What is Model AI Governance Framework for Agentic AI?
A model AI governance framework for agentic AI is a structured set of controls for AI systems that can plan, act, and invoke tools or workflows with limited direct human prompting. It is important because agentic systems can create higher operational, security, and accountability risks than static models, so governance must address autonomy, supervision, and escalation.
In Depth
In practice, such a framework defines the system's permitted objectives, autonomy bounds, approval checkpoints, tool-access rules, logging, testing, incident escalation, and kill-switch arrangements. It also assigns responsibilities across product, security, legal, compliance, and business owners so that actions taken by the agent can be traced and intervened in when needed.
For compliance teams, the key issue is ensuring that the model's delegated actions remain within approved risk appetite and that there are documented controls for prompt injection, misuse, harmful side effects, and unauthorized external actions. This term is closely aligned with emerging governance practice for agentic AI and with broader AI management system expectations in ISO/IEC 42001, NIST AI RMF, and EU AI Act-style risk management for high-risk use cases.
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