Introduction to Agentic AI

AI agents are the next evolution of software systems. Instead of writing explicit instructions for every scenario, you give an LLM the ability to reason about a task, use tools to interact with the world, and iterate until the task is complete. This is a fundamental shift: from deterministic programs to systems that decide what to do at runtime.

This course teaches developers the concepts, architectures, and trade-offs behind agentic AI systems. You will learn how LLMs work under the hood, how the agent loop turns a chatbot into an autonomous problem-solver, how memory and retrieval give agents persistent knowledge, and how to design agent systems that are safe, observable, and cost-effective in production.

No frameworks. No framework lock-in. The course focuses on patterns and principles that apply regardless of which LLM provider or tooling you use.

Course Structure

1. LLM Foundations — 3 lessons
2. The Agent Paradigm — 4 lessons
3. Reasoning and Planning — 3 lessons
4. Memory and Knowledge — 4 lessons
5. Agent Architectures — 4 lessons
6. Safety and Reliability — 3 lessons
7. Production Engineering — 4 lessons
8. Real-World Agent Patterns — 2 lessons