Fundamentals of Building AI Agents

Overview

Are you ready to build AI that thinks, acts, and gets things done? In this course, you’ll learn how to design agents that go beyond language generation to reason, take action, and tackle real-world tasks using tools and data.  During the course, you'll explore the foundations of tool calling and chaining with LangChain. You’ll discover how to extend the capabilities of Large Language Models (LLMs) by connecting them with calculators, code, and external data sources. You'll learn how LLMs trigger tool use through LangChain Expression Language (LCEL) and look at manual tool calling for greater control and accuracy. Plus, you’ll explore built-in agents that can analyze data, create visualizations, and run SQL queries using natural language.  To get the most from this course, we recommend that you have Python programming skills, a basic understanding of LangChain, and familiarity with core AI concepts.  Whether you're building a chatbot or a smart assistant, if you’re looking to build the skills to create dynamic, intelligent, and goal-oriented AI systems, enroll today!

Syllabus

Foundations of Tool Calling and Chaining

In this module, you'll discover what AI Agents are, compare AI agent system designs, and learn when, and when not, to use them. You'll learn how tool calling and chaining work together in LangChain to create powerful AI systems. You'll learn to connect language models with external tools and gain skills you can use to build systems that perform precise operations while maintaining natural conversational abilities.

LCEL and Manual Tool Calling in LangChain

In this module, you'll orchestrate components through structured workflows using LangChain Expression Language (LCEL). You’ll learn how to manually invoke tools in LangChain by parsing large language model (LLM) outputs, validating inputs, and executing functions. You’ll build a real-world tool calling agent that includes workflows where the LLM suggests tools while you retain full control for processing the query.

Using Built-in Agents in LangChain

In this module, you'll learn how to use LangChain's built-in DataFrame and SQL agents for data analysis and database operations. Discover how these pre-built agents implement natural language interfaces for conversational data analysis, making insights available to users without technical expertise. You'll learn how to build AI-driven applications that convert conversational queries into structured data operations, enhancing usability and decision-making.