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Composable AI Network Architecture

1 post · May 2026 – Aug 2026

What this trail covers

SingleStore's architecture is evolving to support a network of specialized AI models and agents, moving away from monolithic AI systems. This involves leveraging its distributed SQL capabilities for high concurrency and low-latency access to fresh enterprise data, enabling independent scaling and evolution of AI components. The focus is on data freedom through open formats like Apache Iceberg and integrating AI intelligence directly within the operational core. This post emphasizes the architect's role in building a real-time converged data architecture for agentic AI, highlighting the five building blocks: continuous ingestion, multi-pattern query support on one engine, machine-scale concurrency, multimodal data without cross-system joins, and compute close to data. SingleStore's HTAP engine, Universal Storage, hybrid search, and Aura GPU-aware compute are positioned as key components of this convergence layer, distinct from traditional data warehouses.

All SingleStore trails
  1. Enterprise AI Will Be a Network, Not a Monolith`This post introduces the concept of building enterprise AI as a network of specialized models and agents rather than a single monolithic model. It highlights the critical role of a high-concurrency, low-latency database like SingleStore in managing the complex interactions and data synchronization required by agent swarms. The post also emphasizes the importance of open data formats like Apache Iceberg for data freedom and composability in AI architectures.
  2. Why Your AI Model Shouldn't Be Doing the Database's JobThis post details the architectural trade-off of using AI models for data integration versus executing joins and data processing within the database engine. It introduces the concept of the 'token tax' and explains how forcing models to perform database tasks leads to inflated costs and reduced accuracy. The post advocates for a unified HTAP engine that handles filtering, joins, and vector similarity scoring within the storage layer. It also highlights the SingleStore Context Engine's role in caching execution plans to bypass language models for repeated queries, significantly reducing token costs and improving auditability. The post identifies symptoms of model-based integration (high context window volume, cost scaling with data growth, lack of query transparency) and discusses trade-offs of a unified AI data architecture, such as the need for continuous data streaming and the potential for erroneous SQL generation.
  3. Real-Time Data Convergence Architecture for Agentic AIThis post details the architectural pattern of real-time data convergence for agentic AI, defining it as a platform where ingestion, operational analytics, real-time analytics, and AI retrieval operate on the same live data. It outlines five essential building blocks for this architecture: continuous ingestion with no staging delays, multi-pattern query support on a single engine, concurrency designed for machines (thousands of queries per second), multimodal data (structured, semi-structured, vector) without cross-system joins, and compute close to data (GPU-aware compute embedded in the platform). The post positions SingleStore as the 'convergence layer' above data warehouses, handling the hot path with its HTAP engine, Universal Storage, hybrid search capabilities, and Aura GPU-aware compute service (including Aura Analyst, GPU-powered notebooks, model hosting, Cloud Functions, and Python UDFs) for low-latency, in-region scoring and real-time enrichment.