Free course material
Make autonomy governable in operation.
AI Governance for Agents
Move from model governance to runtime governance for autonomous agents, delegated authority, tools, memory, and multi-agent systems.
What this course is about
A practical path from understanding to application.
Autonomous agents change governance because they can plan, call tools, retain state, delegate work, and affect real systems. This course develops a practical operating model for governing that authority through risk classification, identity, authorization, policy-as-code, human oversight, security testing, observability, continuous evaluation, and enterprise control-plane architecture.
Covered material
What learners will work through.
- The shift from model governance to agent governance and autonomy-based risk classification
- Agent identity, delegated authority, fine-grained authorization, and accountability
- Policy-as-code, runtime governance, tool and MCP governance, and bounded autonomy
- Data, RAG, memory, multi-agent delegation, human oversight, and escalation
- Guardrails, agent security, red teaming, adversarial testing, and incident readiness
- Observability, continuous evaluation, governance control planes, and enterprise operating models
Who it is for
Meet learners where they are.
Governance and risk leaders, engineers, architects, security teams, platform owners, product leaders, and technical executives responsible for deploying or overseeing agentic systems.
Adapt this course
Bring the material to your organization.
We can adapt the level, examples, exercises, and pace for executives, non-technical groups, technical teams, or mixed audiences. Sessions combine theory with practical and coding work where it fits, led by educators who meet with your group.
Book a custom training conversationRelated One+i service
AI governance & technical leadership
Connect open learning with the strategy, delivery, and adoption support around it.