Multi-Agent System (MAS) — Governance of Collaborative AI Architectures

Definition

A multi-agent system (MAS) is an AI architecture in which multiple autonomous AI agents operate collaboratively to accomplish tasks that exceed the capability, scope, or efficiency of any single agent. In a MAS, agents can perceive their environment, make decisions, use tools, communicate with other agents, and take actions — either independently or through coordinated delegation patterns managed by an orchestrator agent.

The structural components of a multi-agent system include: an orchestrator agent that decomposes goals into subtasks and delegates them to specialized subagents; subagents that execute specific functions (web search, code execution, document analysis, API calls); shared memory or state that agents use to pass information between steps; and tool interfaces that connect agents to external systems. In production enterprise deployments, MAS architectures are increasingly used for complex workflow automation, research, content pipelines, and decision support.

From a governance perspective, MAS introduce a qualitatively different risk profile from single-agent or traditional AI systems. The AIGN Agentic AI Governance Framework 1.0 identifies the core governance challenge of MAS as distributed accountability: when multiple agents contribute to a decision or action sequence, the attribution of responsibility for errors, harms, or policy violations becomes structurally unclear without explicit governance design.

Why it matters operationally

Multi-agent systems matter for governance because they are the architectural pattern where AI governance frameworks show their most significant gaps. ISO/IEC 42001 was designed for AI management systems governing AI models. The EU AI Act was written with single-system classification in mind. Neither framework has explicit provisions for how governance obligations distribute across a network of collaborating agents.

The Future Society’s 2025 analysis of AI agent governance under the EU AI Act identifies the classification problem as fundamental: a MAS deployed in a high-risk context may involve one orchestrator agent and five specialized subagents, each developed by different providers. Which system is the high-risk system? Which provider must complete the conformity assessment? The answer requires governance by design, not ex post regulatory interpretation.

For organizations deploying MAS in enterprise workflows, the practical governance question is: when the system causes harm, who is accountable, what was the decision sequence that led to the outcome, and is there an audit trail that makes that sequence reconstructable?

Regulatory framework

Framework	Multi-agent system governance
EU AI Act	Has no explicit provisions for MAS. Annex III risk classification applies to the system based on its intended use, but in a MAS the responsibility between orchestrator and subagents is not regulated. The European AI Office is monitoring agentic systems as an area of regulatory development.
AIGN Framework 1.0	The first framework specific to governance of autonomous and multi-agent systems (July 2025). Proposes principles of distributed traceability, per-agent scope confinement, and emergency stop mechanisms for the orchestrator.
ISO/IEC 42001	Annex A controls on AI system lifecycle must be extended to cover multi-agent architectures. Certification requires the AIMS to cover all AI systems in scope, including subagents operating within organizational control.
NIST AI RMF	The Map function must cover the inventory of all agents in the system, not just the primary agent. The Manage function must include containment mechanisms for each agent.

How Zertia evaluates it

Zertia evaluates multi-agent system governance through the AI Model Audit, which for MAS architectures includes: mapping the full agent topology (orchestrator, subagents, tool interfaces, shared memory); evaluating whether accountability is assigned at each node of the architecture; assessing whether audit trails are complete across the full action sequence; and examining whether containment controls (scope limitation, override mechanisms, rollback procedures) apply to the system as a whole and to each agent individually.

For organizations preparing ISO/IEC 42001 certification that include MAS in their AIMS scope, Zertia applies the AIGN Framework 1.0 principles as a reference alongside ISO 42001 Annex A controls.

[AI Model Audit] · ISO 42001 Certification