AI Agents For Beginners Tutorial 2026: Build Your First Agentic AI System

What is AI Agents For Beginners?

AI Agents For Beginners is an open-source educational project from Microsoft that teaches developers how to build autonomous AI agents from scratch. It solves the problem of getting started with agentic AI—a paradigm where language models take actions, make decisions, and iterate toward goals rather than simply generating text.

What is AI Agents For Beginners?

This repository contains 18 structured lessons delivered as Jupyter notebooks, designed to take you from zero to building real AI agent systems. Whether you're a solo founder exploring agentic workflows or a developer integrating agents into existing applications, this project provides hands-on, reproducible examples using frameworks like AutoGen and Foundry. The curriculum progresses from basic concepts (what agents are, how they reason) through advanced patterns (multi-agent collaboration, retrieval-augmented generation with agents, and local deployment).

Key Features

• 18 progressive lessons structured from foundational to advanced topics, each in its own Jupyter notebook
• Framework agnostic foundation with examples using AutoGen, Foundry, and local-first approaches
• Agentic RAG patterns showing how to combine retrieval systems with agent decision-making
• Multi-agent orchestration examples demonstrating how agents coordinate and collaborate
• Local deployment options via Foundry Local, enabling privacy-conscious development
• Production-ready patterns covering error handling, tool use, memory management, and observability
• Runnable code in every notebook—no pseudo-code or incomplete examples

Getting Started

Prerequisites

Before you begin, ensure you have:

• Python 3.10 or higher
• Jupyter Lab or Jupyter Notebook installed
• An API key for your LLM provider (OpenAI, Azure OpenAI, or similar)
• Git for cloning the repository

Installation Steps

1. Clone the repository:
   

   git clone https://github.com/microsoft/ai-agents-for-beginners.git
   cd ai-agents-for-beginners

2. Create a Python virtual environment:
   

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:
   

   pip install -r requirements.txt

4. Configure your API credentials by creating a .env file in the project root:
   

   OPENAI_API_KEY=your_api_key_here
   AZURE_OPENAI_ENDPOINT=your_endpoint_here
   AZURE_OPENAI_API_KEY=your_key_here

5. Launch Jupyter and open the first lesson notebook:
   

   jupyter lab

Your First Agent

After completing setup, open lesson-01-intro-to-agents.ipynb. You'll encounter code similar to this:

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent(
    name="assistant",
    llm_config={"model": "gpt-4", "api_key": "your-key"}
)

user_proxy = UserProxyAgent(name="user", human_input_mode="NEVER")

user_proxy.initiate_chat(
    assistant,
    message="What are the top 3 best practices for building AI agents?"
)

This creates a basic two-agent conversation loop. The assistant answers questions, and the user proxy can evaluate responses or request refinements. From here, lessons build toward tool calling, planning, and multi-agent systems.

When to Use It

Use Case 1: Customer Support Automation

Founders building customer service platforms benefit from learning agentic patterns. Rather than fixed decision trees, agents can interpret customer requests, look up account information, check knowledge bases, and decide whether to escalate—all dynamically. The agentic-RAG lessons show exactly how to combine retrieval systems with decision logic.

Use Case 2: Multi-Step Business Workflows

Enterprises need agents that can handle workflows spanning multiple systems. Approve a purchase order, update inventory, send notifications, and log the transaction—all as one coherent agent flow. This course teaches coordination patterns and tool integration that make such workflows reliable.

Use Case 3: Local AI Development

Teams with data sensitivity or offline requirements use the Foundry-Local lessons to build agents that run on private infrastructure. The curriculum explicitly covers this deployment model, a growing concern for regulated industries.

Who It's Best For

This project is ideal for AI developers transitioning from prompt engineering to agentic systems, technical founders evaluating agent-based product architectures, and teams needing standardized patterns for consistency. If you've built chatbots and are ready for the next frontier, this is your starting point.

Takeaway

AI Agents For Beginners fills a real gap: agentic AI is too new for most traditional documentation, yet too important to ignore. Microsoft's 18-lesson structure, combined with working code and progressive complexity, makes it the fastest way to ship your first agent. The open-source nature and accompanying project site mean the material stays current. If you're serious about building with agents, start here.