Who is this course suitable for?

This course is designed for AI developers, data scientists, and software engineers interested in building intelligent agents using LangChain. Whether you’re a beginner or have prior experience with AI, the course offers foundational knowledge in Agentic AI and LangChain ecosystem, making it accessible even without a programming background.

What will I learn in this course?

Throughout the course, you will learn to create intelligent agents using LangChain. You’ll dive into prompt engineering, context engineering, and the use of LCEL for building robust workflows. Topics also include working with LLMs (Large Language Models), creating and enhancing agents with memory, and integrating external tools into your agents for increased functionality. By the end of the course, you'll be well-equipped to design complex agents and workflows.

What tools and technologies will be used in this course?

The course covers LangChain, Gemini, LCEL, Python, and tools like Pydantic and TypedDict. These tools will be used to help you build agents, create structured outputs, and enhance model behavior in various applications.

Do I need any prior experience with LangChain or AI?

No prior programming experience is required. This course is designed for beginners and intermediate learners. We will walk you through each step, from understanding the core concepts of Agentic AI and LangChain to building your first intelligent agent. The course provides everything you need, including explanations of programming concepts and hands-on coding exercises.

Will I get hands-on practice with LangChain and AI tools?

Absolutely! This course is built around hands-on demos, coding exercises, and practice assignments. You will work with LangChain, Gemini, LCEL, and other tools to create real-world applications like intelligent agents, workflows, and error-resilient systems. The course is designed to ensure that you gain practical experience throughout.

How long will it take to complete the course?

The course is structured to be completed in 4 weeks with a recommended study pace of 3–4 hours per week. You can work at your own pace, revisiting content as needed. This flexibility allows you to balance your learning with your professional schedule.

Will I receive a certificate upon completion?

Yes, after successfully completing all the modules, assignments, and the final project, you will receive a Certificate of Completion. This certificate validates your skills in LangChain development, Agentic AI, and intelligent agent design, enhancing your professional profile.

What makes this course different from other AI courses?

This course stands out by focusing specifically on Agentic AI and the LangChain framework, two powerful tools for building intelligent agents. Unlike other AI courses, it emphasizes practical, hands-on learning through real-world applications such as building agents, creating automated workflows, and integrating external tools like Gemini and @tool for added functionality.

What career opportunities can this course lead to?

Upon completion of this course, you will be ready for roles such as AI Developer, Machine Learning Engineer, Intelligent Systems Architect, and Automation Specialist. This course will prepare you to build AI-powered agents, automate workflows, and implement intelligent systems in various industries, opening doors to career advancement in the growing AI and software development fields.

Is this course suitable for someone with no prior experience in AI?

Yes, this course is designed for both beginners and intermediate learners. It provides foundational knowledge in Agentic AI and LangChain, so even if you are new to AI, you will gain the skills needed to build intelligent agents from scratch. The course ensures that no prior knowledge is necessary to get started.

When will I have access to the lectures and assignments?

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.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, 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.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Building Your First AI Agent with LangChain

本课程是 Agentic AI Engineering 专项课程的一部分

位教师：Edureka

2,499 人已注册

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Define core principles of Agentic AI and LangChain ecosystem, including architecture and components.
Apply LangChain frameworks to set up AI environments and build intelligent agents.
Analyze prompt engineering, context design, and LCEL workflows to optimize agent behavior.
Design and evaluate multi-step agent workflows, integrating external tools to solve real-world tasks.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Agentic AI Engineering 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

This program introduces you to Building Simple Agents with LangChain, designed for developers and AI enthusiasts seeking to create intelligent agents powered by LangChain. You’ll begin by mastering the foundational concepts of Agentic AI and the LangChain ecosystem, including understanding its architecture, key components, and capabilities.

Next, you’ll dive into LLM development, focusing on prompting, context engineering, and persona design. You’ll learn to create effective prompts, engineer context to guide model behavior, and design powerful, multi-step workflows using LangChain Expression Language (LCEL). Through hands-on demonstrations, you'll build and optimize intelligent agent systems that can interact with various data sources and tools. As you progress, you’ll explore practical agent development with create_agent, and understand how to enhance agents with memory and external tools. You’ll also learn to produce structured outputs with Pydantic and TypedDict, ensuring that your agents can handle complex tasks with precision. By the end of the program, you will be able to: - Define the core principles of Agentic AI and the LangChain ecosystem. - Apply LangChain’s create_agent framework to build and customize intelligent agents. - Analyze prompt engineering and context engineering techniques to influence model behavior. - Design multi-step workflows and error-resilient pipelines using LangChain Expression Language. - Integrate external tools and synthesize structured outputs for solving complex tasks. - Optimize agents to handle real-world applications, from querying data to generating actionable insights. This program is ideal for developers, AI enthusiasts, and technical professionals looking to dive into the world of intelligent agent development. Prior experience with Python programming and basic AI concepts will help maximize your learning experience. Learners need a reliable internet connection, a modern web browser, and access to Python tools. The course uses AI tools like LangChain and Gemini API, which don't require specialized hardware. Basic knowledge of Python and AI concepts is recommended. Join us and learn to build powerful, responsive agents that can automate tasks, optimize workflows, and unlock new capabilities in AI-driven applications.

单元详情

Learn the fundamentals of agentic AI and how it differs from traditional prompt-based systems. Explore how autonomous agents reason, plan, and act, and examine real-world use cases where agentic systems are applied. Gain an understanding of the LangChain v1.0 ecosystem, its core components, and architecture. Build a solid technical foundation by setting up a modern AI development environment with API access and virtual environments, preparing you for hands-on agent development.

