What you'll learn:
- Master the core mechanics of LLMs, including tokenization, context windows, and advanced temperature sampling for predictable model outputs.
- Integrate LLMs with external tools and APIs to transform static chat models into dynamic systems capable of performing real-world actions.
- Architect autonomous agents using the ReAct framework, implementing memory and reasoning loops to solve complex, multi-step problems.
- Design and deploy multi-agent systems and safety patterns using industry-standard frameworks to build production-ready AI assistants.
AI can feel overwhelming. This course is designed to make it practical, approachable, and fun.
In this hands-on course, you’ll start from the fundamentals of AI and large language models, then work your way up to building real AI agents from scratch. You’ll learn how models like ChatGPT actually work, including transformers, tokens, context windows, temperature, prompting, and API-based development. From there, you’ll move into the architecture of AI systems, where you’ll explore tools, tool calling, workflows, and the key difference between workflows and true agents.
The course goes beyond theory. You’ll build and experiment with real agents inside guided lab environments, without worrying about infrastructure setup, API keys, or unexpected cloud costs. Everything is provided so you can stay focused on learning and building.
Throughout the course, you’ll create four AI agents: Zippy, Savvy, Meshy, and Cody. You’ll see how a simple single agent evolves into a multi-agent system, with specialized agents for research, memory, and coding. You’ll also learn core implementation patterns such as memory, planning, reasoning, error handling, and multi-agent orchestration, along with coding agent patterns inspired by production systems used by leading AI companies.
By the end of this course, you’ll know how to design, build, test, and deploy effective AI agents with confidence.
