Agent Governance Learning Path
Learn how agent governance enables AI adoption by balancing innovation, security, compliance, ownership, and human accountability.
What you will learn
Policies, lifecycle controls, identity, data permissions, evaluation, risk management, and oversight.
Architecture, security, and governance
Make governance an enablement system. Provide approved patterns, clear ownership, measurable controls, and a path from pilot to production.
🎧 Recommended podcast episodes
- Agent Governance Explained — practical governance perspectives for enabling adoption.
- Responsible AI Is Good Business — connects governance to business outcomes.
Frequently asked question
What is the best next step?
Start with the recommended episode, document the business problem you are solving, and use the parent hub to choose the next adjacent learning path.
Continue learning
Return to the Microsoft AI and Agents Learning Hub, then continue through the M365.fm Learning Hub.
Last reviewed: July 2026.
Agent governance learning objectives
Learn how ownership, identities, permissions, controls, and monitoring make enterprise agent use accountable.
FAQ
What should agent governance define first?
Start with the accountable owner, permitted actions, data boundaries, and the review process for high-impact outcomes.
Continue learning: return to the Microsoft AI and Agents Learning Hub and choose the adjacent module that matches your role.
Last reviewed: July 2026.