What roles or career paths are relevant for someone with AI agent development skills?

<text variant="body1">AI agent development skills are valuable for roles such as Software Developers, Data Scientists, Machine Learning Engineers, AI Developers, AI Engineers, and Automation Specialists. <text variant="body1">These positions involve building intelligent systems that use language models to interact with tools, run code, and automate real-world workflows. These are skills that are increasingly in demand across tech-driven industries.

Do I need prior machine learning experience to build AI agents?

Not at all! If you're already familiar with Python, you're all set. This course teaches you how to create AI agents using LangChain. You won’t need an advanced machine learning background to build real-world, action-oriented AI systems.

How is building AI agents different from traditional software development?

Building AI agents goes beyond writing fixed application logic. It focuses on creating intelligent systems that can reason, make decisions, and take action by calling external tools, executing code, and interacting with data. While you still use Python and frameworks like LangChain, the approach includes designing structured workflows using function calling, chaining, and tool orchestration. This enables your applications to respond intelligently and perform tasks autonomously, offering far more flexibility than traditional software.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Fundamentals of Building AI Agents

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Fundamentals of Building AI Agents

This course is part of multiple programs.

Instructors: Joseph Santarcangelo

44,331 already enrolled

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

122 reviews

Intermediate level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

90%

Most learners liked this course

3 modules

Gain insight into a topic and learn the fundamentals.

122 reviews

Intermediate level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

90%

Most learners liked this course

What you'll learn

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

11 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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!

Module details

This module introduces AI agents and explains how they differ from traditional large language model workflows. You will explore how agents use reasoning, tools, and memory to perform multi-step tasks and real-world interactions. The module also covers tool calling and chaining in LangChain, including how to design and integrate custom and pre-built tools. Through hands-on practice, you will begin building AI agents capable of executing structured, goal-oriented workflows.

What's included

8 videos7 readings4 assignments1 app item1 plugin

8 videosTotal 53 minutes

Course Introduction3 minutes
RAG and Agentic AI Professional Certificate Overview6 minutes
What are AI Agents?12 minutes
Tool Calling for LLMs5 minutes
Why AI Needs Tools: From Guessing to Real-World Action5 minutes
Build Effective AI Tools for Advanced LLMs8 minutes
Build Intelligent Agents for Dynamic LLM Tool Use8 minutes
Build a Custom Math Toolkit Agent with LangChain6 minutes

7 readingsTotal 50 minutes

Course Overview3 minutes
Comparing AI System Designs5 minutes
When to (and not to) use AI Agents5 minutes
Tools, Agents, and Function Calling in LangChain10 minutes
Popular Built-in Tools in LangChain5 minutes
Summary and Highlights: Foundations of Tool Calling and Chaining2 minutes
Cheat Sheet: Foundations of Function Calling and Chaining 20 minutes

4 assignmentsTotal 50 minutes

Practice Quiz: Introduction to AI Agents9 minutes
Practice Quiz: Getting Started with Tool Calling10 minutes
Practice Quiz: Building and Orchestrating Tools10 minutes
Graded Quiz: Foundations of Tool Calling and Chaining21 minutes

1 app itemTotal 45 minutes

Lab: Build an AI Math Assistant with LangChain Tool Calling45 minutes

1 pluginTotal 1 minute

Helpful Tips for Course Completion1 minute

This module focuses on building structured workflows using LangChain Expression Language (LCEL) and implementing manual tool calling for greater control. You will learn how to construct chains, extract tool inputs from LLM outputs, and validate and execute tool calls effectively. The module also explores how to bind custom tools to models and manage tool invocation for accuracy, safety, and cost efficiency. Through labs, you will develop agents that combine automated reasoning with controlled execution.

What's included

4 videos3 readings4 assignments3 app items1 plugin

4 videosTotal 19 minutes

LangChain LCEL Chaining Method5 minutes
When to Call Tools Manually4 minutes
Build LLM Agents with Tools 5 minutes
Build Interactive LLM Agents6 minutes

3 readingsTotal 12 minutes

Cheat Sheet: LangChain Expression Language (LCEL)5 minutes
Structured Outputs for Tool Calling5 minutes
Summary and Highlights: Introduction to Chaining and LCEL Basics2 minutes

4 assignmentsTotal 56 minutes

Practice Quiz: Introduction to Chaining & LCEL Basics10 minutes
Practice Quiz: Manual Tool Calling Basics 10 minutes
Practice Quiz: Parsing and Validating Tool Calls15 minutes
Graded Quiz: Manual Tool Calling in LangChain21 minutes

3 app itemsTotal 165 minutes

Lab: AI Powered Data Analysis with LCEL45 minutes
Lab: Build Interactive LLM Agents with Tools60 minutes
Lab: Build a Tool Calling Agent60 minutes

1 pluginTotal 20 minutes

Cheat Sheet: Manual Tool Calling in LangChain 20 minutes

This module explores the use of pre-built agents in LangChain for data analysis and database interactions. You will learn how to configure and use DataFrame and SQL agents to process natural language queries and generate insights. The module also demonstrates how these agents translate conversational input into structured operations for visualization and data retrieval. Through hands-on labs, you will build AI-powered applications that enable intuitive interaction with data systems.