Context Engineering: Building AI Agents That Work in Production

In this workshop, you’ll learn what context engineering really means in practice—and why it’s one of the most important factors shaping the quality of AI outputs.

We’ll start by breaking down how context influences model behavior, and why many AI systems fail not because of the model, but because of poorly designed context. From there, we’ll explore how to think about context as a system design problem, not just a prompting task.

You’ll learn how to structure context effectively, select the right information, and design workflows that guide models toward more accurate, consistent, and useful responses. Along the way, we’ll look at common failure patterns and how to diagnose and fix them in real applications.

This workshop is practical, hands-on, and designed for anyone building with LLMs who wants to move from prototypes to reliable, production-ready systems.

What you’ll learn

• What context engineering is—and why it matters for agentic applications
• The core components of effective context design
• How context shapes model behavior and output quality
• How to diagnose and fix weak, noisy, or inconsistent outputs
• Proven context engineering patterns used in modern AI systems
• How to design context pipelines for real-world agent workflows

By the end of the workshop, you’ll have a clear understanding of how to think about context as a system design problem, not just a prompting problem, and you’ll leave with practical methods you can apply in your own AI projects.

Workshop deliverables

• A 1 -hour live workshop focused on context engineering for real AI applications
• A practical framework for designing better context pipelines
• Real examples showing how context design affects output quality
• Workshop slides for later review
• Session recording so you can revisit the material anytime
• Take-home guidance you can apply to your own LLM projects