The evolution of AI is driving a major transformation in enterprise infrastructure. Companies were once experimenting with generative AI but are now focusing on agentic AI because it can automate complex workflows and decision-making. About 23% of enterprises are already scaling AI agents and 39% are experimenting with them, according to a McKinsey report. These systems bring advances in speed, efficiency and decision-making. At the same time, as these workloads move into production, the urgency to modernize infrastructure has increased.
These agentic systems also lower the barrier to entry for non-technical users, expanding opportunities for innovation beyond IT and AI teams. They also allow business teams to collaborate more directly in shaping operations. For example, a finance team can design an autonomous system that reconciles invoices, flags anomalies and initiates approvals without needing engineers.
Data as the Foundation
The demands of AI pipelines on infrastructure are growing as data lakes now scale from petabytes to exabytes in size. The amount of data U.S. companies use is expected to grow by 122% by 2026. How companies control, manage and use proprietary and high-quality data will determine competitive advantage across business sectors. For agentic systems, data is critical to accuracy and autonomous decision-making and these systems must draw from both structured and unstructured sources.
Organizations are deluged with an ever-increasing volume of data, but compute has become commoditized, and many algorithms processing the data have become commoditized. Data itself remains the primary source of insight. To drive progress, enterprises should use internet-scale public datasets, and then correlate, augment, and fine-tune them with proprietary data. This combination of large-scale data with proprietary expertise enables AI capabilities that are harder to imitate and faster to improve, sharpening competitive edge.
Evolving Infrastructure for Agentic AI
These agentic AI demands create a range of distinct needs across the entire pipeline. Data preparation requires large-scale storage, high-throughput ingestion and the ability to handle structured and unstructured data. Model training demands intensive computing, fast access to curated datasets and low-latency connections between storage and GPUs. Inference requires speed, consistency and scalable deployment.
To support these phases, enterprises must modernize to deliver higher input/output operations per second. They are adopting dense, scalable flash-based platforms that increase efficiency while reducing power, footprint and cooling needs. Electricity demands from data centers worldwide are expected to more than double by 2030, according to the IEA. Flexible consumption models and elastic capacity also help manage fluctuating AI workloads, allowing organizations to scale storage and compute up or down on demand and only pay for the performance they use. However, these intensifying workloads expose bottlenecks across storage, compute and networking layers.
The growth of agentic AI workloads has shifted critical bottlenecks toward the compute layer, since agentic systems generate rapid, continuous data requests that overwhelm traditional data paths, making it necessary to bring GPU compute closer to the data. GPUDirect architectures are becoming essential for unlocking the performance of modern flash systems, while networking is being reinvented through high-speed fabrics such as InfiniBand and emerging protocol standards such as Spectrum-X.
To allow data to reach GPUs quickly enough to keep training and inference workloads fully utilized, storage should scale elastically and deliver high throughput, low latency and parallelism. Integrated designs that unify compute, storage and networking are becoming essential to eliminate pipeline bottlenecks and fully support agentic AI workloads.
Cloud Strategies for Agentic AI
As infrastructure evolves, cloud strategy becomes a critical extension of the agentic AI stack. Sovereign clouds, located and managed in a specific geographic region, are increasingly critical for agentic AI, which depends on controlled, high-integrity data environments to operate safely and autonomously. Agentic systems only amplify concerns about data residency, provenance and governance because autonomous decision-making raises the stakes of using incorrect, unverified or non-compliant data. Therefore, sovereign clouds are an attractive option for organizations that need tight control of training data and decision-making.
Hybrid cloud models are becoming a common architecture for agentic AI, since they often require experimentation in the public cloud as well as controlled operations of on-prem systems. Agentic workloads and hybrid cloud strategies benefit from Kubernetes, which gives the mobility for autonomous modules to be deployed across clouds and data centers while maintaining policy, governance and security. Hybrid clouds also support agentic inference workloads that may need to be close to data sources for low-latency decisions.
Security, Compliance and Governance
As organizations distribute agentic workloads across sovereign, hybrid and public clouds, regulatory and security pressures will rise with the added geographic and technical complexity. AI and cybersecurity regulations are also evolving, becoming more challenging as governments address the risks associated with advanced AI. Regulatory scrutiny is pushing enterprises to create rigorous standards for resilience and data protection, as seen with the EU Data Act, which establishes strict rules for data access, sharing, portability and governance. Organizations should design systems that can adapt to shifting compliance needs.
Regardless of requirements, enterprises need to strengthen security to ensure protection of data, workloads and access points as threats become more frequent and sophisticated. Zero trust architecture across multiple infrastructure components is essential. Governance considerations are growing as organizations blend public and proprietary data, which means precise controls must be in place for rights, usage, lineage and licenses. Data governance plays a critical role here, especially when managing sensitive information, lineage and compliance.
Agentic systems increase the need to protect confidentiality and prevent data exposure, because they autonomously access and combine data across systems, expanding attack surfaces and requiring continuous security controls. At the same time, agentic AI strengthens security and governance by automating policy enforcement and the monitoring of data usage and access, while giving real-time validation against regulatory requirements.
Managing Infrastructure in an Agentic Era
Meeting these regulatory and security expectations requires infrastructure that is not only modernized but also managed with greater automation and consistency. Agentic frameworks simplify infrastructure management through automated provisioning, monitoring and optimization. Automation and shared governance also reduce risks during deployment and scaling by providing consistency across hybrid and multicloud environments.
Unified control planes provide visibility into storage, compute, networking and GPU resources as AI systems grow more complex. Agentic systems also allow non-experts to build or deploy autonomous modules tied directly to workflows. Infrastructure is increasingly managed as a unified stack. Overall, observability, provisioning, monitoring and optimization all need to be integrated into an agentic management model to ensure consistent performance and reliability.
Conclusion
Agentic AI depends on high-quality, unified data, so data architecture and governance are critical to achieving competitive advantage. While investing in infrastructure, enterprises should also invest in reskilling so employees across all functions can build and collaborate with autonomous systems. Consolidating infrastructure and adopting high-end platforms helps reduce energy consumption while maintaining performance at AI scale. Architectures must be flexible to support evolving agentic capabilities and regulatory shifts. Ultimately, organizations that modernize early with data unification, integrated systems and hybrid cloud flexibility will be best positioned to lead as agentic AI becomes a core driver of enterprise transformation.
