Agentic AI is starting to move from theory into real enterprise use, with 67% of C-suite executives expecting to have AI agents independently running in their organizations by 2027. But when teams try to build and run these systems on infrastructure that wasn’t designed for them, things break down fast. To succeed, enterprises need a modern agentic AI infrastructure designed to handle the demands of AI agent development and operations.
Agentic AI introduces new performance, scale, and data management challenges that legacy systems cannot handle. High-performing, scalable AI agents require constant, real-time data inputs for continuous learning and improvement, and quickly stretch the limits of traditional enterprise data foundations. To move from experimentation to enterprise-scale deployment, organizations need AI-native infrastructure built for this kind of continuous data access and decision-making.
Agentic AI places new demands on enterprise infrastructure. Below, we outline what’s required to build and run AI agents in practice, and how VAST Data’s AI OS provides a unified foundation for data, compute, and intelligence.
Why Infrastructure Matters in the Agentic AI Era
Human agents constantly take in real-time data and contextual cues that allow them to make the best possible decision for each task given the circumstances and data at-hand. Enterprise AI agents work in exactly the same way and, therefore, must also be able to read, process, and act on vast data streams in real-time.
To achieve this same level of contextual decision-making, agentic AI depends on seamless access to structured, unstructured, and real-time data. However, legacy data infrastructures — built for transactional or static workloads — cannot support the continuous reasoning and action-taking required by autonomous AI agents.
Organizations that try to use their legacy data platform for agentic AI quickly see initiatives falling short. When AI agents are unable to receive the data they need, their learning and performance suffers. Besides failing to achieve business outcomes, this common scenario negatively impacts enterprises via wasted spend and delayed innovation timelines.
For building autonomous AI systems that deliver clear, impactful results, a new kind of data infrastructure is required.
Core Infrastructure Requirements for Agentic AI
The future of intelligence starts with the right foundation, and an effective agentic AI infrastructure differs from traditional static infrastructures in a few important ways. Here are the five essential components of a robust agentic AI data platform:
Unified Data Foundation
Housing all data inputs in one unified location is the best storage configuration for Agentic AI. By keeping structured, unstructured, and streaming data sources together — situated as close as possible to the AI agents receiving them — cumbersome silos are eliminated and data latency is greatly reduced. This simplified data access structure improves the training data ingestion and live inference performance of Retrieval Augmented Generation (RAG) based AI agents.
High-Performance Storage
In addition to storage unification, the storage media used also has a significant impact on AI agent performance. Traditional spinning disk storage introduces too much lag into the AI development and inference processes. When it comes to agentic AI, all-flash data storage is much better suited for providing the speed needed for real-time reasoning and data recall. Additionally, the linear scalability of flash storage ensures that as AI agents grow and learn, their performance does not degrade.
Real-Time Processing and Vectorization
AI agents require instant access to both historical and streaming data, but these data inputs can be in many different formats. Therefore, an agentic AI infrastructure requires an integrated, exabyte-scale vector database that continuously converts text, audio, and visual data inputs into vector embeddings that AI agents can read and understand. This database brings compute closer to the data with integrated functions and triggers, and enables real-time, highly-scalable vector search capabilities for AI agents.
Intelligent Orchestration
Successful agentic AI deployments depend on reliable coordination across multiple agents and systems. Expansive, disjointed environments with siloed systems or databases cannot meet the level of unification that agentic AI requires. Instead, an agentic AI infrastructure must be fully aligned, controlled, and orchestrated via a centralized engine, with complete visibility across the entire network. This degree of connectivity is what allows enterprises to maintain real-time communication, event-driven triggers, and governance controls as AI operations grow.
Security and Governance
To maintain enterprise confidence and security, autonomous AI systems demand atomic-level data protection and compliance. While AI agents should remain free to think and perform tasks as they best see fit, having the right guardrails in place can ensure that, for example, sensitive data is not accessed or shared. Approaches such as zero-trust security, multi-tenant isolation, and policy-driven access are critical agentic AI infrastructure components for enterprise trust. Consolidating from several multi-tier point solutions to a single tier multi-protocol AI OS eases the complexity of governance, and helps meet necessary regulatory compliance.
How VAST Data Powers Agentic AI Infrastructure
VAST Data provides a unified data platform for agentic AI, bringing storage, data pipelines, and compute together in a single system and built to meet the infrastructure demands of fast-moving enterprises.
The VAST AI Operating System (AI OS)
At the heart of it all is the VAST AI OS for agentic AI development. This unified platform consolidates data storage, database, and compute into one AI-native layer, eliminating architectural silos and powering continuous agent learning.
How Enterprises Benefit: All-in-one AI agent platform with simple, near-limitless scale.
Disaggregated Shared Everything (DASE) Architecture
VAST’s innovative DASE architecture does away with the complex tiered structure of legacy systems, and makes all components instantly available on a single tier. This simplified structure supports independent scaling of compute and storage needs, and provides high concurrency and resilience for mission-critical agentic AI workloads.
How Enterprises Benefit: Predictable performance at exabyte scale, all under one global namespace.
VAST InsightEngine
The VAST InsightEngine powers the real-time conversion (vectorization) of data inputs, keeping its vector database within arm’s reach of AI agents. This enables the continuous semantic search required for instant agent decision-making. InsightEngine also integrates seamlessly with NVIDIA NIM and other inference systems for live context retrieval.
How Enterprises Benefit: Real-time agent inferencing and actions, yielding optimal AI performance and productivity.
VAST AgentEngine
AgentEngine is the management and orchestration layer of the VAST AI OS — it simplifies and automates the deployment, monitoring, and scaling of agentic AI applications. AgentEngine provides a dedicated runtime for multi-agent workflows, a Model Context Protocol (MCP) Toolbox, and deep observability into agent actions and reasoning. This shifts AI agents from fragile prototypes to trusted enterprise applications that run reliably at scale.
How Enterprises Benefit: Confident AI agent deployments, with full visibility into agent actions and performance.
Building the Path to Agentic AI Readiness
Enterprise adoption of agentic AI requires patience and a commitment to making the tough but necessary changes. To start, assess where your data infrastructure stands today and determine if/how it needs to evolve. Things to look at include:
Current Gaps: Are existing systems capable of handling demanding AI workloads? If not, where are they lacking in terms of performance, integration, and scalability?
Unification Feasibility: What is the current structure of internal storage, database, and compute layers? Can they be consolidated?
Infrastructure Augmentation: Is retrofitting legacy systems the best option? Or could it make more sense to adopt platforms that natively support AI workloads?
Agentic AI readiness isn’t an overnight transformation — it’s a long-term journey that begins with modernizing and reworking your data foundation.
The Enterprise Advantage with VAST Data
VAST Data is helping the world’s leading organizations build the infrastructure that makes agentic AI possible — scalable, secure, and enterprise-ready. By unifying data and compute with VAST AI OS, enterprises eliminate the common barriers to performance and cost-efficiency, accelerating innovation while maintaining critical governance and security.
Take the next step today and move your organization confidently from experimental to production-scale AI agents with the right agentic AI infrastructure:
