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.

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Fundamentals of Building AI Agents

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Fundamentals of Building AI Agents

This course is part of multiple programs.

Instructors: Joseph Santarcangelo

34,670 already enrolled

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3 modules

Gain insight into a topic and learn the fundamentals.

111 reviews

Intermediate level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

91%

Most learners liked this course

3 modules

Gain insight into a topic and learn the fundamentals.

111 reviews

Intermediate level

Recommended experience

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

91%

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

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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!

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.

What's included

8 videos2 readings4 assignments1 app item6 plugins

8 videos Total 53 minutes

Course Introduction 3 minutes
RAG and Agentic AI Professional Certificate Overview 6 minutes
What are AI Agents? 12 minutes
Tool Calling for LLMs 5 minutes
Why AI Needs Tools: From Guessing to Real-World Action 5 minutes
Build Effective AI Tools for Advanced LLMs 8 minutes
Build Intelligent Agents for Dynamic LLM Tool Use 8 minutes
Build a Custom Math Toolkit Agent with LangChain 6 minutes

2 readings Total 5 minutes

Course Overview 3 minutes
Summary and Highlights: Foundations of Tool Calling and Chaining 2 minutes

4 assignments Total 50 minutes

Practice Quiz: Introduction to AI Agents 9 minutes
Practice Quiz: Getting Started with Tool Calling 10 minutes
Practice Quiz: Building and Orchestrating Tools 10 minutes
Graded Quiz: Foundations of Tool Calling and Chaining 21 minutes

1 app item Total 45 minutes

Lab: Build an AI Math Assistant with LangChain Tool Calling 45 minutes

6 plugins Total 45 minutes

Helpful Tips for Course Completion 1 minute
Reading: Comparing AI System Designs 5 minutes
Reading: When to (and not to) use AI Agents 5 minutes
Reading: Tools, Agents, and Function Calling in LangChain 9 minutes
Reading: Popular Built-in Tools in LangChain 5 minutes
Cheat Sheet: Foundations of Function Calling and Chaining 20 minutes

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.

What's included

4 videos1 reading4 assignments3 app items3 plugins

4 videos Total 19 minutes

LangChain LCEL Chaining Method 5 minutes
When to Call Tools Manually 4 minutes
Build LLM Agents with Tools 5 minutes
Build Interactive LLM Agents 6 minutes

1 reading Total 2 minutes

Summary and Highlights: Introduction to Chaining and LCEL Basics 2 minutes

4 assignments Total 56 minutes

Practice Quiz: Introduction to Chaining & LCEL Basics 10 minutes
Practice Quiz: Manual Tool Calling Basics 10 minutes
Practice Quiz: Parsing and Validating Tool Calls 15 minutes
Graded Quiz: Manual Tool Calling in LangChain 21 minutes

3 app items Total 165 minutes

Lab: AI Powered Data Analysis with LCEL 45 minutes
Lab: Build Interactive LLM Agents with Tools 60 minutes
Lab: Build a Tool Calling Agent 60 minutes

3 plugins Total 30 minutes

Cheat Sheet: LangChain Expression Language (LCEL) 5 minutes
Reading: Structured Outputs for Tool Calling 5 minutes
Cheat Sheet: Manual Tool Calling in LangChain 20 minutes

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.

What's included

4 videos3 readings3 assignments2 app items3 plugins

4 videos Total 24 minutes

From Natural Language to Data Visualizations with LangChain 8 minutes
An Introduction to AI-Powered SQL Agents 3 minutes
Implementing LangChain’s AI-Powered SQL Agent 8 minutes
Course Wrap-Up 5 minutes

3 readings Total 14 minutes

Summary and Highlights: Using Built-in Agents in LangChain 2 minutes
Congratulations and Next Steps 10 minutes
Team and Acknowledgement 2 minutes

3 assignments Total 51 minutes

Practice Quiz: Natural Language Data Visualization 15 minutes
Practice Quiz: Conversational Database Access 15 minutes
Graded Quiz: Using Built-in Agents in LangChain 21 minutes

2 app items Total 90 minutes

Lab: Build Your Own Data Visualization Agent 30 minutes
Lab: Build a Natural Language SQL Agent 60 minutes

3 plugins Total 35 minutes

Reading: Bridging Language and Data: How AI Transforms Analytics 5 minutes
Reading: Natural Language Interfaces for Data Systems 10 minutes
Cheat Sheet: Using Built-in Agents in LangChain 20 minutes

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Instructors

Instructor ratings

(23 ratings)

Joseph Santarcangelo

IBM

37 Courses 2,402,063 learners

Offered by

IBM

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Reviewed on Oct 7, 2025

It was made easy and lab launch added so much of hands-on knowledge. Only if you could make it available for life-time access.

Reviewed on Oct 1, 2025

It provides enough general and specific knowledge to create complex AI Agents. A great entry point to Agentic AI

Reviewed on Oct 11, 2025

Greate course . Helped me see more practical options when developing the Agentic workflow

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Frequently asked questions

AI agent development skills are valuable for roles such as Software Developers, Data Scientists, Machine Learning Engineers, AI Developers, AI Engineers, and Automation Specialists.

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.

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.

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.

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.