Agents Course documentation
Q1: What is an Agent?
Unit 0. Welcome to the course
Live 1. How the course works and Q&A
Unit 1. Introduction to Agents
IntroductionWhat is an Agent?Quick Quiz 1What are LLMs?Messages and Special TokensWhat are Tools?Quick Quiz 2Understanding AI Agents through the Thought-Action-Observation CycleThought, Internal Reasoning and the Re-Act ApproachActions, Enabling the Agent to Engage with Its EnvironmentObserve, Integrating Feedback to Reflect and AdaptDummy Agent LibraryLet’s Create Our First Agent Using smolagentsUnit 1 Final QuizConclusion
Unit 2. Frameworks for AI Agents
Unit 2.1 The smolagents framework
Unit 2.2 The LlamaIndex framework
Unit 2.3 The LangGraph framework
Unit 3. Use Case for Agentic RAG
Unit 4. Final Project - Create, Test, and Certify Your Agent
Bonus Unit 1. Fine-tuning an LLM for Function-calling
Bonus Unit 2. Agent Observability and Evaluation
Bonus Unit 3. Agents in Games with Pokemon
Q1: What is an Agent?
Which of the following best describes an AI Agent?
Q2: What is the Role of Planning in an Agent?
Why does an Agent need to plan before taking an action?
Q3: How Do Tools Enhance an Agent’s Capabilities?
Why are tools essential for an Agent?
Q4: How Do Actions Differ from Tools?
What is the key difference between Actions and Tools?
Q5: What Role Do Large Language Models (LLMs) Play in Agents?
How do LLMs contribute to an Agent’s functionality?
Q6: Which of the Following Best Demonstrates an AI Agent?
Which real-world example best illustrates an AI Agent at work?
Congrats on finishing this Quiz 🥳! If you need to review any elements, take the time to revisit the chapter to reinforce your knowledge before diving deeper into the “Agent’s brain”: LLMs.
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