Description

Introduction

As Agentic AI systems become more autonomous and goal-driven, their deployment introduces complex governance and risk management challenges. These systems can act independently, interact with dynamic environments, and influence critical decisions. This course explores frameworks, best practices, and real-world strategies to govern agentic AI responsibly while mitigating operational, ethical, legal, and systemic risks.

Prerequisites

• Understanding of AI/ML systems and basic agentic AI concepts

• Familiarity with enterprise AI workflows or AI policy considerations

• Interest in AI governance, compliance, or risk management

• Optional: Exposure to LLM-based tools and agentic frameworks (e.g., LangChain, AutoGen)

Table of Contents

1. Overview of Agentic AI Deployment Risks
1.1 Autonomy and Risk Amplification
1.2 Technical vs. Organizational Risks
1.3 Risk Taxonomy: Operational, Strategic, Legal, Ethical

2. Governance Frameworks for Agentic Systems
2.1 AI Governance Models and Maturity Levels
2.2 Policies for Deployment, Monitoring, and Control
2.3 Stakeholder Roles and Responsibilities

3. Risk Assessment and Mitigation Planning
3.1 Pre-Deployment Risk Mapping
3.2 Agent Behavior Simulation and Stress Testing
3.3 Establishing Guardrails and Failsafes

4. Compliance and Regulatory Considerations
4.1 Evolving AI Regulations (EU AI Act, ISO/IEC standards)
4.2 Legal Liabilities and AI Decision Traceability
4.3 Industry-Specific Regulatory Landscapes

5. Monitoring, Auditing, and Reporting Mechanisms
5.1 Real-Time Behavior Logging and Escalation Protocols
5.2 Internal and Third-Party Auditing for Agentic Systems
5.3 Transparency Reports and Accountability Dashboards

6. Organizational Structures for Governance
6.1 AI Risk Committees and Review Boards
6.2 Cross-Functional Collaboration: Tech, Legal, Ethics
6.3 AI Governance Playbooks and Standard Operating Procedures

7. Security, Misuse, and Containment
7.1 Protecting Agents from Adversarial Attacks
7.2 Preventing Unauthorized Autonomy and Escalation
7.3 Agent Shutdown, Isolation, and Response Plans

8. Case Studies and Governance Failures
8.1 Agentic AI in Finance, Healthcare, and Government
8.2 Real-World Failures Due to Poor Governance
8.3 Lessons Learned and Redesign Insights

Deploying agentic AI requires more than technical expertise—it demands rigorous governance structures, proactive risk identification, and continuous oversight. As these systems become more capable and independent, managing their risks becomes a shared responsibility across disciplines. This course equips professionals to lead responsible deployments of agentic AI that are secure, compliant, and aligned with organizational and societal values.