Curious how to bring agentic AI to life using Python? Want to see how Semantic Kernel can orchestrate real-world AI behavior?

In this hands-on session, we’ll walk through the code necessary to build a Python-based product Q&A chatbot that goes beyond basic RAG implementations. You’ll see how to give your AI agent the ability to:

Pull product data from a vector store
Enhance RAG answers with live web search
Evaluate hallucination risk
Respond with a trust score for transparency

We’ll explore how Semantic Kernel enables agentic behavior; and, along the way, you’ll learn how to ground responses in multiple data sources and implement an evaluation loop to keep your AI honest.

If you're a developer looking to move from experimentation to real-world AI applications, this session will give you the tools, patterns, and confidence to build smarter, more reliable agents in Python.