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URL: https://www.coursera.org/learn/fundamentals-of-building-ai-agents

⇱ Fundamentals of Building AI Agents | Coursera

Fundamentals of Building AI Agents

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Fundamentals of Building AI Agents

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4.6

133 reviews

Intermediate level

Recommended experience

Flexible schedule
1 week at 10 hours a week
Learn at your own pace
89%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.6

133 reviews

Intermediate level

Recommended experience

Flexible schedule
1 week at 10 hours a week
Learn at your own pace
89%
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

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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!

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 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
7 readings•Total 50 minutes
  • Course Overview•3 minutes
  • Comparing AI System Designs•5 minutes
  • When to (and not to) use AI Agents•5 minutes
  • Tools, Agents, and Function Calling in LangChain•10 minutes
  • Popular Built-in Tools in LangChain•5 minutes
  • Summary and Highlights: Foundations of Tool Calling and Chaining•2 minutes
  • Cheat Sheet: Foundations of Function Calling and Chaining •20 minutes
4 assignments•Total 50 minutes
  • Graded Quiz: Foundations of Tool Calling and Chaining•21 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
1 app item•Total 45 minutes
  • Lab: Build an AI Math Assistant with LangChain Tool Calling•45 minutes
1 plugin•Total 1 minute
  • Helpful Tips for Course Completion•1 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 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
3 readings•Total 12 minutes
  • Cheat Sheet: LangChain Expression Language (LCEL)•5 minutes
  • Structured Outputs for Tool Calling•5 minutes
  • Summary and Highlights: Introduction to Chaining and LCEL Basics•2 minutes
4 assignments•Total 56 minutes
  • Graded Quiz: Manual Tool Calling in LangChain•21 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
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
1 plugin•Total 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.

What's included

4 videos6 readings3 assignments2 app items

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
6 readings•Total 49 minutes
  • Bridging Language and Data: How AI Transforms Analytics•5 minutes
  • Natural Language Interfaces for Data Systems•10 minutes
  • Summary and Highlights: Using Built-in Agents in LangChain•2 minutes
  • Cheat Sheet: Using Built-in Agents in LangChain•20 minutes
  • Congratulations and Next Steps•10 minutes
  • Team and Acknowledgement•2 minutes
3 assignments•Total 51 minutes
  • Graded Quiz: Using Built-in Agents in LangChain•21 minutes
  • Practice Quiz: Natural Language Data Visualization•15 minutes
  • Practice Quiz: Conversational Database Access•15 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

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Instructors

Instructor ratings
4.6 (27 ratings)
IBM
37 Courses•2,497,133 learners

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Showing 3 of 133

DP
·

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.

HD
·

Reviewed on Apr 1, 2026

This course exceeded my expectations and proved to be an outstanding investment in my professional development

AC
·

Reviewed on Oct 1, 2025

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

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.

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.

Financial aid available,