涵盖的内容

11个视频7篇阅读材料4个作业

11个视频总计59分钟

Specialization Introduction6分钟
Course Introduction5分钟
Introduction to Agentic AI6分钟
Core Concepts of Agentic AI7分钟
Real-World Agentic AI Use Cases5分钟
What is LangChain v1.0?5分钟
LangChain Architecture Deep Dive6分钟
Key Components and Capabilities of LangChain5分钟
Preparing a Modern AI Development Environment4分钟
Demonstration: Gemini API Key Setup with AI Studio3分钟
Demonstration: Setting up Virtual Environment and Configuring API Keys7分钟

7篇阅读材料总计100分钟

Course Syllabus15分钟
Agentic AI Systems: A Practical Overview15分钟
Architectural Patterns for Autonomous and Collaborative AI Agents15分钟
LangChain Evolution: From Early Releases to v1.015分钟
LangChain v1.0: System Architecture and Design15分钟
Setting Up a Reliable AI Development Environment 15分钟
Module Summary: Getting Started with Agentic AI an the LangChain Ecosystem10分钟

4个作业总计33分钟

Knowledge Check: Getting Started with Agentic AI and the LangChain Ecosystem15分钟
Practice Assignment: Introduction to Agentic AI6分钟
Practice Assignment: LangChain v1.0 Ecosystem6分钟
Practice Assignment: Setting Up Your AI Development Environment6分钟

Discover how to work effectively with large language models using LangChain. Learn prompt engineering best practices, structured prompting techniques, and how context and persona design influence model behavior. Explore LangChain Expression Language (LCEL) to build modular, multi-step, and error-resilient workflows. Develop practical skills to design reusable pipelines that replace fragile, monolithic prompts with maintainable LLM workflows.

涵盖的内容

22个视频5篇阅读材料5个作业

22个视频总计131分钟

How LLMs Work in LangChain6分钟
Comparing Leading LLM Providers7分钟
Best Practices for Choosing the Right Model6分钟
Demonstration: Building a Gemini-Powered CLI Tool6分钟
Principles of Effective Prompt Engineering7分钟
Core Prompting Techniques7分钟
Designing Structured and Reliable Inputs5分钟
Demonstration: Prompt Creation using LangChain's Prompt Templates7分钟
Demonstration: Mastering Prompt Engineering with LangChain - I7分钟
Demonstration: Mastering Prompt Engineering with LangChain - II3分钟
Introduction to Context Engineering6分钟
Types of Context in LLM-driven Applications6分钟
Demonstration: Enhancing Model Responses with Context Engineering7分钟
Demonstration: Tech Persona Context Injection using LangChain - I5分钟
Demonstration: Tech Persona Context Injection using LangChain - II7分钟
Building Pipelines Using LCEL5分钟
Advanced LCEL Workflow Patterns4分钟
Demonstration: Constructing Chains with LCEL7分钟
Demonstration: Designing Multi-Step LCEL Workflows - I6分钟
Demonstration: Designing Multi-Step LCEL Workflows - II7分钟
Demonstration: Implementing Error-Resilient LCEL Pipelines - I4分钟
Demonstration: Implementing Error-Resilient LCEL Pipelines - II6分钟

5篇阅读材料总计70分钟

Optimizing LLM Provider Selection for Scalable and Cost-Efficient AI15分钟
Best Practices in Prompt Engineering15分钟
Designing Effective Context for Reliable LLM Outputs15分钟
Designing Modular Workflows with LCEL15分钟
Module Summary: Applied LLM Development: Prompting, Context Engineering and LCEL10分钟

5个作业总计39分钟

Knowledge Check: Applied LLM Development: Prompting, Context Engineering and LCEL15分钟
Practice Assignment: Working with Large Language Models6分钟
Practice Assignment: Prompt Engineering Fundamentals6分钟
Practice Assignment: Context Engineering and Persona Design6分钟
Practice Assignment: LangChain Expression Language (LCEL) Workflows6分钟

Learn how to build intelligent agents using LangChain’s create_agent framework. Explore core agent architecture patterns, multi-step reasoning, and memory integration for conversational continuity. Gain hands-on experience creating and integrating tools, and producing reliable, validated structured outputs using Pydantic and TypedDict. Build practical skills to design agents that reason, act, and interact with external systems.

涵盖的内容

11个视频3篇阅读材料3个作业

11个视频总计66分钟

Understanding the create_agent Framework6分钟
Core Patterns in Agent Architecture6分钟
Demonstration: Building Your First LangChain Agent - I5分钟
Demonstration: Building Your First LangChain Agent - II6分钟
Demonstration: Enhancing Agents with Memory7分钟
Building and Using Tools in LangChain7分钟
Structured Outputs with Pydantic and TypedDict7分钟
Demonstration: Creating Tools with @tool7分钟
Demonstration: Integrating External Tools into Your Agent5分钟
Demonstration: Producing Validated Structured Outputs - I6分钟
Demonstration: Producing Validated Structured Outputs - II4分钟

3篇阅读材料总计40分钟

Advanced Considerations for Structured Output in create_agent15分钟
Tool Design Principles for Scalable Agent Workflows15分钟
Module Summary: Practical Agent Development with LangChain10分钟

3个作业总计27分钟

Knowledge Check: Practical Agent Development with LangChain15分钟
Practice Assignment: Building Agents with create_agent6分钟
Practice Assignment: Tools and Structured Output in LangChain6分钟

Consolidate your learning across the entire course and reflect on your growth in agentic AI and LangChain development. Apply your skills in a hands-on practice project, building a beginner intelligent agent that combines prompting, workflows, tools, and memory. Complete a graded end-of-course assessment to demonstrate your ability to design and reason about agent-based AI systems and prepare for more advanced agentic applications.