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. This crash course is your shortcut: in just 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this hands-on course gives you a complete end-to-end agentic AI foundation — without getting buried in theory or outdated code.
What You’ll Learn:
- How to build AI agents with Python + OpenAI’s Agents SDK
- Tool calling, streaming, and tracing techniques
- Best practices in prompt engineering and context design
- How to integrate memory and RAG for deeper contextual reasoning
- Deploying your agent securely with authentication and guardrails
- How to build multi-agent systems with task delegation and parallel execution
Who This Course is For:
- Engineers and developers with basic Python experience
- AI/ML professionals looking to quickly learn agent orchestration
- Product builders and technical leads exploring agentic workflows
- Learners who want to build, not just read about agents
About the Instructors: Your instructors combine deep industry experience with a passion for clear, actionable teaching. Frank Kane spent 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 taught over 1 million students through his company, Sundog Education.
