arXiv: Governance-as-a-Service - Multi-Agent Framework for AI Compliance
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Proposes Governance-as-a-Service, a modular framework for scalable AI compliance and oversight in multi-agent systems, addressing governance challenges in distributed autonomous AI ecosystems.
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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.
Summary
This paper introduces Governance-as-a-Service (GaaS), a modular enforcement layer that regulates multi-agent AI systems at runtime without modifying model internals or requiring agent cooperation. GaaS uses declarative rules and a Trust Factor mechanism to score agents on compliance, enabling coercive, normative, and adaptive interventions. Evaluated across content generation and financial decision-making tasks with open-source models, GaaS demonstrates reliable blocking of high-risk behaviors while maintaining system throughput, positioning governance as infrastructure-level alignment for distributed AI ecosystems.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Policy Effectiveness | Analysis | 64.0 |
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Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement
Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement
Suyash Gaurav \equalcontrib 1 ,
Jukka Heikkonen 2 ,
Jatin Chaudhary #2
Abstract
As AI systems evolve into distributed, agentic ecosystems capable of autonomous task execution, asynchronous reasoning, and multi-agent coordination the absence of scalable, decoupled governance remains a structural liability. Existing oversight mechanisms are typically reactive, hardcoded, or embedded within agent architectures, rendering them brittle, non-auditable, and difficult to generalize across heterogeneous deployments.
We propose Governance-as-a-Service (GaaS): a modular, policy-driven enforcement layer that governs agent outputs at runtime without modifying internal model logic or assuming agent cooperation. GaaS operates through declarative rule sets and a Trust Factor mechanism that scores agents based on longitudinal compliance and severity-aware violation history. It supports coercive, normative, and adaptive interventions, allowing for graduated enforcement and per-agent trust modulation.
To empirically evaluate GaaS, we design three simulation regimes using open-source language models (LLaMA3, Qwen3, DeepSeek-R1) across two critical domains: content generation and financial decision-making. In the baseline, agents operate without governance; in the second, GaaS is deployed as an enforcement layer; in the third, adversarial agents are introduced to probe robustness. All agent actions are intercepted, evaluated, and logged for downstream analysis. Results indicate that GaaS consistently blocks or redirects high-risk behaviors while preserving agentic throughput. Trust scores evolve in alignment with rule compliance, demonstrating the system’s ability to isolate, penalize, and adapt to untrustworthy components within complex multi-agent systems. By treating governance as a runtime service on par with compute, storage, or memory GaaS establishes a foundation for infrastructure-level alignment in unregulated, interoperable agent ecosystems. It does not teach agents ethics; it enforces them.
1 Introduction
The emergence of AI agents marks a significant evolution in machine learning transforming theoretical constructs into modular, production-grade systems capable of multi-layered task execution, long-horizon planning, and hierarchical reasoning (Russell, Dewey, and Tegmark 2015 ; Hughes et al. 2025 ; Acharya, Kuppan, and Divya 2025 ) . These agents now write public-facing content, execute financial transfers, and even control infrastructure-level actions with minimal human intervention. This newfound autonomy is often framed as a strength, but it also introduces a structural weakness: governance in agentic environments becomes deeply entangled with the agents themselves because of the decentralized nature of th
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