Week 1: Introduction to Agentic AI

Agent definitions, ReAct paradigm, autonomous systems overview

Week 1 of 12

Learning Objectives

  • Define what constitutes an AI agent
  • Implement a basic ReAct agent from scratch
  • Understand the agent trajectory structure

Topics Covered

  • Agent definitions and taxonomy
  • ReAct paradigm (Yao et al., 2023)
  • Thought-Action-Observation loop
  • Agent vs. standard LLM inference

Resources

Jupyter Notebooks

Open First Agent in Colab First Agent

Required Readings

PaperAuthorsYearLink
ReAct: Synergizing Reasoning and Acting in Language Models Yao et al. 2023 arXiv
A Survey on Large Language Model based Autonomous Agents Wang et al. 2024 arXiv

Reading Guide: ReAct Paper

2-3 hours ReAct paradigm Reasoning traces Agent trajectories

Critical reading of the foundational ReAct paper introducing reasoning-acting paradigm

Primary Paper

ReAct: Synergizing Reasoning and Acting in Language Models
Yao, S., Zhao, J., Yu, D., et al. (2023)
ICLR 2023 arXiv

Exercise: Agent Classification

100 Points 3-5 hours Intermediate

Analyze and classify AI systems according to agent taxonomy

Learning Objectives

  • Analyze: Classify systems according to agent properties
  • Evaluate: Assess strengths and limitations of architectures
  • Apply: Apply formal definitions to real-world systems

Tasks

TaskPointsDescription
System Classification 40 Classify 6 systems (ChatGPT, Copilot, Roomba, etc.)
Architecture Analysis 35 Identify architecture types and memory/tool access
Design Proposal 25 Design a research assistant agent

Exercise

Build a basic ReAct agent from scratch that can:

  1. Answer questions using a simulated search tool
  2. Perform calculations using a calculator tool
  3. Show the complete thought-action-observation trajectory

Discussion Questions

  1. What distinguishes an AI agent from a standard LLM application?
  2. How does the ReAct paradigm improve over pure Chain-of-Thought?
  3. What are the limitations of current agent architectures?

Additional Resources

Discussion & Questions

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