Agentic AI Data Architectures

Chapter 3. Beyond Storage: Patterns for Agentic Applications

Chapter 2 framed distributed SQL as more than a storage technology positioned as the backbone of memory systems that unify meaning and truth. Having established this foundation, the next step is to explore how such capabilities are used in practice. Atop the different data stores apps apply patterns, repeatable strategies that apply distributed SQL to the challenges of agentic AI. Each pattern demonstrates how infrastructure becomes intelligence, transforming distributed SQL from a theoretical substrate into a practical framework for adaptive reasoning.

Beyond Storage: Semantic and Transactional Patterns

With an infrastructure foundation in place, the next step is to examine the most basic query patterns that illustrate how distributed SQL binds meaning and truth together. These patterns show how vectors and transactions, once handled in isolation, can now be joined within a single query fabric. Semantics and facts in distributed SQL are not separate, so they can interact natively, producing results that are both contextually rich and operationally valid. This convergence of SQL and semantic retrieval marks the entry point for practical design for many of the patterns that follow, where retrieval begins to look less like isolated lookups and more like coherent memory. Here are three patterns for how SQL and semantic retrieval work together.

Semantic-Transactional Join

This pattern combines vector similarity search ...