Fundamentals of Building AI Agents

Fundamentals of Building AI Agents

本课程是多个项目的一部分。

位教师：Joseph Santarcangelo

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3个模块

深入了解一个主题并学习基础知识。

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3个模块

深入了解一个主题并学习基础知识。

116 条评论

中级等级

推荐体验

灵活的计划

1 周在 10 小时一周

自行安排学习进度

91%

大多数学生喜欢此课程

您将学到什么

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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作业

11 项作业

授课语言：英语（English）

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该课程共有3个模块

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.

涵盖的内容

8个视频7篇阅读材料4个作业1个应用程序项目1个插件

8个视频总计53分钟

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

7篇阅读材料总计50分钟

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

4个作业总计50分钟

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

1个应用程序项目总计45分钟

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

1个插件总计1分钟

Helpful Tips for Course Completion1分钟

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.

涵盖的内容

4个视频3篇阅读材料4个作业3个应用程序项目1个插件

4个视频总计19分钟

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

3篇阅读材料总计12分钟

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

4个作业总计56分钟

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

3个应用程序项目总计165分钟

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

1个插件总计20分钟

Cheat Sheet: Manual Tool Calling in LangChain 20分钟

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.