課程資料
課程簡介
Complete AI Agent Engineer Training: AI Agent Architecture, n8n, LangChain, RAG, LangGraph, LangSmith, ReAct, ReWOO
The Problem
Agentic AI is the future of AI-powered organizations. It helps businesses innovate faster than ever before. Therefore, it’s not surprise that the demand for AI Agent Engineers has been surging in the job marketplace.
Supply, however, has been minimal, and acquiring the skills necessary to be hired as an AI Agent Engineer can be challenging.
So, how is this achievable?
Universities have been slow to develop specialized programs focused on practical AI agent engineering skills. The few attempts that exist are expensive and time-consuming. At the same time, most online courses offer high-level walkthroughs of individual techniques for building agentic systems, yet integrating these skills remains challenging.
The Solution
AI agent engineering is a multidisciplinary field covering:
AI agent foundations
AI agent design and architecture
Python programming
Working with low-code automation platforms like n8n
AI agent optimization for speed and cost
Connecting agents to tools, memory, and APIs with LangChain
Model AI agent workflows with LangGraph
AI agent evaluation with LangSmith
Applying agents to real-world problems
Launching and optimizing agents in production
Each topic builds on the previous one, and skipping steps can lead to confusion. For instance, optimizing agent performance without a fundamental understanding of agent architecture is rarely achievable.
So, we created the AI Agent Engineer Bootcamp 2026 to provide the most effective, time-efficient, and structured AI agent training available online.
This pioneering training program overcomes the most significant barrier to entering the AI agent field by consolidating all essential resources in one place.
Our course is designed to teach interconnected topics seamlessly—providing all you need to become an AI agent engineer at a significantly lower cost and time investment than traditional programs.
The Skills
1. Intro to AI Agents
Decision-making logic, actuators, updated environment, single agents, multi-agents, guardarails—there are familiar AI agent buzzwords; what exactly do they mean?
Why study AI agent basics?
Build a solid foundation that will support your learning journey. Understand the big picture and how different building blocks fit together.
2. AI Agent Architecture
We build AI agents to solve problems. Each problem requires the right architecture and an understanding of the trade-offs involved.
Why study AI agent architecture?
The system design choices you will make will determine how effective and efficient your AI agents are. By mastering classic AI agent architecture you will be able to make confident choices at the system design stage—before problems become costly to fix.
3. Building AI Applications with LangChain
LangChain is a framework that allows for seamless development of AI-driven applications by chaining interoperable components.
Why study LangChain?
Learn how to create agents that can reason. LangChain facilitates the creation of systems where individual pieces—such as language models, databases, and reasoning algorithms—can be interconnected to enhance overall agent functionality.
4. LangGraph
LangGraph sets the foundation of how we can build and scale AI workloads. Use this tool to design agents that reliably handle complex tasks.
Why study LangGraph?
With LangGraph you will be introduced to multi-step agent orchestration. This is where you learn how to add conversational memory to your agent, so it learns to remember, adapt, and grow smarter with every interaction.
5. AI Agents in Practice
Step into the world of AI agents with this practical module on agentic systems. You will gain real-world experience. From prompt design and multi-step reasoning to safety techniques and LangSmith monitoring.
Why study AI Agents in Practice?
Gain the practical skills to build production-ready AI workflows. Take the next step in your AI journey with hands-on projects.
What You Get
$1,250 AI agent engineering training program
Active Q&A support
Essential skills for AI engineering employment
AI learner community access
Completion certificate
Real-world business case solutions for job readiness
We're excited to help you become an AI Agent Engineer from scratch—offering an unconditional 30-day full money-back guarantee.
With excellent course content and no risk involved, we're confident you'll love it.
Why delay? Each day is a lost opportunity. Click the ‘Buy Now’ button and join our AI Agent Engineer program today.
課程章節
- 40 個章節
- 216 堂課
- 第 1 章 Intro to AI Agents: Understanding AI agents
- 第 2 章 Intro to AI Agents: Essential ingredients for building AI agents
- 第 3 章 Intro to AI Agents: Types of AI agents: from simple to complex structures
- 第 4 章 Intro to AI Agents: Guiding and teaching AI agents
- 第 5 章 Intro to AI Agents: AI agent architecture patterns
- 第 6 章 Intro to AI Agents: Implementing AI agents in practice
- 第 7 章 Practical example n8n: Build an agentic automation with n8n
- 第 8 章 Intro to AI Agents: AI agent infrastructure
- 第 9 章 Intro to AI Agents: AI agents in business
- 第 10 章 AI Agents Architecture Module: Intro
- 第 11 章 AI Agents Architecture Module: Foundations of Agentic AI
- 第 12 章 AI Agents Architecture Module: Prompting for Agentic Systems
- 第 13 章 AI Agents Architecture Module: Agentic Workflows
- 第 14 章 AI Agents Architecture Module: Single-Agent Architecture Patterns
- 第 15 章 AI Agents Architecture Module: Planning and Decomposition
- 第 16 章 AI Agents Architecture Module: Multi-Agent Architectures
- 第 17 章 AI Agents Architecture Module: Execution, Performance, and Reliability
- 第 18 章 AI Agents Architecture Module: Memory Systems
- 第 19 章 AI Agents Architecture Module: Oversight and Control
- 第 20 章 AI Agents Architecture Module: Governance and Safety
- 第 21 章 AI Agents Architecture Module: Evaluation and Benchmarking
- 第 22 章 LangChain Module: Introduction to LangChain
- 第 23 章 LangChain Module: Tokens, Models, and Prices
- 第 24 章 LangChain Module: Setting Up the Environment
- 第 25 章 LangChain Module: The OpenAI API
- 第 26 章 LangChain Module: Model Inputs
- 第 27 章 LangChain Module: Output Parsers
- 第 28 章 LangChain Module: LangChain Expression Language (LCEL)
- 第 29 章 LangChain Module: Retrieval Augmented Generation (RAG)
- 第 30 章 LangGraph Module: Introduction
- 第 31 章 LangGraph Module: Setting Up the Environment
- 第 32 章 LangGraph Module: Graph Components and Implementation
- 第 33 章 LangGraph Module: Message Management
- 第 34 章 LangGraph Module: Thread-Level Persistence
- 第 35 章 LangGraph Module: Conclusion
- 第 36 章 Agents in Practice Module: Introduction to the Course
- 第 37 章 Agents in Practice Module: Agentic Systems in Practice
- 第 38 章 Agents in Practice Module: Project 1 - Job-Helper agent (ReAct)
- 第 39 章 Agents in Practice Module: Project 2 - ReWOO Job-Helper agent
- 第 40 章 Agents in Practice Module: Project 3 - Business-Idea Evaluator
課程內容
- The course provides the entire toolbox you need to become an AI Agent Engineer, Understand key AI agent concepts and build a solid foundation, Impress interviewers by showing an understanding of AI agents, Apply your skills to real-life business cases, Harness the power of AI agents, Leverage LangChain for seamless development of AI-driven applications by chaining interoperable components, Model AI agent workflows with LangGraph, Evaluate AI agents with LangSmith, Build single and multi-agent systems
此課程所涵蓋的技能
評價
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VVijayalakshmi Gnanapranavan
useful
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SSagar Sudam Khatale
Now almost last chapter (52) from section 9 but still I feel, general discussion going on. By the time we could have started actual AI related things. Somehow I feel that info till now is repetitive. Not a single hands-on activity till now. I will update the review as we proceed further.
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FFernando Zepeda
Its a good match, a little difficult to keep up with the terminology but really interesting
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RRohit Goyal
the langchain part is nice and the instructure make it clear for the new to langchain and python (from different language java for me)