Context Engineering for AI Agents

As AI applications move beyond simple prompts, context has become one of the most important factors in performance, reliability, and user experience. From chatbots to autonomous agents, how you manage and retrieve context can make the difference between helpful systems and frustrating ones.

In this code-along webinar, Nyah Macklin, a Senior Developer Advocate for AI at Neo4j, will show you how to engineer context effectively for AI applications. You’ll explore multiple context management approaches—including prompt management, retrieval-augmented generation (RAG), and knowledge graphs—and learn how to use Neo4j as a memory layer for AI chatbots and agents. By the end, you’ll have practical patterns you can apply directly to your own AI systems.

Session Pre-Requisites

Ahead of time please create:

1. Github Account (please create ahead of time if you don't already have one). This sometimes includes installing 2-factor authentication which I believe is now mandatory for github accounts. Doing this ahead of time is imperative. 

2. GraphAcademy Account (Click Login/Signup)

We will do most development inside Github Codespaces (a sandboxed environment) so you will not need a separate IDE.