M01
Understanding AI
Let me start with what AI is not.
Course summary
AI Agents
Use this page to revisit what each stage gives you and return to the exact weak point that needs another pass.
Stage 1 of 6
Foundations
No technical experience required. Build your understanding from the ground up.
M02
From LLMs to agents
A Large Language Model (LLM) is like an incredibly well-read assistant who has consumed most of human knowledge available on the internet.
M03
Your computer's command line
I know the command line can look intimidating.
M04
Setting up your environment
Python is the language of AI development.
M05
Your first AI interaction
Let us have your first conversation with a local AI model.
Stage 2 of 6
Core concepts
Build deep understanding of how AI agents work under the hood.
M01
How AI agents think
ReAct stands for reasoning and acting .
M02
Tools and actions
Tools are functions that agents can call to interact with the world.
M03
Memory and context
Short-Term Memory.
M04
Design patterns
For complex tasks, planning before acting often works better than interleaved reasoning.
M05
Architecture fundamentals
State is everything your agent needs to remember to complete its task.
Stage 3 of 6
Practical building
Hands-on implementation of real-world agent systems.
M01
Building your first agent
Let us build a production-ready single agent step by step.
M02
Multi-agent systems
In practice, when tasks cross more than a couple of domains, a single agent often degrades quickly.
M03
Workflow automation with n8n
n8n (pronounced "n-eight-n") is a workflow automation platform that lets you connect different apps and services.
M04
Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open protocol for connecting AI clients to external tools and data sources.
M05
Integration and APIs
Computer use is moving from demo territory into real workflows, but the safe lesson is not which model tops which benchmark.
Stage 4 of 6
Security and ethics
Critical understanding of AI security threats and responsible deployment.
M01
The threat landscape
AI agents face unique security challenges that traditional software does not.
M02
Secure implementation
Every piece of data that enters your agent system is a potential attack vector.
M03
Ethics and responsible AI
AI agents inherit biases from their training data, their developers, and their deployment context.
Stage 5 of 6
Advanced mastery
Expert-level techniques for production AI systems.
M01
Fine-tuning open source models
My opinion is that fine tuning is only worth it when you can name the win you want, the risk you accept, and the test you will run before anyone depends on it.
M02
Enterprise architectures
Enterprise architecture is where good agent ideas get messy.
M03
Production deployment
Production is not just running code.
M04
Research frontiers
This module is about judgement.
Stage 6 of 6
Capstone and certification
Demonstrate mastery through a comprehensive project.
M01
Capstone project build and evidence pack
Your capstone project is to design, build, and document a complete AI agent system that solves a real-world problem.
M02
Peer review and certification readiness
Before receiving your certification, you will review another learner's project and receive feedback on yours.
M03
Architecture challenge simulation
Before your final assessment, test your architectural decision-making skills with this professional simulation game.