Agentic AI knows what it wants when it comes to enterprise storage. These autonomous, AI-driven agents can make their own decisions, and it has become clear that what they require from enterprise storage solutions is low latency, high performance, 100% availability, scalability, intelligent orchestration, cyber resilience, and a retrieval-augmented generation (RAG) reference architecture.
Agentic AI is evolving fast, putting higher demands on the data infrastructure of organizations. These AI agents adapt to new information and do multiple tasks, as well as collaborate with other AI agents — all independent of humans. Figuratively speaking, if generative AI (GenAI) is the cook that makes a pizza, then agentic AI is the one that checks if the customer needs non-dairy cheese, adjusts to the pattern of toppings the customer has had for their last 10 orders, and communicates directly with the AI agent that will handle delivery to the customer.
This “pizza” metaphor serves to illustrate how agentic AI has ushered us into a new world where systems can “think” (or reason), plan, continuously learn, adapt, and, ultimately, act on their own in complex or dynamic scenarios. Agentic AI represents a significant leap forward in AI capabilities — even above and beyond the iteration of GenAI that the world has seen over the last couple of years. Agentic AI is basically built to function like digital workers. And the implications for enterprise storage are causing enterprises to reassess their storage infrastructure.
Decisions made about agentic AI generate questions about how the data infrastructure of enterprises will evolve. Gartner’s 2025 Emerging Tech Report states that more than 60% of enterprise AI rollouts this year will embed agentic architectures. This marks a shift to proactive AI. The enterprise agentic AI market is projected to grow from approximately $2 billion in 2024 to over $40 billion by 2030, according to Emergen Research, Markets and Markets, and other industry trackers (June 2025). Moreover, according to a Bank of America Global Research analysis released in June, spending on agentic AI could be as high as $155 billion over the next five years, which is more than three times higher than the projections of other analysts.
An AI RAG architecture, as an overlay on top of the storage infrastructure, is now a must-have for any organization using or planning to use agentic AI, such as having agentic AI agents make their own rapid decisions based on the latest information and carry out multi-step actions to fulfill customer orders, provide a service, or coordinate better business outcomes.
RAG makes agentic AI more accurate. It enables enterprises to continuously refine a RAG pipeline — an inherently iterative process — with new data, reducing the risk of inaccuracies, outdated information, or AI hallucinations. While large language models (LLMs) and small language models (SLMs) are typically trained on publicly available data and lack exposure to an enterprise’s private data, RAG augments agentic AI models using relevant and private data retrieved from an enterprise’s vector databases, which are a critical component in GenAI. Current versions of commonly used database engines support storing and retrieving vector data.
RAG combines the power of generative AI models with enterprises’ active private data. This ensures that agentic AI agents are basing their decisions and communications with people and other AI agents on correctly informed private corporate information and avoiding the possible legal issues arising from the use of public data. To sum it up, RAG is important for producing accurate results and making agentic AI highly practical for everyday operations and ongoing business.
This means that the C-suite of enterprises can have high confidence that the adoption of agentic AI will work well across autonomous enterprise workflows, including everything from generative process agents to personal AI assistants to self-optimizing industrial systems.
Underpinning this technological advancement with RAG are the core pillars of enterprise storage infrastructure — or “slices” of the pie. An enterprise that deploys agentic AI needs the following storage capabilities to ensure that AI runs properly and RAG delivers what it is supposed to deliver:
- High Performance: Speed of the storage infrastructure is a foundational element for AI to be at its best. Having the highest performance is a differentiator for an enterprise.
- Low Latency: Delays in the operation of agentic AI are a “no-no.” Just as people disliked delays due to latency issues in the early days of the Internet, customers will not tolerate delays due to latency in the data infrastructure when agentic AI applications are business- and mission-critical. Having the lowest latency possible for enterprise storage is now a priority for agentic AI.
- 100% Availability: Continuous, uninterrupted access to data is a must-have in the agentic AI world. Having 100% availability in enterprise storage is a requirement for this new wave of AI.
- Scalability: A storage infrastructure must be able to scale to higher capacity, as the datasets used for agentic AI continue to increase. It needs to be able to scale without “breaking the bank” too. Flexible consumption models for enterprise storage should be evaluated to find the right option for your organization. Furthermore, large, scalable vector databases should be easily supported on the storage platform you use.
- Intelligent Orchestration: Intelligence must be built into the storage infrastructure to orchestrate and optimize data placement dynamically and predictively. This lays the groundwork for supporting agentic AI by adeptly managing AI workloads.
- Cyber Resilience: With cyberattacks hitting enterprises every week, agentic AI must be protected. One of the safeguards is cyber storage resilience, which mitigates the impact of a cyberattack, such as ransomware or malware, by taking a cyber-oriented, recovery-first approach. It is best to have cyber recovery capabilities that can recover from a cyberattack on primary storage in one minute or less.
When you are seeking out an enterprise storage solution to support your organization’s AI applications, workloads, and deployments, look for a solution that does not require specialized equipment. Moreover, choose a RAG reference architecture that can run on existing storage infrastructure, even in a multi-vendor environment.
A RAG workflow can be easily created from existing open-source products and corporate private data already in an enterprise’s on-premises data center. A Kubernetes cluster can be leveraged as the foundation for running the RAG pipeline, enabling high availability, scalability, and resource efficiency.
Agentic AI and RAG-optimized enterprise storage go hand in hand in the next phase of enterprise AI.
