Agentic AI Governance Framework — AIGN Framework 1.0 and EU AI Act

Definition

An Agentic AI Governance Framework is a structured set of principles, requirements, and controls specifically designed for the governance of autonomous and multi-agent AI systems — addressing the governance gaps that general AI management frameworks like ISO/IEC 42001, the EU AI Act, and NIST AI RMF leave unaddressed when applied to systems that act autonomously across extended task sequences.

The AIGN Agentic AI Governance Framework 1.0, published by the Artificial Intelligence Governance Network in July 2025, is the first comprehensive governance framework purpose-built for autonomous and multi-agent AI. It operationalizes trust for self-improving, autonomous, and multi-agent systems through five governance domains: Identity and Authorization (who or what can instruct the agent, and how that authority is verified); Scope and Containment (what the agent can do, access, and affect); Traceability (what records must be maintained to reconstruct agent decisions and actions); Oversight and Intervention (when and how humans can monitor, redirect, or stop agents); and Incident and Escalation (how failures are detected, classified, reported, and remediated).

For organizations deploying agentic AI in contexts covered by the EU AI Act, The Future Society’s 2025 analysis provides the complementary regulatory mapping: how the EU AI Act’s existing high-risk classification and conformity assessment framework applies to agentic systems, and where regulatory gaps require additional governance measures beyond what the Act currently mandates.

Why it matters operationally

Agentic AI governance frameworks matter because the governance frameworks most organizations use — ISO 42001, EU AI Act, NIST AI RMF — were designed for AI systems that respond to discrete inputs with discrete outputs. Agentic systems are fundamentally different: they plan across time horizons, take sequences of actions with compounding effects, delegate to sub-systems, and operate with reduced human involvement per decision. The governance gaps that emerge at the intersection of autonomy and consequential action require dedicated frameworks, not just analogical extension of existing ones.

The AIGN Framework 1.0 provides the most operational reference available for closing these gaps. Organizations deploying agentic AI in enterprise production environments that want to demonstrate governance alignment with international best practice now have a specific framework to reference — equivalent in function to what ISO 42001 provides for AI management systems generally.

Regulatory framework

Framework	Agentic governance framework context
AIGN Framework 1.0	The first framework specific to autonomous and multi-agent AI governance (July 2025). Five domains: Identity, Scope, Traceability, Oversight, Incidents. Governance reference for organizations deploying agentic systems in production.
EU AI Act	The Future Society (2025) maps how the EU AI Act applies to agentic systems, identifying regulatory gaps where the Regulation has no specific provisions for agentic architectures. The European AI Office is monitoring this area.
ISO/IEC 42001	The general AI management system framework that agentic governance frameworks complement. The five AIGN Framework domains can be mapped to ISO 42001 Annex A controls, extending their application to agentic systems.
NIST AI RMF	NIST is developing specific guidance for agentic systems as an extension of the AI RMF, including the publication on measurement in agentic ecosystems (2026).

How Zertia evaluates it

Zertia uses the AIGN Framework 1.0 as a reference framework alongside ISO 42001 Annex A when auditing organizations that deploy agentic AI systems. The AI Model Audit for agentic systems evaluates governance across all five AIGN domains: whether agent identity and authorization mechanisms are in place; whether scope limitation is implemented and reviewed; whether traceability covers the full action sequence; whether oversight and intervention mechanisms are genuine and operational; and whether incident response procedures address agentic-specific failure modes.

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