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.
Fundamentals of Building AI Agents
Fundamentals of Building AI Agents
This course is part of IBM RAG and Agentic AI Professional Certificate
Instructors: Joseph Santarcangelo
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
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
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
June 2025
Assessments
11 assignments
Taught in English
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This course is part of the IBM RAG and Agentic AI Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- 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 from IBM
There are 3 modules in this course
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
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
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
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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.
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