Course 5401
This course provides learners with the essential skills to govern the design, deployment, and scaling of autonomous, Agentic AI systems. It focuses on enabling rapid innovation and accelerating speed-to-market while managing the unique risks presented by AI systems that can make decisions without constant human input. The course is structured around the practical application of the four-phase Agentic AI Governance Maturity Roadmap (Establish, Implement, Scale, Accelerate). The course uses a blend of presentations, detailed case-study scenarios (Cymbal Health, Cymbal Insurance, etc.), group discussions, a tabletop exercise, and quizzes to ensure effective learning. The real-world examples ensure participants can immediately connect theoretical principles to their own organizational and regulatory challenges.
In-Person
Online
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Prerequisites:
Familiarity with AI Tools in an Enterprise setting
Who Should Attend:
Important Course Information:
Module 1: The agentic AI governance imperative
Module 2: Establish
Module 3 Implement
Module 4: Scale
Module 5: Accelerate Module 5: Accelerate
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Agentic AI refers to AI systems that can autonomously make decisions, plan actions, use tools, and execute tasks with limited human intervention. Because these systems can act independently, organizations must establish governance frameworks to ensure accountability, transparency, security, compliance, and alignment with business objectives. Effective governance helps organizations manage risks while enabling responsible innovation.
This course is designed for business leaders, AI program managers, risk and compliance professionals, cybersecurity practitioners, data governance teams, auditors, legal and regulatory specialists, and anyone responsible for overseeing, deploying, or managing AI systems within an organization. No advanced AI development experience is required.
Participants will learn how to identify governance challenges unique to Agentic AI, assess operational and compliance risks, establish governance controls, define accountability frameworks, and align AI initiatives with emerging regulations and industry best practices. By the end of the course, learners will be able to develop a practical governance approach for autonomous AI systems that balances innovation, trust, and organizational risk management.
