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Welcome to Session 3 of our 5-week bootcamp.
This session will focus on giving our agents memory and tools so they can do more than simply generate responses. We’ll explore how to maintain information across interactions, create and call tools, and give agents access to multiple capabilities within a single workflow.

By the end of the session, you’ll have a stronger foundation for building agents that can maintain context, take action, and interact with external functionality.

What We'll Cover
Session 1: Agent Foundations — September 10

  • Effective Prompting
  • Understanding Tokens
  • Models and Instructions
  • Building Your First Agent

Session 2: Controlling Agent Behavior — September 17

  • Pydantic Fundamentals
  • Context and Runtime Data
  • Dynamic Instructions
  • Structured Outputs

Session 3: Memory and Tools — September 24

  • Memory and Context
  • Creating Tools
  • Tool Calling
  • Using Multiple Tools

Session 4: Building More Capable Agents — October 1

  • Single-Agent Capstone
  • Model Context Protocol (MCP)
  • Connecting Agents to MCP Tools
  • Guardrails

Session 5: Multi-Agent Systems — October 8

  • Multi-Agent Design Patterns
  • Handoffs and Agents as Tools
  • Multi-Agent Development
  • Multi-Agent Capstone

Prerequisites: A basic understanding of Python is recommended. You do not need prior experience building AI agents or working with the OpenAI Agents SDK. Familiarity with Python classes, functions, type hints, and basic API usage will help you get the most out of the bootcamp.

You should also create and securely save an OpenAI API key that you'll use throughout the bootcamp to run examples, build agents, and interact with OpenAI models from your Python code. Make sure you can access your key before the session begins.

We hope you join us!

AGENDA

  • 6:00 p.m. – Welcome
  • 6:05 p.m. – Session 2 Review
  • 6:10 p.m. – Memory and Context
  • 6:40 p.m. – Introduction to Tools
  • 7:00 p.m.BREAK
  • 7:10 p.m. – Creating Tools
  • 7:50 p.m.BREAK
  • 8:00 p.m. – Tool Calling
  • 8:40 p.m. – Questions/Discussion
  • 9:00 p.m. – End

Related topics

Artificial Intelligence
Machine Learning
Data Science
Data Science using Python
Python

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