AI Agent Engineering — 38 Lessons
From beginner to production-ready AI agents: tools and function calling, memory and RAG, planning, multi-agent teams, safety, testing, deployment and monitoring — explained simply.
What Is an AI Agent? — From Chatbot to Autonomous AI
Lesson 1: what makes an AI system an 'agent', and how agents go far beyond a simple chatbot.
GPT-6, Astra & AGI — Has Artificial General Intelligence Arrived?
Lesson 2: the newest generation of AI models, what AGI really means, and why it matters for agent builders.
From Beginner to Production-Ready Agents in 30 Days
Lesson 3: the full course roadmap — what you will build each week and how the pieces fit together.
How Large Language Models Think — Tokens, Context & Reasoning
Lesson 4: what happens inside the model that powers every agent.
The Anatomy of an Agent — Model, Tools, Memory & Guardrails
Lesson 5: the four building blocks every production agent needs.
Prompt Engineering for Agents — Instructions That Actually Work
Lesson 6: writing system prompts that make agents reliable, not just clever.
Tool Use & Function Calling — Giving Your Agent Hands
Lesson 7: how agents call real functions — search, databases, email and your own APIs.
Memory for Agents — Short-Term, Long-Term & Vector Search
Lesson 8: how agents remember conversations, facts and files.
RAG for Agents — Searching Knowledge Before Acting
Lesson 9: retrieval-augmented generation — the pattern that grounds agents in real documents.
Planning & Reasoning — Teaching Agents to Think in Steps
Lesson 10: task decomposition, chain-of-thought and self-checking.
Structured Output — JSON, Schemas & Reliable Answers
Lesson 11: making agents return data your software can trust.
The Model Context Protocol (MCP) — A Universal Tool Plug
Lesson 12: the open standard that lets any agent use any tool.
Human-in-the-Loop — Approvals, Reviews & Safe Autonomy
Lesson 13: deciding which actions an agent may take alone and which need your yes.
Multi-Agent Systems — Teams of Specialists
Lesson 14: why one giant agent often loses to a small team of focused agents.
Orchestration Patterns — Router, Pipeline & Supervisor
Lesson 15: the three classic ways to organise agent teamwork.
Building Your First Agent — A Complete Walkthrough
Lesson 16: from blank page to a working research assistant agent.
Testing Agents — Evals, Golden Sets & Regression Checks
Lesson 17: how to prove your agent works — and keep proving it after every change.
Debugging Agent Failures — Reading Traces Like a Detective
Lesson 18: finding why an agent went wrong using its step-by-step trace.
Safety & Security — Prompt Injection, Leaks & Abuse
Lesson 19: protecting agents from malicious inputs and costly mistakes.
Cost & Performance — Tokens, Caching & Model Choice
Lesson 20: making agents fast and affordable without making them dumb.
Deploying Agents — APIs, Queues & Schedules
Lesson 21: moving your agent from a laptop experiment to a real service.
Monitoring Agents in Production — Logs, Alerts & Dashboards
Lesson 22: watching your agent's health after launch.
Real-World Case Study — A Customer Support Agent
Lesson 23: anatomy of a production agent that answers customer questions safely.
Real-World Case Study — A Research & Report Agent
Lesson 24: how a multi-step agent turns a question into a sourced report.
Voice & Multimodal Agents — Agents That See, Hear and Speak
Lesson 25: adding ears, eyes and a voice to your agents.
Agents with Code — Sandboxes, Data Analysis & Automation
Lesson 26: letting agents write and run code safely.
The Agentic Web — Agents That Browse, Book & Buy
Lesson 27: agents that act on websites on your behalf — and the new rules this creates.
Capstone — Design Your Own Production Agent
Lesson 28: a guided blueprint for planning an agent you could really ship.
Careers in Agent Engineering — Skills, Portfolio & Interviews
Lesson 29: turning agent skills into real opportunities.
Course Finale — Your Agent Engineering Roadmap
Lesson 30: the whole course on one page — and your next twelve months as an agent builder.
Python for AI Agents — Start from Zero
Python for AI Agents — Start from Zero: Python connects agent decisions to real functions and data.
APIs & JSON — Connect Agents to the Real World
APIs & JSON — Connect Agents to the Real World: APIs let programs exchange information; JSON gives that information a structured shape.
Embeddings — Meaning as Numbers
Embeddings — Meaning as Numbers: Embeddings represent content as lists of numbers used to compare meaning.
Document AI — Reading PDFs & Documents
Document AI — Reading PDFs & Documents: Document AI extracts information from files so an agent can work with their contents.
ReAct — Reason, Act & Observe
ReAct — Reason, Act & Observe: A ReAct workflow alternates reasoning, tool actions and observations.
LangChain & LangGraph — Agent Workflows
LangChain & LangGraph — Agent Workflows: Workflow libraries help connect model calls, tools and state into an agent application.
CrewAI — Build an AI Team
CrewAI — Build an AI Team: A team of agents divides a task among roles with defined responsibilities.
AI Agent Engineering Certificate
Finish all 38 AI Agent Engineering lessons and pass the exam with 70% or higher to earn your AI Agent Engineering certificate.
Take the AI Agent Exam