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。

114 条评论

中级等级

推荐体验

灵活的计划

1 周在 10 小时一周

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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!

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.

涵盖的内容

8个视频2篇阅读材料4个作业1个应用程序项目6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

2篇阅读材料总计5分钟

Course Overview3分钟
Summary and Highlights: Foundations of Tool Calling and Chaining2分钟

4个作业总计50分钟

Graded Quiz: Foundations of Tool Calling and Chaining21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

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

6个插件总计45分钟

Helpful Tips for Course Completion1分钟
Reading: Comparing AI System Designs5分钟
Reading: When to (and not to) use AI Agents5分钟
Reading: Tools, Agents, and Function Calling in LangChain9分钟
Reading: Popular Built-in Tools in LangChain 5分钟
Cheat Sheet: Foundations of Function Calling and Chaining 20分钟

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.

涵盖的内容

4个视频1篇阅读材料4个作业3个应用程序项目3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

1篇阅读材料总计2分钟

Summary and Highlights: Introduction to Chaining and LCEL Basics2分钟

4个作业总计56分钟

Graded Quiz: Manual Tool Calling in LangChain21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

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

3个插件总计30分钟

Cheat Sheet: LangChain Expression Language (LCEL)5分钟
Reading: Structured Outputs for Tool Calling5分钟
Cheat Sheet: Manual Tool Calling in LangChain 20分钟

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.