In the era of intelligent agents and generative AI, data is not just an input - it is the differentiator. This session focuses on the strategic importance of data in designing, building, and deploying high-performing AI agents. Whether you're using Microsoft Copilot Studio, Azure AI services, or building custom RAG (Retrieval-Augmented Generation) pipelines, the success of your solution hinges on how well your data is curated, connected, and consumed.

We’ll explore end-to-end data strategies — from identifying the right data sources to optimizing them for retrieval and grounding. We'll also discuss how semantic search, vector databases, metadata enrichment, and feedback loops contribute to relevance, accuracy, and adaptability in AI agents.

Key Takeaways:
-Understand the data lifecycle in AI agent development
-Learn how to identify, structure, and prepare enterprise data for grounding and RAG
-Explore best practices for data governance, compliance, and quality
-Discover how to use vector search and semantic indexing for better contextual responses
-Design feedback loops for continuous learning and improvement of AI outcomes

Join us to uncover why great AI agents begin with great data - and how a solid data strategy can elevate your AI initiatives from functional to truly intelligent.