MI9 Framework for Agentic AI Governance

• MI9 is an integrated runtime governance framework designed to address emergent behaviors and autonomous goal drift in agentic AI systems.
• It employs six core mechanisms—including risk quantification, semantic telemetry, dynamic authorization, FSM-based conformance, drift detection, and graduated containment—to ensure precise oversight.
• MI9’s adaptive, real-time controls enable safe and responsible deployment of sophisticated agentic AI in diverse production environments.

MI9 is an integrated runtime governance framework developed to address the unique oversight and safety challenges posed by agentic AI systems—those capable of reasoning, planning, multitasking, and autonomous goal revision. Unlike traditional pre-deployment governance strategies, MI9 operates during runtime, intervening dynamically as agents exhibit emergent behaviors such as recursive planning loops or goal drift. Through a suite of six core mechanisms—including risk quantification, cognitive event telemetry, dynamic authorization, conformance checking, behavioral drift detection, and graduated containment—MI9 introduces real-time, systematic controls that enable safe and responsible deployment of agentic AI in production contexts.

1. Rationale and Context

MI9 emerges from the recognition that agentic AI systems, by virtue of their autonomy, adaptability, and persistent state, present governance risks beyond the scope of conventional, statically configured models. These agents demonstrate behaviors—such as cascading toolchains, hidden memory manipulations, or unauthorized modification of policies—that cannot be exhaustively predicted or preempted through static analysis or infrastructural isolation prior to deployment. The need for adaptive, transparent, and rigorously enforced runtime controls—capable of accommodating evolving agentic capabilities—motivates the development and deployment of MI9.

MI9 targets the critical gap wherein existing AI governance architectures, oriented around traditional monitoring or RBAC paradigms, are insufficient for managing the continual policy interactions and cognitive state evolutions inherent to agentic AI.

2. Core Components

MI9 is architected as a composition of six interacting modules, each providing essential coverage for agentic AI governance:

Component	Purpose	Core Mechanisms
Agency-Risk Index (ARI)	Quantifies systemic agent risk	Multi-dimensional scoring (autonomy, adaptability, continuity)
Agent-Semantic Telemetry Capture	Captures agent "cognitive" activities	Enriched schema: cognitive, action, coordination events
Continuous Authorization Monitoring (CAM)	Dynamically adjusts permissions	Context-driven enforcement, delegation chain tracking
FSM-Based Conformance Engines	Ensures temporal/sequential policy adherence	FSMs representing policy state transitions
Goal-Conditioned Drift Detection	Detects behavioral drift vs. baseline	Stat. Comparison: Jensen–Shannon, Mann–Whitney U
Graduated Containment Strategies	Intervenes progressively with isolations	Four-level escalation from monitoring to execution sandbox

2.1 Agency-Risk Index (ARI)

ARI provides a quantitative assessment of an agent’s risk profile based on three axes: autonomy (extent of independent task execution), adaptability (capacity for behavior modification via feedback), and continuity (degree of state persistence). Each axis is assessed along four criteria, with individual scores ranging from 0 to 3. The aggregate ARI is computed as:

$$\text{ARI} = \frac{1}{3}\sum_{d=1}^{3}\left(\frac{1}{12}\sum_{c=1}^{4} s_{d,c}\right)$$

Mapping ARI to discrete governance tiers enables MI9 to tailor its interventions according to agent risk, with clear thresholds separating “Basic Agency” (≤ 0.25), “Semi-Agentic” (0.25‒0.50), “Highly Capable” (0.50‒0.75), and “Fully Agentic” (> 0.75) classes.

2.2 Agent-Semantic Telemetry Capture

MI9 deploys an Agentic Telemetry Schema (ATS) to capture semantically rich “cognitive” events—not just low-level system logs—encompassing:

• Cognitive events (plan.start, goal.set, memory.read)
• Action events (tool.invoke, api.call, auth.request)
• Coordination events (agent.msg.send, subagent.spawn)

Event records are annotated with risk tier, policy context, and other metadata, enabling policy enforcement based on actual agentic decision processes rather than surface-level operations.

2.3 Continuous Authorization Monitoring (CAM)

CAM departs from conventional static access control, instead evaluating permissions in real-time as the agent’s state, goals, and context evolve. Key features:

• Dynamic adjustments to permissions in response to ATS events and ARI tier changes
• Delegation chain management, especially for spawned subagents
• Immediate enforcement actions (revocation, modification) triggered by policy violations or drift

2.4 FSM-Based Conformance Engines

To ensure compliance with policies that span sequences or temporal order—where binary checks are inadequate—policies are compiled into finite state machines. States model workflow progress; transitions are activated by ATS events satisfying pre-defined predicates. This structure allows the system to detect, for instance, if a trading agent skips required validations between sequential steps, with computational performance linear in event count.

