Databases weren’t built for agent sprawl – SurrealDB wants to fix it

The New Stack

by Paul Sawers

February 24, 2026

AI-Generated Deep Dive Summary

AI agents pose a challenge for traditional enterprise data stacks, which were not designed to handle their complex requirements, such as transactional state management, long-term memory, similarity search, and real-time updates. To address this issue, SurrealDB has introduced a multi-model database aimed at reducing architectural sprawl by consolidating the various systems needed to support AI agents into a single engine. This approach aims to eliminate latency, duplication, and operational overhead, which are common when stitching together multiple tools like relational databases, vector stores, and graph engines. SurrealDB 3.0 builds on this foundation with enhanced features tailored for AI-native workloads, including persistent agent memory, expanded vector capabilities, and a new in-database plugin system. The company, founded in 2022 by Jamie and Tobie Morgan Hitchcock, has raised $44 million in funding to further develop its platform. Their focus is on simplifying the integration of AI agents, knowledge graphs, real-time applications, and edge computing by combining structured data, contextual memory, and connected data within one system. For DevOps professionals and cloud developers, SurrealDB offers a potential solution to the growing complexity of managing diverse database systems for modern AI workloads. By unifying multiple data access patterns—such as transactional updates, search, analytics, and relationship queries—SurrealDB aims to streamline operations and reduce the risk of outages caused by data duplication across siloed systems. This consolidation not only improves efficiency but also makes it easier to build scalable AI-native applications that require real-time interactions and dynamic data handling. The significance lies in its ability to address the unique demands of AI agents, which often require simultaneous access to structured, unstructured, and connected data. By reducing the need for multiple databases and middleware layers, SurrealDB could lower costs and simplify maintenance for organizations adopting agent-based systems at scale. As AI-native applications continue to evolve, tools

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Originally published on The New Stack on 2/24/2026