The state of Agentic AI and the need for Agentic Memory

We’re at an incredible inflection point in AI. For the past few years, generative AI has rightly commanded the spotlight, with its ability to create, design, and synthesise information. We are now at the point of another paradigm shift with Agentic AI.

This isn’t just about faster information processing. It’s about enabling systems to act with more independence, context, and coordination. Generative AI introduced models that can learn and create based on prompts. Agentic AI builds on top of that and adds the critical capabilities of acting on its own and collaborating with both humans and other agents.

It picks up precisely where generative AI leaves off, taking extracted and summarised information and transforming it into autonomous action, with limited or no human intervention. We’re talking about systems that can dynamically pursue goals, make decisions, and evolve their behaviour over time.

Is your data ready for Agentic AI?

The latest State of the Agentic AI Market Report from ISG highlights key challenges with how data is currently managed. Enterprises are grappling with fragmented data, unclear governance, and operating models simply not built for this new kind of capability.

The report points out that traditional data architectures, often built on decades-old data architectures with ETLs and pipelines, are simply not fit for the agentic era. These multi-year roadmaps for data foundations are increasingly seen as “fruitless efforts” because agents interact with data profoundly differently than humans or other data-driven technologies. They don’t need context from applications because they can create their own from raw data, at scale.

The report makes it painfully clear that data is quickly shifting from being an asset to a liability for many organisations. Such organisations are stockpiling data they can’t effectively use due to quality issues, yet still paying for its storage, security, and compliance. This isn’t just a minor hurdle, it’s a fundamental roadblock to scaling Agentic AI and realising its full business impact.

Defining Agentic Memory

This is where the concept of “Agentic Memory” becomes not just important, but absolutely critical. The report discusses the need for:

• a context-aware data backbone for scaling Agentic AI
• centralised data hubs, designed to facilitate data interactions for all models and agents.
• the ability to synchronise multi-agent memory at scale
• agents that learn from experience and refine their actions over time.

These aren’t just buzzwords. They describe the essential “memory” layer for Agentic AI because autonomous agents need more than just data storage. They need a dynamic, real-time, interconnected knowledge base that allows them to:

• sense context and analyse data in real time
• make bounded decisions within defined constraints
• escalate effectively when human input is required
• maintain a direct lineage from data to outcome
• collaborate seamlessly with other agents and human decision-makers.

The current data landscape, with its silos and legacy infrastructure, simply cannot provide the high-quality, real-time contextual data that is required for this new paradigm.

SurrealDB: powering the future of Agentic Memory

This is precisely the future SurrealDB was built for. We understand that Agentic AI demands a new kind of database. A database that is natively multi-modal, real-time, and deeply context-aware.

SurrealDB isn’t just another database, it’s designed to be the ultimate platform for Agentic Memory.

Imagine a data layer that:

• eliminates silos at the source. By natively supporting graph, vectors, document, relational, and more, SurrealDB provides a single, unified “memory” for all your agents, without complex ETLs or rigid schemas. This directly addresses the problem of fragmented data, giving your agents the full context they need to act.
• enables real-time, contextual decisions. Agents need instant access to dynamic, evolving context. Our event-driven architecture and real-time capabilities ensure that agents always have the freshest, most relevant information for their actions.
• provides modularity and portability. A key concern for enterprises is model and cloud lock-in. SurrealDB is built with open, modular principles, allowing you to swap out models, agents, or even cloud environments without being tied to proprietary protocols or vendor ecosystems, thus fostering the modular architectures critical for Agentic AI.
• adapts and learns with your agents. Just as agents learn and refine their actions, their underlying data store must be equally adaptive. SurrealDB’s flexible schema and real-time capabilities mean your “Agentic Memory” can evolve as your AI systems mature.

What really matters: taking high-quality decisions faster

The “State of the Agentic AI Market Report” rightly concludes that the future will be won not by those who pilot the most agents, but by those who transform how they make and manage decisions.

This means moving from linear business processes to composable decision systems, where execution is goal-driven and adaptive. Achieving this requires a fundamental shift in thinking.

Databases have always been at the core of traditional software applications. With the rise of Agentic AI, the role of databases must evolve to serve as the “Agentic Memory” at the core of reliable agentic systems.

The question isn’t if you need to rethink your data for agents, but how quickly you can equip your agents with the memory they need to drive unparalleled decision velocity.

I’m excited about this future and can’t wait to share what we’ve been working on.