite state machine states for an agentic workflow.

Behavioral Drift Detection

Drift detection is key to discerning between legitimate adaptation and emergent misalignment. MI9 employs goal-conditioned baselines to differentiate natural learning processes from anomalous behavioral shifts. Statistical techniques like Jensen-Shannon divergence and Mann-Whitney U tests are used to evaluate deviations from established norms.

Graduated Containment Strategies

MI9 offers a graduated containment approach tailored for agentic systems, comprising targeted intervention levels. It supports operational continuity by prioritizing corrective over disruptive measures, including state-preserving monitoring to execution isolation, depending on risk assessments.

Evaluation and Comparison

MI9's performance was evaluated using synthetic agent scenarios, demonstrating a notable detection rate of 99.81%. It outpaced existing frameworks in governance dimensions such as causal clarity and predictive alerting, fulfilling critical governance needs in complex agentic environments. This systematic evaluation underscores MI9's capability to provide actionable intelligence through real-time interventions and risk signal processing.

Conclusion

MI9 presents a novel governance framework for agentic AI systems, offering real-time intervention capabilities that traditional approaches lack. By integrating agent-semantic telemetry and adaptive permission monitoring, MI9 lays the foundational infrastructure for deploying agentic systems safely and responsibly at scale. While the synthetic evaluation highlights its potential, real-world validation within production environments remains an important next step for comprehensive efficacy assessment.