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Section 1: Introduction to Agentic AI

What is Agentic AI?

Generative AI vs. Agentic AI

Section 2: The Anatomy of an AI Agent

The Role of Reasoning in AI Agents

Memory Systems in AI Agents

Tool Usage and Action Execution

Section 3: How AI Agents Work: The Workflow

Task Planning and Goal Breakdown

Execution and Feedback Loops

Section 4: Safety, Limitations, and Ethics

Current Limitations of Agentic AI

Safety and Human-in-the-Loop

Section 5: Knowledge Check

Quiz: Foundations of Agentic AI

Quiz: Agent Components

Quiz: Memory and Tools

Quiz: Workflows and Feedback

Quiz: Limitations and Safety

Section 6: Course Feedback

End of Course Survey

Welcome to "Foundations of Agentic AI: Components, Workflows, and Safety," your comprehensive guide to the next frontier of artificial intelligence. While traditional generative AI models excel at answering questions and generating text, Agentic AI represents a monumental shift toward autonomous, goal-oriented action.

Welcome to "Foundations of Agentic AI: Components, Workflows, and Safety," your comprehensive guide to the next frontier of artificial intelligence. While traditional generative AI models excel at answering questions and generating text, Agentic AI represents a monumental shift toward autonomous, goal-oriented action. In this course, you will explore how AI systems are evolving from passive responders to active problem-solvers capable of reasoning, planning, and executing complex tasks.

We will dive deep into the anatomy of an AI agent, uncovering how these systems use short-term and long-term memory, leverage external tools like web searches and calculators, and apply the ReAct (Reasoning and Acting) framework to "think" before they act. You will learn the step-by-step workflow of task planning, goal breakdown, and the critical feedback loops that allow agents to learn from their environment and correct their own mistakes.

Beyond the mechanics, this course places a strong emphasis on the ethical and practical considerations of deploying autonomous systems. You will examine current limitations, such as AI hallucinations and infinite loops, and understand why "human-in-the-loop" (HITL) safeguards are essential for responsible AI development. Whether you are an AI enthusiast, a developer, or a business leader looking to harness the power of autonomous agents, this course provides the foundational knowledge you need to navigate the future of Agentic AI safely and effectively.

Foundations of Agentic AI: Components, Workflows, and Safety

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            <p>Artificial intelligence is rapidly evolving from passive, text-generating models into dynamic, goal-oriented systems capable of autonomous action. This course, Foundations of Agentic AI: Components, Workflows, and Safety, explores this critical transition by examining the fundamental architecture of AI agents. Learners will investigate the core characteristics that distinguish agentic systems from traditional generative AI, focusing on the underlying anatomy that enables autonomous problem-solving. By delving into the cognitive mechanisms of these systems, the curriculum covers essential components such as advanced reasoning frameworks, the integration of short-term and long-term memory, and the practical execution of external tools to achieve complex objectives.</p>

<p>Beyond the structural components, the curriculum provides a comprehensive analysis of the operational workflows that drive AI agents. This includes the systematic breakdown of high-level goals into manageable sub-tasks and the implementation of continuous execution and feedback loops that allow agents to observe, adapt, and correct their course in real-time. Furthermore, as autonomy increases, so does the necessity for rigorous safety protocols. The course critically evaluates the current limitations of agentic AI, such as infinite loops and hallucinations, while emphasizing the ethical imperative of human-in-the-loop oversight and robust guardrails. Through structured modules and rigorous knowledge checks, participants will develop a thorough understanding of how to safely and effectively navigate the emerging landscape of autonomous artificial intelligence.</p>
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