The course is divided into 4 units. These will take you from the basics of agents to a final assignment with a benchmark.
Unit
Topic
Description
0
Welcome to the Course
Welcome, guidelines, necessary tools, and course overview.
1
Introduction to Agents
Definition of agents, LLMs, model family tree, and special tokens.
1 Bonus
Fine-tuning an LLM for Function-calling
Learn how to fine-tune an LLM for Function-Calling
2
Frameworks for AI Agents
Overview of smolagents
, LangGraph
and LlamaIndex
.
2.1
The Smolagents Framework
Learn how to build effective agents using the smolagents
library, a lightweight framework for creating capable AI agents.
2.2
The LlamaIndex Framework
Learn how to build LLM-powered agents over your data using indexes and workflows using the LlamaIndex
toolkit.
2.3
The LangGraph Framework
Learn how to build production-ready applications using the LangGraph
framework giving you control tools over the flow of your agent.
2 Bonus
Observability and Evaluation
Learn how to trace and evaluate your agents.
3
Use Case for Agentic RAG
Learn how to use Agentic RAG to help agents respond to different use cases using various frameworks.
4
Final Project - Create, Test and Certify Your Agent
Automated evaluation of agents and leaderboard with student results.
3 Bonus
Agents in Games with Pokemon
Explore the exciting intersection of AI Agents and games.