This course is a practice-oriented program that goes beyond simple chatbots to build multi-agent systems where multiple agents collaborate. 👉 You will learn three frameworks—Swarm, LangGraph, and DeepAgents—step-by-step, completing six practical projects including a customer support chatbot, blog content creation, a PostgreSQL query agent, and an autonomous research agent. 👉 By implementing enterprise-grade features such as long-term memory using Redis and PostgreSQL, Human-in-the-loop security mechanisms, and cost monitoring, you will be able to build production-level systems that can be applied to real-world services immediately after the course.
53 learners are taking this course
Level Basic
Course period Unlimited
Swarm Agents: Designing systems where multiple agents collaborate dynamically
LangGraph: Visualize and control complex workflows as graphs
DeepAgents: Implementing Automated Agent Orchestration
Long Term Memory: Building Persistent Storage Using Redis and PostgreSQL
Human-in-the-loop: Approval/rejection/modification mechanisms for sensitive tasks
Performance Monitoring: Track and optimize token usage, costs, and execution time
Complex AI agent collaboration, now with clear design!
This course goes beyond simple chatbots to focus on building multi-agent systems where multiple AI agents
collaborate organically.
Have you seen customer support chatbots collaborating with multiple agents to solve problems faster and more accurately?
Imagine a process where AI agents collaborate on their own to automate blog posting, from content planning to writing and publishing.
How about maximizing your work efficiency by delegating repetitive tasks, such as writing PostgreSQL queries and conducting complex research, to AI agents?
Does building an AI agent feel vague to you?
Through this course, master Swarm, LangGraph, and Deep Agents,
build production-level systems yourself, and
experience an amazing transformation.
Going beyond simple chatbot development,
we will complete production-level systems that work directly in the field,
helping you leap forward as an 'AI Agent Expert'.
You will be able to design and implement numerous AI agent building experiences on your own.
Swarm, LangGraph, and Deep Agents—you will learn these three core frameworks step-by-step, gaining the ability to design and build complex multi-agent systems yourself. Beyond simply calling APIs, you will understand how agents collaborate and be able to construct optimal architectures.
From customer support chatbots to autonomous research agents, you will personally complete 6 practical projects.
Beyond just learning theory, you will strengthen your practical skills by completing six diverse projects that can be immediately applied to real-world services. You will gain experience building AI agents necessary for solving actual business problems, such as customer support chatbots, blog content creation, PostgreSQL query agents, and autonomous research agents.
You will fully master the core technologies for building enterprise-grade AI systems.
Redis and PostgreSQL will be used to implement long-term memory, human-in-the-loop security mechanisms, and cost monitoring—essential advanced features for real-world service operations. Through this, you will grow into an expert capable of building production-level, stable, and scalable AI systems immediately after completing the course.
This course teaches you how to build complex multi-agent systems step-by-step using the Swarm, LangGraph, and Deep Agents frameworks. From customer support chatbots to autonomous research agents, you will complete production-level systems that can be immediately applied to real-world services through six practical projects.
Customer support chatbots, blog content creation, PostgreSQL query agents, and autonomous research agents, you will build six real-world projects to develop your skills in designing and implementing multi-agent systems using LangGraph, Swarm, and Deep Agents.
You will strengthen your capabilities in operating AI agents in production environments by implementing enterprise-grade features required for actual services, such as long-term memory, Human-in-the-loop security, and cost monitoring using Redis and PostgreSQL.
Section 1
👉 In this section, we will proceed with installing Python, configuring the virtual environment, setting up VS Code, and installing the essential extensions required for the course exercises.
👉 Build a smooth development environment through project dependency management and environment variable configuration.
Section 2
👉 Design and implement a system where multiple agents collaborate dynamically using the Swarm framework.
👉 Learn efficient interaction and task distribution between agents through Tool Calling Agents, Active Agent Routers, and more.
Section 3
👉 Learn how to visualize and control complex agent workflows as graphs using LangGraph.
👉 Covers advanced agent orchestration through tool integration, hierarchical agents, supervisor agents, and the implementation of specialized and autonomous agents.
Section 4
👉 Learn automated agent orchestration methods through the Deep Agents framework.
👉 It covers core features for building enterprise-grade systems, from Quick Start to Subagents, Backend configuration, Long Term Memory, Human-in-the-loop security mechanisms, and Middleware.
Those who feel uncertain about how to integrate AI features into existing web services,
and those who have used LLMs but are looking for specific methodologies on how to design multi-agent architectures.
Those who understand the potential of AI agents but face technical limitations during actual service implementation,
and lack confidence in specific implementation methods or the feasibility of applying enterprise-grade features.
Those who have experience in model training but feel apprehensive about actual service deployment and operation,
and wish to learn practical know-how for building AI systems that operate reliably in production environments.
Practice Environment
Operating System: Windows 10 or higher recommended
Essential Tools: Python 3.13 or higher, VS Code, Docker
Recommended Specifications: 8GB RAM or more, 50GB or more SSD storage space
Prerequisite Knowledge and Important Notes
Essential understanding of basic Python syntax and object-oriented programming.
Recommended to learn basic concepts of LLM and AI Agents.
Applied Knowledge
Learning Materials
Lecture notes PDF and practice code provided
Example project source code and GitHub repository
References and additional learning material links provided
Who is this course right for?
Those who have built a web/app but don't know how to integrate AI features
Those who have used LLMs but find multi-agent architectures confusing
Those who have trained a model but are afraid of production deployment
Someone who plans AI features without knowing the technical limitations.
Need to know before starting?
Basic knowledge of Python is required.
Experience using LangChain or ChatGPT API is required.
805
Learners
21
Reviews
3
Answers
4.9
Rating
4
Courses
Hello, I am Gyeongwon Cho, your instructor.
I have built extensive practical experience across various industrial environments, from SMEs to large corporations, in fields such as web development, artificial intelligence (AI), and AWS infrastructure construction.
Based on this experience, I have been conducting offline lectures in the field of AI since 2022, providing education that bridges the gap between practical application and theory.
All
20 lectures ∙ (7hr 14min)
Course Materials:
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