In the rapidly evolving landscape of enterprise AI, scaling solutions efficiently and effectively remains a significant challenge. Traditional data engineering approaches often need help to meet the demands of AI applications that require swift and intelligent data access. This presentation introduces an Agentic Framework that automates data engineering tasks through intelligent agents and leverages data virtualization to unify disparate data sources.

Attendees will explore how this framework empowers organizations to accelerate AI deployment by automating data pipelines, enhancing data accessibility, and reducing operational bottlenecks. Key features include the integration of Retrieval-Augmented Generation (RAG) and real-time RAG techniques to improve the relevance and timeliness of AI outputs. The framework also incorporates caching mechanisms to optimize data retrieval speeds and employs Massively Parallel Processing (MPP) architectures to handle large-scale data operations efficiently.

Additionally, I will discuss the role of vector databases in managing and querying high-dimensional data essential for similarity search and other AI-driven tasks. The session will explore how these components combine within the Agentic Framework to provide a scalable and flexible solution for AI initiatives.

I will illustrate the tangible benefits of adopting this approach, including improved time-to-insight, increased agility in AI development, and cost savings. Join me to discover how leveraging an Agentic Framework for data engineering and data virtualization—enhanced with RAG, real-time RAG, caching, MPP, and vector databases—can propel your enterprise AI strategies to new heights.