Report: 83% of Enterprises Need Infrastructure Upgrades to Support Agentic AI

As enterprise AI evolves beyond simple conversational use into Agentic AI that performs tasks on its own, analysis shows that existing infrastructure struggles to keep up. According to a recent survey of over 1,400 IT leaders, 83% of enterprises said infrastructure upgrades are needed to deploy agentic AI in production environments.

Agentic workloads cause a single prompt to trigger hundreds of subsequent actions and require maintaining a massive Context Window in memory, resulting in enormous costs and operational complexity on existing architectures. In fact, 62% of leaders are experiencing an Inference Tax driven by data egress costs, storage bloat, and idle hardware.

To address this, enterprises need a Fluid Compute strategy that includes the following:

• Heavy Training: Large-scale model training using accelerators such as TPU 8t
• Low-latency Inference: Models optimized with on-chip memory (TPU 8i) for real-time responses
• Orchestration: Efficient agent control and simulation using Arm-based processors such as Google Axion

In addition, centralized governance is essential to address the Agent Sprawl problem caused by numerous distributed agents. Enterprises need solutions like Agent Gateway to manage agent permissions, identities, and workflows, and to gain visibility into data sharing.