Udemy

The AI Agent Bootcamp 2026: Complete AI Аgent Course

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  • 4,844 Students
  • Updated 2/2026
4.5
(411 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
11 Hour(s) 45 Minute(s)
Language
English
Taught by
365 Careers
Rating
4.5
(411 Ratings)
1 views

Course Overview

The AI Agent Bootcamp 2026: Complete AI Аgent Course

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.


Course Content

  • 40 section(s)
  • 216 lecture(s)
  • Section 1 Intro to AI Agents: Understanding AI agents
  • Section 2 Intro to AI Agents: Essential ingredients for building AI agents
  • Section 3 Intro to AI Agents: Types of AI agents: from simple to complex structures
  • Section 4 Intro to AI Agents: Guiding and teaching AI agents
  • Section 5 Intro to AI Agents: AI agent architecture patterns
  • Section 6 Intro to AI Agents: Implementing AI agents in practice
  • Section 7 Practical example n8n: Build an agentic automation with n8n
  • Section 8 Intro to AI Agents: AI agent infrastructure
  • Section 9 Intro to AI Agents: AI agents in business
  • Section 10 AI Agents Architecture Module: Intro
  • Section 11 AI Agents Architecture Module: Foundations of Agentic AI
  • Section 12 AI Agents Architecture Module: Prompting for Agentic Systems
  • Section 13 AI Agents Architecture Module: Agentic Workflows
  • Section 14 AI Agents Architecture Module: Single-Agent Architecture Patterns
  • Section 15 AI Agents Architecture Module: Planning and Decomposition
  • Section 16 AI Agents Architecture Module: Multi-Agent Architectures
  • Section 17 AI Agents Architecture Module: Execution, Performance, and Reliability
  • Section 18 AI Agents Architecture Module: Memory Systems
  • Section 19 AI Agents Architecture Module: Oversight and Control
  • Section 20 AI Agents Architecture Module: Governance and Safety
  • Section 21 AI Agents Architecture Module: Evaluation and Benchmarking
  • Section 22 LangChain Module: Introduction to LangChain
  • Section 23 LangChain Module: Tokens, Models, and Prices
  • Section 24 LangChain Module: Setting Up the Environment
  • Section 25 LangChain Module: The OpenAI API
  • Section 26 LangChain Module: Model Inputs
  • Section 27 LangChain Module: Output Parsers
  • Section 28 LangChain Module: LangChain Expression Language (LCEL)
  • Section 29 LangChain Module: Retrieval Augmented Generation (RAG)
  • Section 30 LangGraph Module: Introduction
  • Section 31 LangGraph Module: Setting Up the Environment
  • Section 32 LangGraph Module: Graph Components and Implementation
  • Section 33 LangGraph Module: Message Management
  • Section 34 LangGraph Module: Thread-Level Persistence
  • Section 35 LangGraph Module: Conclusion
  • Section 36 Agents in Practice Module: Introduction to the Course
  • Section 37 Agents in Practice Module: Agentic Systems in Practice
  • Section 38 Agents in Practice Module: Project 1 - Job-Helper agent (ReAct)
  • Section 39 Agents in Practice Module: Project 2 - ReWOO Job-Helper agent
  • Section 40 Agents in Practice Module: Project 3 - Business-Idea Evaluator

What You’ll Learn

  • 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


Reviews

  • V
    Vijayalakshmi Gnanapranavan
    3.5

    useful

  • S
    Sagar Sudam Khatale
    2.0

    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.

  • F
    Fernando Zepeda
    4.0

    Its a good match, a little difficult to keep up with the terminology but really interesting

  • R
    Rohit Goyal
    5.0

    the langchain part is nice and the instructure make it clear for the new to langchain and python (from different language java for me)

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