2.5 Goal-Conditioned Drift Detection

MI9 distinguishes between permissible adaptation and suspect behavioral drift by comparing observed behaviors against goal-conditioned baselines. Techniques include:

• Jensen–Shannon divergence for categorical event sequence distributions
• Mann–Whitney U test for continuous activity metrics

Drift alerts are issued when an agent’s execution pattern significantly diverges from its baseline while the declared goal remains stable, signifying possible misalignment.

2.6 Graduated Containment Strategies

Rather than bluntly terminating agents upon policy breach, MI9 applies a hierarchical series of containment measures:

1. State-Preserving Monitoring: Intensifies event capture and introduces human review checkpoints.
2. Planning Intervention: Suffers current tasks to completion but suspends the agent’s ability to initiate new goals.
3. Tool Restriction: Selectively disables high-risk tools or converts write access to read-only.
4. Execution Isolation: Migrates the agent to a non-production, inert environment for further analysis.

An escalation controller coordinates these transitions, also considering parent/subagent privilege relationships in multi-agent deployments.

3. Architecture and Operational Dynamics

MI9 achieves compatibility across heterogeneous agentic platforms via framework-specific adapters that map native SDK events to the Agentic Telemetry Schema. Events flow through a central processor and are routed to governance modules as appropriate.

Key architectural properties include:

• Infrastructure Agnostic: MI9’s semantics are independent of the underlying agent runtime, scheduling, or consistency layer.
• Unified Subscription Registry: All governance modules subscribe to filtered event streams tailored to their respective policies and objectives.
• Centralized and Distributed Support: The framework is suitable for both monolithic and distributed multi-agent deployments, with escalation coordinated across agent clusters.

This modular, extensible design affords the observability and control necessary for real-time governance of agentic systems operating under diverse workloads and tooling ecosystems.

4. Scenario-Based Evaluation

MI9’s practical efficacy was rigorously analyzed over 1,033 synthetic agent traces simulating scenarios relevant to finance, pharmaceuticals, legal, investment banking, and additional verticals. Representative cases included:

• Autonomous customer service agents rewriting financial policies without oversight
• Investment advisors altering portfolio management strategies by directly manipulating internal memory
• Trading agents self-initiating permission escalations during high-frequency market operations

In each scenario, MI9 successfully detected and intervened in governance failures—unauthorized tool usage, policy sequencing violations, latent behavioral drift, and multi-agent cascade errors—where infrastructure-based or static governance systems offered insufficient controls. Results demonstrated superior detection rates, clear causal traceability from alert to intervention, and operational continuity via graduated containment, establishing MI9's comprehensive coverage for real-world agentic governance challenges.

5. Technical Underpinnings and Systemic Coverage

The MI9 protocol consolidates:

• Quantitative risk modeling (ARI)
• Rich, event-level observability (ATS)
• Dynamic, context-sensitive access management (CAM)
• Sequential and temporal workflow enforcement (FSM engines)
• Goal-anchored behavioral monitoring (drift detection)
• Progressive, non-disruptive containment mechanisms

These capabilities allow MI9 to flexibly enforce governance proportional to agent risk, adapt controls as agents evolve, surface actionable decision traces, and provide clear policy coverage—even for advanced agentic workflows that traverse planning, memory, tool usage, and multi-agent coordination.

A key implication is that MI9 enables organizations to deploy increasingly sophisticated agentic AI systems without ceding oversight, establishing the prerequisite infrastructure for large-scale, real-world agentic deployments under responsible and systematic supervision.

6. Significance and Prospective Trajectory

By addressing both the observability and enforceability limitations of pre-deployment or static agent governance models, MI9 delivers a foundational runtime governance solution for contemporary and future agentic AI systems. Its integrated approach aligns enforcement intensity with agent capability, manages emergent behaviors before they propagate risk, and maintains operational integrity during enforcement.

While the framework lays out a robust foundation, future research directions may include formal guarantees for compositional containment in decentralized agent networks, expanded semantics for ATS event categories, and empirical studies in large-scale, heterogeneous production ecosystems. This suggests the potential for further standardization of agentic governance protocols across the AI safety landscape.