Over the past year, copilot agents and retrieval-augmented generation (RAG) systems have evolved from intriguing prototypes to production-grade solutions embedded in real workflows. In this session, I’ll explore hard-earned lessons from building and deploying several Copilot and RAG applications—covering what worked, what didn’t, and what I’d do differently.

I'll walk through real architectures built with Copilot Studio for Copilot agents and Azure AI Foundry, Azure AI Search and python for RAG systems, unpacking key design choices around data ingestion, chunking, embedding, and orchestration. You’ll see where retrieval quality makes or breaks quality, how multiple use cases change complexity, and how to align user expectations with system functionality.

Whether you’re just starting to experiment with copilot agents or already scaling a RAG application with a knowledge base, this talk will give you practical insights, patterns, and pitfalls to help you build more reliable, maintainable, and explainable AI assistants.

Key Takeaways:
- Understand practical architecture patterns for Copilot agents and RAG apps
- Learn how to tune chunking, retrieval, and prompt orchestration for quality