Master AI Agents: 36 Hands-On Chapters from ReAct to Production

Tired of scattered tutorials? The 36-chapter agent-study course is your all-in-one, hands-on path to mastering AI agents. It covers everything from the foundational ReAct loop to advanced protocols like MCP and A2A, RAG, DSPy, and production observability—all as runnable Python files.

What problem does it solve?
Most AI agent resources are either too theoretical or too fragmented. This course bridges the gap with practical, interview-focused examples. You don't just read about agents—you build and break them.

Quick example: Building a ReAct agent

# agent-study chapter 2: Simple ReAct loop
import json

def react_agent(query):
    # Step 1: Thought
    thought = f"I need to answer: {query}"
    # Step 2: Action (mock tool call)
    action = {"tool": "search", "input": query}
    # Step 3: Observation
    observation = "Mock result: Agent frameworks are powerful."
    # Step 4: Final answer
    return f"Final: {observation}"

print(react_agent("What is an AI agent?"))

Why is it interesting?

• Production-ready: Includes observability (tracing, metrics) so you can deploy with confidence.
• Protocols: Learn MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication—cutting-edge standards.
• RAG + DSPy: Combine retrieval-augmented generation with DSPy's programmatic prompting for robust systems.
• Interview-focused: Each chapter ends with real-world questions and exercises.

How to start?

1. Clone the repo from the link below.
2. Pick any chapter (e.g., 01_react_loop.py).
3. Run it: python chapter_01.py
4. Modify and experiment.

Get the full course here: agent-study