With AI increasingly becoming table stakes for organizations, let’s dig into the role NoSQL databases play in facilitating AI adoption, and why a flexible developer data platform with memory, persistence and traceability is needed to power AI agents.
Starting With the Basics on NoSQL
NoSQL databases, short for “Not only SQL,” were developed to address modern data storage and scalability needs that traditional relational databases struggle with.
Unlike relational databases, which were designed to minimize data duplication and scale vertically, NoSQL databases use flexible data models such as key-value, document, column, time series and graph formats to accommodate web, mobile and IoT applications. These databases operate as primary content stores, allowing flexible data access and high availability through horizontal scaling across distributed systems.
Organizations choose NoSQL for its ability to support dynamic, real-time and personalized user experiences, adapting quickly to changing application requirements. NoSQL databases, particularly document-oriented ones, use the JSON format, enabling agile development without rigid schemas.
Additionally, modern NoSQL systems incorporate relational database features, including ACID (atomicity, consistency, isolation and durability) transactions and SQL-like querying, while maintaining scalability, high availability and efficiency. This convergence of relational and NoSQL capabilities simplifies database management, making NoSQL the preferred choice for modern, flexible cloud computing and distributed data applications.
AI Agents Are Operational Applications
AI agents, which automate traditional software and human workflows, require real-time data access for task execution and to support reasoning.
Unlike traditional analytical databases, which are often relational, highly structured and process data in delayed batches, operational databases enable low-latency, high-frequency read and write operations, which are essential for AI-driven applications. In the retail industry, for instance, AI agents can use diverse operational data such as user profiles, inventory, promotions, product vector embeddings and more for powerful semantic search.
To function effectively, agents must integrate multiple data formats, engage with models, cache conversations and maintain those interaction histories. The database needs to support high-velocity workloads, ensuring AI agents remain responsive and scalable.
AI Needs Access to a Variety of Data in a Flexible Way
AI agents require fast data access and a diverse range of data to operate effectively, especially in real-time decision-making scenarios. They need both structured data (such as databases and spreadsheets) and unstructured data (such as text, images and audio) to generate powerful insights and responses. The ability to quickly pull relevant data enables AI to produce responses that are the most contextually relevant to the user and make predictions with minimal latency.
Additionally, real-time data sharing through APIs and functions allows AI systems to integrate seamlessly with other platforms, ensuring up-to-date information flow and facilitating dynamic, automated decision-making. Without rapid access to varied data sources, AI agents risk providing outdated, incomplete or inaccurate responses, limiting their effectiveness, whether supporting internal or customer-facing applications.
Multiagent AI Systems Need To Work Together
In enterprise environments, multiagent AI systems can efficiently handle dynamic workloads and deliver prompt responses but will need real-time performance and scalability. By collaborating through distributed shared memory, these agents can swiftly access and update shared data, enhancing coordination and reducing communication overhead. Implementing low-latency, event-driven synchronization mechanisms ensures that agents remain aligned and can react promptly to changes, thereby maintaining system coherence and responsiveness.
Techniques such as array-based queuing locks can be employed to manage access to shared resources, minimizing contention and ensuring fairness among agents. Additionally, communication protocols like the message passing interface facilitate efficient data exchange and synchronization across distributed systems. Collectively, these strategies enable multiagent AI systems to operate effectively in complex, large-scale enterprise settings.
Memory and Persistence Together
Maintaining short-term, long-term, procedural and shared memory is critical for AI agents to ensure contextual awareness, continuity and efficiency in decision-making. Short-term memory (caching) allows AI to rapidly retrieve recent interactions and computations, reducing redundant processing and improving responsiveness. Long-term memory (persistence) ensures AI agents retain historical context, enabling them to learn from past interactions and refine their outputs over time.
Having both in a unified platform streamlines performance, as agents can seamlessly transition between fast temporary access and deep retained knowledge. Additionally, AI agents need structured storage for critical information such as API definitions, function calls and prompts, allowing them to interact efficiently with data, execute the correct actions and ensure consistency across different sessions. By integrating these memory types, AI systems can provide more intelligent, context-aware and adaptive interactions while optimizing computational efficiency.
Governance and Traceability
Governance and traceability are essential for AI agents, particularly in enterprise environments where compliance, accountability and safe AI behavior are critical. Organizations must ensure that AI-driven decisions are transparent, auditable and explainable to meet regulatory requirements, mitigate risks and build trust in AI systems. Traceability allows enterprises to monitor how AI models reach conclusions, making it possible to detect biases, errors or security vulnerabilities.
By implementing robust governance frameworks, businesses can enforce ethical AI use, prevent unauthorized access or misuse, and maintain consistency in decision-making. Additionally, enterprises need auditable logs of AI interactions, ensuring that every decision can be reviewed, verified and improved over time. Without proper governance and traceability, AI systems may pose compliance risks, erode trust and fail to align with business objectives and legal standards.
The Challenge of Point Solutions
Reliable and unified data architectures are key to successful AI projects. Using multiple database and data cache systems for AI agents create significant challenges by complicating data access, hindering collaboration, disrupting memory integration, limiting flexibility, increasing operational expenses and undermining governance. Organizations that deploy multiple single-purpose database solutions also introduce data sprawl, risk and complexity, making it difficult to effectively use AI, minimize AI confusion, trace the source of AI hallucinations and debug incorrect variables.
Data complexity is AI’s enemy because AI is imprecise to begin with. Using AI within a complex, multi-database architecture produces unreliable results because the risk of feeding AI models inconsistent or incorrect data is too high.
AI agents require fast, seamless access to diverse data for real-time decisions, but drawing data from disparate systems introduces inefficiencies, backtracing issues and delays. Collaboration falters as multiagent systems face compatibility issues, slowing communication and coordination. Memory management suffers from fragmentation, breaking the continuity needed for contextual awareness and performance. Flexibility is curtailed, delaying adaptation to new needs or features, while governance and compliance become harder to enforce due to inconsistent monitoring and traceability.
By simplifying the data management activities that surround AI, a unified, multipurpose database resolves these issues, enabling reliable, scalable and compliant AI operations.
A NoSQL Data Platform To Support Agentic AI
Tens of thousands of organizations have adopted NoSQL, making it their choice for modern applications. AI agents are the next logical step on that path to be supported by fast and flexible NoSQL data.
To run critical applications, many enterprises choose Couchbase to improve resiliency, performance and stability while reducing risk, data sprawl and total cost of ownership. Couchbase is the developer data platform that powers critical applications in our AI world. Find out more about how Couchbase Capella and AI services help organizations accelerate the development of agentic AI applications. Start using Capella today for free and sign up for the private preview of Capella AI Services.
The post Why Use a NoSQL Database for AI? There Are Many Great Reasons appeared first on The New Stack.
NoSQL databases play a key role in facilitating AI adoption. A flexible platform with memory, persistence and traceability is needed to power AI agents.