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.

What does agentic AI demand from data?

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.

Almost half (44%) of UK IT leaders struggle to access and manage the data needed to power AI initiatives, according to our research. It’s a sign that infrastructure isn’t keeping pace with the demands of AI agents.

Why flexibility and schema agility matter

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.

Memory is the new application state

AI agents that operate over time need memory in addition to processing power. That includes:

• Short-term memory: to track recent interactions and maintain conversational continuity.
• Long-term memory: to store past experiences for learning and recall.
• Procedural memory: to remember how to complete tasks and use tools.
• Shared memory: to coordinate actions with other agents.

NoSQL platforms that combine in-memory access with persistent storage simplify memory management and reduce the risk of context loss, which helps to deliver intelligent, consistent outputs. For example, a logistics agent managing deliveries must recall past delays (long-term), respond to live traffic updates (short-term), coordinate with dispatch (shared), and execute new routing strategies (procedural). This requires seamless memory transitions instead of fragmented storage systems.

This matters more than ever. Indeed, our research found that just 32% of UK IT leaders feel prepared to deploy agentic AI. The gap isn’t about ambition - as 52% believe it can uncover new business trends - it’s about infrastructure readiness.

The governance test: can you trust your agent’s outputs?

Governance must be at the heart of AI agent production now that we’re at the inception point. Organizations must be able to explain how decisions were made, what data was used, and what outcomes were generated.

Well-architected NoSQL platforms support this through audit logs, data lineage tracking, and fine-grained access controls. These capabilities shift those platforms to being compliance tools in addition to debugging behavior and improving transparency.

Without traceability, AI risks becoming a black box: vulnerable to biased decisions, erratic behaviour, and regulatory breaches. With 43% of UK organizations concerned about the security risks of third-party AI tools, internal observability and governance are becoming strategic imperatives.

The pitfalls of patchwork architectures

One of the biggest obstacles to effective agent deployment is architectural complexity. Many teams have stitched together disparate tools, creating brittle, fragmented systems that are difficult to scale or maintain.

This approach introduces latency, duplicates logic, and limits the agent’s ability to maintain coherent memory. It also complicates debugging and governance, making production rollouts riskier.

In contrast, a unified NoSQL developer data platform reduces complexity and improves reliability. It ensures consistent context, accelerates data access, and provides the performance foundation agents need to reason and act in real time.

The cost of complexity: why point solutions break at scale

Many enterprises adopted point solutions like caches, vector databases, or operational stores to support early AI experimentation. But as they move toward production, these stitched-together systems start to break down. They add latency, inflate costs, and create governance blind spots.

It’s no wonder, then, that 51% of UK IT leaders say the risk of AI project failure is too high to move forward, and 68% believe consolidating their data stack would make AI easier to manage and control.

A unified NoSQL platform simplifies the underlying data architecture, making it easier to scale, govern, and deliver reliable outputs. Without that foundation, even the best AI strategies risk stalling. In fact, 51% of UK companies believe delays in AI deployment could cost them up to 5% of their monthly revenue.

The future of AI demands simpler, smarter architecture

The rise of agentic AI marks a fundamental shift in how we think about software. These systems are persistent, collaborative, and increasingly autonomous. But to reach their potential, they need modern, fit-for-purpose infrastructure.

NoSQL is enabling the future of AI by combining flexibility, scalability, memory, and governance in a single platform and delivering the data foundation agents need to succeed.

As organizations move from experimentation to production, those foundations will separate the innovators from the sheep. Because agents can only be as intelligent and as trustworthy as the systems they’re built on.