Course Description

This is a self-paced independent learning course  focuses on developing agentic AI applications using LangGraph, a well-established and widely adopted Python-based framework for building AI agents. Participants will learn how to design, structure, and operationalize AI agents capable of reasoning and  decision-making as well as branching.

Course Topics

1. Introduction to Agentic AI
   Understanding the concept, benefits, and real-world applications of agentic AI.

2. Fundamentals of LangGraph
   Core components, architecture, states, nodes, edges, and workflows.

3. Conditional Logic
   Designing intelligent agent behavior using branching logic and flow control.

4. LLM Integration & Reasoning
   Connecting LangGraph with LLMs (e.g., OpenAI, Gemini) and enabling reasoning capabilities.

Learning Outcomes

Upon successful completion of this course, participants will be able to:

• Design and develop AI agents using the LangGraph framework.

• Implement conditional branching to create dynamic and intelligent agent workflows.

• Integrate Large Language Models (LLMs) with LangGraph for reasoning and decision-making.

Course Prerequisites

• Basic knowledge of Python programming.

• Exposure to LLM tools such as ChatGPT, Gemini, or similar platforms is helpful but not mandatory.

Course Assessment

Assessment for this course is based on three (3) online multiple-choice quizzes. Participants must complete and pass these quizzes to demonstrate understanding of the course content.

Note: The course content and assessment are subject to change without prior notification.