From real-time to reasoning: Why NoSQL is core to agentic AI

"AI agents are steadily becoming embedded in enterprise workflows: automating customer interactions, coordinating operations, and reasoning across complex datasets. However, if you take a closer look beneath the surface, many organizations are struggling with the technical challenge of supporting them in real time. Legacy data architectures aren't built for this. To make agents performant, scalable, and accountable, IT leaders are turning to something familiar, but more flexible: NoSQL."

"AI agents differ from traditional AI systems in that they don't just generate outputs; they reason, plan, act, and often collaborate. This means drawing on real-time data from multiple sources, executing tasks, and maintaining context across sessions. The problem is that traditional analytics databases are optimized for batch processing, not the continuous, low-latency reads and writes that agents require. In contrast, operational databases must support constant state changes, contextual awareness, and rapid feedback loops with near-instant performance."

"NoSQL databases, particularly document-based ones, provide the flexible data models needed for modern AI. Agents need to work with a variety of data types from JSON records and user profiles to embeddings and API responses. NoSQL enables this without rigid schemas, supporting rapid iteration and adaptation. Many modern NoSQL platforms also offer features traditionally associated with relational systems like ACID transactions and SQL-like querying, bringing together flexibility and familiarity."

AI agents are being embedded in enterprise workflows to automate customer interactions, coordinate operations, and reason across complex datasets. They require real-time access to diverse data sources, continuous low-latency reads and writes, and the ability to maintain context across sessions. Traditional analytics databases are optimized for batch processing and cannot sustain constant state changes or rapid feedback loops. NoSQL document databases offer schema agility and flexible data models for JSON, embeddings, and API responses while many platforms add ACID transactions and SQL-like queries. Almost half (44%) of UK IT leaders struggle to access and manage the data needed to power AI initiatives, indicating infrastructure gaps. Operational memory and near-instant performance are essential for agentic AI.

Read at ChannelPro