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Context Engineering for AI Agents

Nyah Macklin, Senior Developer Advocate for AI at Neo4j, will show you how to engineer context effectively for AI applications.

Nyah Macklin

2026년 3월 17일

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Google로 계속하기계속 진행하시면 다음 사항에 동의하는 것으로 간주됩니다: 이용약관 및 개인정보 처리방침 그리고 귀하의 데이터가 미국에 저장된다는 점에 동의하는 것으로 간주됩니다.

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.

Key Takeaways:

Learn how effective context management improves the performance of AI applications.
Understand different context engineering techniques, from prompts to RAG and knowledge graphs.
Discover how to use Neo4j as a scalable memory layer for AI chatbots and agents.

Session Resources + Slides