AI agents are everywhere, but how do you go from demo to dependable? In this talk, we’ll build agents using LangGraph, and take it beyond toy examples into enterprise-grade readiness.

We’ll start with context engineering concepts like: control flow, prompt engineering, prompt management and tool calling. From there, we’ll build an agent step-by-step, enabling contextual reasoning via Retrieval-Augmented Generation (RAG), and integrating tool use for dynamic task execution. We'll also cover agent observability with open source observability tools such as LangFuse to bring full observability into the decision our agents make.

Whether you're exploring AI agents or planning your first production deployment, you'll leave this session with the patterns, tools, and clarity to do it right.

Evals are the difference between building a demo and a product you can actually use. This talk is a little story about building a new class of product at StackOne. A front door for AI agents into the HR tech ecosystem. I'll talk about building workflows, agentic rag and how to make sure it doesn't all break all the time.

Join me on the RAG (Retrieval Augmented Generation) road. In this talk we start from the basics, we will go through embeddings and vector databases. Then finally i'll show you a great use case for a RAG application that has a React front end and utilizes the capabilities of generative AI.