Hello, and welcome to the Fundamentals of Building AI Agents course.

This course is designed for aspiring software engineers, data scientists, machine learning engineers, AI architects, and automation engineers. It equips you with the knowledge and skills to develop and integrate intelligent AI agents. You’ll explore foundational concepts like tool calling, understanding how large language models (LLMs) can leverage external capabilities. You’ll also learn to orchestrate complex workflows using chaining, specifically with LangChain Expression Language (LCEL).

As you progress, you’ll master building custom AI tools and defining agent behaviors to create smarter, task-driven AI applications. Through hands-on labs, you’ll implement both manual and built-in tool calling techniques, apply best practices for agent performance, and develop practical applications for data analysis, visualization, and database interaction. By the end, you’ll have the expertise to design and implement powerful AI agent solutions effectively.

This course is part of the IBM RAG and Agentic AI Professional Certificate , designed to provide you with the practical skills and knowledge to excel in developing advanced AI applications that leverage retrieval-augmented generation (RAG), multimodal AI, and agentic AI systems.

Prerequisites

Python programming skills and experience are essential for this course, as you will immediately start building AI agents. Additionally, familiarity with core AI concepts and the LangChain framework is highly recommended. Here are some recommended courses if you are not familiar with the prerequisites:

• Python for Data Science, AI & Development

• Develop Generative AI Applications: Get Started

Objectives

After completing this course, you will be able to:

• Develop AI agents that manually invoke tools for greater control over execution and reliability

• Implement tool calling and chaining to create structured AI workflows

• Use built-in LangChain agents to analyze data, generate visualizations, and execute database queries

• Apply best practices in prompt engineering and tool orchestration to enhance AI agent performance

Course Outline

This course consists of three modules.

Module 1: Foundations of Tool Calling and Chaining

• Lesson 0: Welcome

• Lesson 1: Introduction to AI Agents

• Lesson 2: Getting Started with Tool Calling

• Lesson 3: Building and Orchestrating Tools

• Lesson 4: Module Summary and Evaluation

Key Topics :

• Function Calling for LLMs

• AI Agents and Tool Orchestration

Module 2: LCEL and Manual Tool Calling in LangChain

• Lesson 1: Introduction to Chaining and LCEL Basics

• Lesson 2: Manual Tool Calling Basics

• Lesson 3: Parsing and Validating Tool Calls

• Lesson 4: Module Summary and Evaluation

Key Topics :

• LangChain Expression Language (LCEL)

• Manual Tool Invocation

• Structured Outputs for Tool Calls

• Parsing and Validating LLM Output

Module 3: Using Built-in Agents in LangChain

• Lesson 1: Natural Language Data Visualization

• Lesson 2: Conversational Database Access

• Lesson 3: Module Summary and Evaluation

• Lesson 4: Course Wrap-Up

Key Topics :

• Built-in LangChain Agents

• Natural Language to Data Visualizations

• AI-Powered SQL Agents

Tools/Software

In this course, you will explore and utilize a variety of tools and platforms, including:

• LangChain : To design and implement structured AI workflows, orchestrate large language models (LLMs) with external tools, and build intelligent agents.

• LangChain Expression Language (LCEL) : For building custom chains and flexible, production-ready AI workflows.

• Large Language Models (LLMs) : To experiment with different AI models, understand their capabilities, and integrate them into agent applications.

• LangGraph : To serve as an extension for building advanced agents with LangChain.

• Python : For coding AI applications, defining custom tools, integrating APIs, and implementing LangChain’s functionalities effectively.

• LangChain’s Built-in Agents (e.g., DataFrame Agent and SQL Agent) : For natural language data analysis, visualization, and conversational database access.

Tips for Success

• Stay organized and track key program milestones, risks, and stakeholder communications

• Consistently practice using the generative AI tools and techniques covered in the course

Congratulations on taking this step to elevate your career by mastering AI agent development! Enjoy your learning journey.