What You’ll Learn

• Explain the core principles of agentic AI and distinguish it from generative AI models.
• Describe how AI agents interact with tools and services using protocols like MCP to perform complex tasks autonomously.
• Analyze the structure and function of orchestrator agents and how they coordinate multiple agents to complete workflows.
• Evaluate the benefits, limitations, and risks of deploying agentic AI in real-world environments and multi-agent systems.

Course Overview

• Explore how agentic AI differs from generative AI and why this matters in real-world applications 
• Understand what makes an AI system an “agent” and how agents operate independently 
• Examine how agentic systems compare to traditional AI workflows and automation tools 
• Learn about different categories of AI agents and the roles they play 
• Identify practical scenarios where agentic AI is useful—and where it may not be the best fit 

• Discover how AI agents enhance their capabilities by interacting with external tools and services 
• See how agents can perform tasks like querying databases using real-world examples such as SQL agents 
• Understand the Model Context Protocol (MCP) and how it enables seamless tool integration 
• Learn when it’s appropriate for agents to call tools automatically versus when human oversight is needed 

• Understand the importance of Retrieval-Augmented Generation (RAG) for accessing up-to-date information 
• Learn how agents use RAG to improve reasoning and decision-making 
• Explore orchestrator agents, which coordinate multiple specialized agents to complete complex workflows 
• Get introduced to the basics of multi-agent systems and how they communicate and collaborate 
• Examine the potential risks and challenges of deploying agentic AI in real-world environments 

Course prerequisites