AI Agents Course: 100 Labs from MCP to Multi-Agent

About this course

This course contains the use of artificial intelligence.

We only charge a fee solely for the time invested in building this comprehensive curriculum.

Agentic AI Engineering is a hands-on course for Python developers who want to build AI agents that are reliable, observable, and safe to run in production. Across 100 labs you build agents with memory, MCP tools, multi-agent coordination, evaluation pipelines, and security controls, and you finish by designing a sovereign architecture as your capstone.

Anyone can connect an LLM to a tool and call it an agent. That is vibe coding: it works in the demo, then loops forever, forgets the user, overspends its budget, or acts on a bad decision with nobody watching. An agent engineer solves those problems on purpose. This course takes you from the first group to the second, one lab at a time.

How the course works

Each of the 100 labs is one focused build with a clear title that states what you make, such as a ReAct loop, an MCP server, a circuit breaker, or an agent card endpoint. You write the code, run it, and see it work. The teaching style is active and hands-on. Early modules favor strict, deterministic logic over hope, so every later system stands on a foundation you can test.

What you’ll work with

Python 3.12+, Pydantic, JSON Schema, and JSON-RPC 2.0ReAct loops, human-in-the-loop approval gates, and token budgets

Redis, Pinecone, ChromaDB, and SQLite for agent memory and retrieval

Model Context Protocol (MCP) with Claude Desktop, VS Code, stdio, and Streamable HTTPMulti-agent patterns: supervisor, hierarchical, peer-to-peer, voting, and shared-state locking

Confidential computing concepts: TEEs, remote attestation, confidential containers, and zero-knowledge proofs

The AP2 agent payments model: intent, cart, and payment mandates

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