Learn how to build AI agents that think, reason, and take meaningful action using LangChain’s tool calling and agent frameworks. This course introduces the foundations of AI agent design, comparing different agent architectures and showing when to use structured workflows, manual tool calling, or built-in agents. You’ll learn how to connect LLMs with external tools—calculators, APIs, data sources, and more—to extend model capabilities far beyond text generation.

After completing this course you will be able to:

Develop AI agents that can reason and perform tasks independently
Implement tool calling and chaining to create structured AI workflows
Utilize built-in LangChain agents to analyze data, generate visualizations, and execute database queries
Apply best practices in prompt engineering and tool calling to enhance AI agent performance
Design multi-step agent workflows using LangChain Expression Language
Build end-to-end agent applications that integrate external tools and real data sources

Hands-on labs guide you through building agents with LangChain Expression Language (LCEL), validating model outputs, orchestrating tool calls, and chaining multiple operations together. You’ll also explore pre-built DataFrame and SQL agents to perform data analysis, create visualizations, and execute natural language database queries.

By the end, you’ll be able to design robust, reliable agents that perform precise tasks while maintaining natural conversational flow. Whether you’re developing chatbots, assistants, or automation systems, you’ll gain the skills to engineer AI that can reason, act, and deliver results in real-world workflows.

The following skills are required to be successful with this course:

Python programming skills
Basic understanding of LangChain
Familiarity with core AI concepts

Course Syllabus

Welcome to the Course

Course Introduction
Course Overview
RAG and Agentic AI Professional Certificate Overview
Helpful Tips for Course Completion

Module 1: Foundations of Tool Calling and Chaining

Module Summary and Learning Objectives
Introduction to AI Agents
- What are AI Agents?
- Reading: Comparing AI System Designs
- Reading: When to (and not to) use AI Agents
- Practice Quiz: Introduction to AI Agents
Getting Started with Tool Calling
- Tool Calling for LLMs
- Why AI Needs Tools: From Guessing to Real-World Action
- Reading: Tools, Agents, and Function Calling in LangChain
- Practice Quiz: Getting Started with Tool Calling
Building and Orchestrating Tools
- Build Effective AI Tools for Advanced LLMs
- Build Intelligent Agents for Dynamic LLM Tool Use
- Build a Custom Math Toolkit Agent with LangChain
- Lab: Build an AI Math Assistant with LangChain Tool Calling
- Reading: Popular Built-in Tools in LangChain
- Practice Quiz: Building and Orchestrating Tools
Module Summary and Evaluation
- Summary and Highlights: Foundations of Tool Calling and Chaining
- Cheat Sheet: Foundations of Function Calling and Chaining
- Graded Quiz: Foundations of Tool Calling and Chaining

Module 2: LCEL and Manual Tool Calling in LangChain

Module Summary and Learning Objectives
Introduction to Chaining and LCEL Basics
- LangChain LCEL Chaining Method
- Lab: AI-Powered Data Analysis with LCEL
- Cheat Sheet: LangChain Expression Language (LCEL)
- Practice Quiz: Introduction to Chaining & LCEL Basics
Manual Tool Calling Basics
- When to Call Tools Manually
- Reading: Structured Outputs for Tool Calling
- Practice Quiz: Manual Tool Calling Basics
- Parsing and Validating Tool Calls
- Build LLM Agents with Tools
- Build Interactive LLM Agents
- Lab: Build Interactive LLM Agents with Tools
- Lab: Build a Tool Calling Agent
- Practice Quiz: Parsing and Validating Tool Calls
Module Summary and Evaluation
- Summary and Highlights: Introduction to Chaining and LCEL Basics
- Cheat Sheet: Manual Tool Calling in LangChain
- Graded Quiz: Manual Tool Calling in LangChain

Module 3: Using Built-in Agents in LangChain

Module Summary and Learning Objectives
Natural Language Data Visualization
- From Natural Language to Data Visualizations with LangChain
- Reading: Bridging Language and Data: How AI Transforms…
- Lab: Build Your Own Data Visualization Agent
- Practice Quiz: Natural Language Data Visualization
Conversational Database Access
- An Introduction to AI-Powered SQL Agents
- Implementing LangChain’s AI-Powered SQL Agent
- Reading: Natural Language Interfaces for Data Systems
- Lab: Build a Natural Language SQL Agent
- Practice Quiz: Conversational Database Access
Module Summary and Evaluation
- Summary and Highlights: Using Built-in Agents in LangChain
- Cheat Sheet: Using Built-in Agents in LangChain
- Graded Quiz: Using Built-in Agents in LangChain

Course Wrap-Up

Course Wrap-Up
Congratulations and Next Steps
Team and Acknowledgement

Agentic AI: Developing AI Agents

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