AI Agents Crash Course: Build with Python & OpenAI

Learn to build agentic AI solutions using Python, OpenAI SDK, tools, memory, RAG, and guardrails - in just four hours!

What you'll learn
- Build functional AI agents using Python and the OpenAI SDK
- Implement tool calling, memory, and streaming responses in your agents
- Use prompt engineering and context engineering to control agent behavior
- Integrate Retrieval-Augmented Generation (RAG) using embedding databases
- Enforce safety with guardrails and prompt adherence techniques
- Orchestrate multi-agent systems with task decomposition and hand-offs
- Deploy AI agents to the cloud with authentication and secure setup
- Trace and debug agent behavior using OpenAI’s built-in tools

Requirements
- Basic experience with Python programming
- An OpenAI account with API access
- A GitHub account to use GitHub Codespaces for development
- Familiarity with basic coding tools (e.g., running scripts, editing code)

Description
Building intelligent AI agents can feel overwhelming. Between OpenAI’s complex SDK, retrieval-augmented generation (RAG), tool-calling, memory, and prompt engineering, it’s hard to know where to start.

Thiscrash course is your shortcut: injust a few hours, you'll go from zero to deploying your own functional, real-world agentic AI system.

You’ll build a smart nutrition assistant that:

Uses OpenAI’s Agents SDK to understand and respond to prompts

Calls external tools and APIs

Leverages memory and RAG for contextual intelligence

Includes guardrails to behave safely and reliably

Can be deployed to the cloud with authentication

Whether you're a developer, data scientist, or AI-curious engineer, this hands-on course gives you a completeend-to-end agentic AI foundation– without getting buried in theory or outdated code.

What You’ll Learn

How to build AI agents withPython + OpenAI’s Agents SDK

Tool calling, streaming, and tracing techniques

Best practices inprompt engineeringand context design

How to integratememoryandRAGfor deeper contextual reasoning

Deploying your agent securely withauthenticationandguardrails

How to build multi-agent systems with task delegation and parallel execution

Who This Course is For

Engineers and developers withbasic Python experience

AI/ML professionals looking to quickly learnagent orchestration

Product builders and technical leads exploringagentic workflows

Learners who want tobuild, not just read about agents

About the Instructors

Your instructors combinedeep industry experiencewith a passion for clear, actionable teaching.

Frank Kanespent 9 years at Amazon and IMDb, where he built large-scale recommender systems and led engineering teams. He holds 17 patents in machine learning and distributed systems and has taughtover 1 million studentsthrough his company, Sundog Education.

Zoltan C. Tothbrings over two decades of experience in AI infrastructure and data systems. As a former principal instructor and Solutions Architect Databricks and Data Engineering lead at startups, he’s helped companies around the world scale their analytics and AI platforms. Zoltan also teaches AI and data engineering at the Central European University.

Together, Frank and Zoltan guide you step-by-step through building agents the right way: with real code, real tools, and production-ready techniques.

Ready to build your first AI agent,fast?

Enroll now and start building today.

Who this course is for:
- Learners who prefer a project-based, fast-paced crash course over lengthy theory
- Python developers who want to quickly get hands-on with building AI agents
- Software engineers and technical leads looking to integrate AI agents into real-world products
- Entrepreneurs, indie hackers, and product builders interested in agentic AI workflows
- Anyone curious about how to use the OpenAI SDK to build smart, autonomous applications
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