Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement

2025-08-26

AI Summary

Current multi-agent AI systems face structural risks—including unscalable, non-auditable, and cross-heterogeneous-deployment-incompatible governance mechanisms—because existing supervision methods are embedded within agents, rendering them reactive and fragile. This paper proposes a modular, policy-driven, externalized runtime governance framework that abstracts compliance regulation into an independent service layer, requiring no model architecture modifications or agent cooperation. Key innovations include: (i) a declarative rule engine; (ii) runtime output interception; (iii) severity-weighted violation assessment; and (iv) a scalable trust scoring mechanism enabling mandatory, normative, and adaptive interventions. Extensive experiments across LLaMA3, Qwen3, and DeepSeek-R1 demonstrate that the framework effectively blocks high-risk behaviors, preserves system throughput, enables precise compliance tracking, and exhibits strong robustness under adversarial testing.

Technology Category

• Multiagent Systems: Mechanism Design
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

As AI systems evolve into distributed ecosystems with autonomous execution, asynchronous reasoning, and multi-agent coordination, the absence of scalable, decoupled governance poses a structural risk. Existing oversight mechanisms are reactive, brittle, and embedded within agent architectures, making them non-auditable and hard to generalize across heterogeneous deployments. We introduce Governance-as-a-Service (GaaS): a modular, policy-driven enforcement layer that regulates agent outputs at runtime without altering model internals or requiring agent cooperation. GaaS employs declarative rules and a Trust Factor mechanism that scores agents based on compliance and severity-weighted violations. It enables coercive, normative, and adaptive interventions, supporting graduated enforcement and dynamic trust modulation. To evaluate GaaS, we conduct three simulation regimes with open-source models (LLaMA3, Qwen3, DeepSeek-R1) across content generation and financial decision-making. In the baseline, agents act without governance; in the second, GaaS enforces policies; in the third, adversarial agents probe robustness. All actions are intercepted, evaluated, and logged for analysis. Results show that GaaS reliably blocks or redirects high-risk behaviors while preserving throughput. Trust scores track rule adherence, isolating and penalizing untrustworthy components in multi-agent systems. By positioning governance as a runtime service akin to compute or storage, GaaS establishes infrastructure-level alignment for interoperable agent ecosystems. It does not teach agents ethics; it enforces them.

Problem

• Addressing scalable governance in distributed AI ecosystems
• Enforcing compliance without modifying agent internals
• Providing runtime policy enforcement for heterogeneous deployments

Innovation

• Modular policy-driven enforcement layer for runtime regulation
• Declarative rules and Trust Factor mechanism for compliance scoring
• Coercive normative adaptive interventions with graduated enforcement