⚡ Quick Summary
Agentic AI systems—those that can plan, act, and adapt with minimal human input—are no longer research experiments. They’re shipping fast. The AIGN Agentic AI Governance Framework v1.0 offers the first structured governance model tailored to these autonomous, tool-using systems. Developed by the Artificial Intelligence Governance Network, this framework distinguishes between traditional ML governance and the risks introduced by agentic behavior, including cascading failures, emergent misalignment, and toolchain misuse. It introduces 5 governance principles, 4 system types, and a 3-tiered responsibility model to help organizations assess and mitigate risks across planning, action, and reflection loops. Designed for both enterprise and public sector deployments.
🧩 What’s Covered
The 32-page framework is structured into four parts:
- What Makes Agentic AI DifferentThe introduction defines agentic systems as AI that can:It highlights risks not present in classic supervised ML, such as:
- Plan tasks across time
- Execute actions via tools/APIs
- Modify their own goals (within constraints)
- Cascade risk (a bad plan compounding across steps)
- Toolchain abuse (e.g. API injections)
- Emergent misalignment (when tools shift model behavior unpredictably)
- Four Agent TypesA taxonomy helps teams scope governance based on autonomy and access:Each comes with recommended controls (p. 14–19), such as planning limits, action review hooks, or runtime guardrails.
- Tactical Assistants (e.g. auto-fillers, basic planners)
- Multitool Agents (e.g. code/finance agents with toolchains)
- Autonomous Planners (e.g. long-horizon project agents)
- Self-Reflective Systems (e.g. recursive optimizers or multi-agent collectives)
- Governance PillarsFive cross-cutting pillars are introduced:
- Action Auditability: Structured logging, human veto, traceable plans
- Sandboxing: Tool scope restriction, memory isolation, rollback
- Boundary Clarity: Making human vs. agent responsibility legible
- System Decomposition: Breaking agents into auditable submodules
- Reflection Controls: Guarding meta-behavior and self-updates
- Three-Tiered Responsibility ModelInspired by the RACI framework, it assigns roles across:This helps avoid the “responsibility sink” that emerges when failures fall between actors.
- Model Developers (what agentic functions are enabled)
- Tool/Environment Providers (what tools can be accessed)
- Deployers (what policies are enforced, how autonomy is scoped)
💡 Why it matters?
Agentic AI brings new governance challenges: unpredictability, toolchain dependency, and blurred responsibility. This framework is a crucial first step in codifying how organizations can structure accountability, embed controls, and prevent risk amplification. It aligns with ISO 42001 and the EU AI Act’s emphasis on post-deployment oversight—but fills a gap neither directly address yet. As enterprise agents proliferate, the playbook for guardrails can’t lag behind.
❓ What’s Missing
- Metrics or Benchmarks: The framework lacks maturity levels or quantitative scoring.
- Human-AI Handover Design: Limited detail on failover strategies or UI handoffs.
- Public Interest Scenarios: Focuses heavily on enterprise deployment; little on civic/government use.
👥 Best For
- AI Governance and Safety Leads
- Product teams building or integrating autonomous agents
- CISOs and Risk Officers managing tool-based AI execution
- Procurement officers vetting agentic systems
- Policy teams preparing internal guidance for agent-based tooling
📄 Source Details
Title: Agentic AI Governance Framework v1.0
Published by: Artificial Intelligence Governance Network (AIGN)
Date: July 2025
Length: 32 pages
Structure: Typology of agents, five governance pillars, three responsibility layers
Website: aigovernancenetwork.org
📝 Thanks to
The team at AIGN, and contributing experts from Cohere for AI, Cambridge Centre for the Study of Existential Risk, and several anonymous reviewers, for bringing structure and clarity to one of the most urgent frontiers in AI governance.
