The role of Retrieval-Augmented Generation (RAG) in AI is facing intense scrutiny as 2026 approaches, with many vendors suggesting its original architecture is becoming obsolete. This shift marks a significant moment in the evolution of data infrastructure, which is experiencing unprecedented change driven by agentic AI.

For decades, relational databases like Oracle dominated the data landscape, organizing information in a structured manner. However, the rise of NoSQL document stores, graph databases, and, more recently, vector-based systems has disrupted this stability. According to many in the field, the limitations of the original RAG pipeline, which functions much like a basic search retrieving results for specific queries at specific times, are driving this change. These pipelines were often confined to single data sources, a constraint that became increasingly apparent prior to June 2025.

The core issue with early RAG implementations lies in their limited scope and real-time constraints. The original RAG architecture's inability to adapt to multiple data sources and evolving information needs has fueled the perception that it is nearing its end.

As data becomes increasingly vital in the age of AI, the need for more sophisticated and adaptable data infrastructure is paramount. The debate surrounding RAG's future reflects a broader trend toward more dynamic and integrated data solutions. The evolution of data infrastructure is happening faster than ever before, driven by the demands of agentic AI. This rapid development underscores the critical importance of data in shaping the future of AI and its applications.