Hello, and welcome to this course on AI agents.
The demand for AI agent developers is growing fast as more businesses turn to intelligent
systems that can reason, take action, and solve problems in real time.
From customer support chatbots that handle complex queries, to research assistants that
fetch and summarize data, to legal assistants that analyze case law, medical agents that
provide diagnostic suggestions, and tools that generate visualizations or write reports,
AI agents are becoming a critical part of modern applications.
This rising need has opened up exciting opportunities for developers who know how to connect language
models with tools, data sources, and logic.
Companies are actively seeking talent that can go beyond basic prompts and build smart,
task-driven solutions.
Now is the perfect time to get started.
This hands-on course is designed for aspiring software engineers, data scientists, machine
learning engineers, AI architects, automation engineers, or someone in a related role.
Python programming skills and experience are essential for this course, as you will immediately
start building AI agents.
Familiarity with core AI concepts and the chain framework is highly recommended.
This course begins with a detailed look at AI agents, covering what they are, how they
differ from traditional workflows, and when to best deploy them.
You'll be provided with a foundation in tool calling and chaining using LangChain.
You'll explore why language models need tools and how function calling increases precision.
Through guided videos and readings, you'll build a math assistant by converting the functions
into tools and orchestrating them with LangChain.
You'll also be introduced to LangChain's built-in agents.
Module 2 introduces you to LCEL, or LangChain Expression Language, a concise, chain-first
syntax that simplifies the creation of modular AI workflows.
You'll learn when and how to manually invoke tools based on LLM outputs, and how to parse
and validate these calls for structured execution.
In a hands-on lab, you'll build a tool calling agent to automate U2 operations.
In Module 3, the course focuses on building with LangChain's built-in agents.
You'll learn how to create natural language data visualizations and build a conversational
agent that queries SQL databases using plain English.
With detailed readings and walkthroughs, you'll implement two agents, one for data visualization
and another for conversational database access, using LangChain's built-in components.
To get the most from this course, make sure you watch the videos, go through each reading,
check your understanding with practice quizzes, perform the hands-on labs, and test your knowledge
with the graded assessment.
Thank you and all the best with this course!