Report: 83% of organizations need to upgrade their infrastructure to support agentic AI

While this unlocks entirely new use cases, there's a catch: it places significant stress on the underlying infrastructure we've relied on in the past. We recently surveyed more than 1,400 senior IT leaders for our State of AI Infrastructure report , and a resounding pattern emerged: the gap between AI ambition and infrastructure reality is widening. In fact, 83% of organizations say they require infrastructure upgrades to support production-grade agentic AI.

Because yesterday's infrastructure simply wasn't built for agents that act autonomously. In this blog, we lay out the core insights from our research on how leading organizations are rethinking their infrastructure to build resilient, fluid foundations. For more details and depth, we encourage you to download and read the full report.

Escape the "inference tax" with fluid compute Agentic workloads introduce a new level of scale, where a single prompt can trigger hundreds of downstream actions, requiring massive context windows to be held in memory. Trying to run these continuous reasoning loops on legacy architecture is financially unsustainable. In fact, 62% of leaders are seeing a significant inference tax driven by data egress fees, storage bloat, and idle specialized hardware.

Furthermore, 81% cite operational complexity as a hidden cost of scaling AI. To fix this, organizations need fluid compute - the ability to dynamically match the right silicon to the right task while minimizing operational overheads. For heavy training : Compute accelerators like our new TPU 8t deliver tremendous scale to train the world's most sophisticated models.

For low-latency inference: The TPU 8i, meanwhile, was purpose-built to maximize on-chip memory, so agents can think and react in real-time. For orchestration : General-purpose compute powered by CPUs is emerging as a critical component for driving AI control plane operations. Using highly efficient, Arm-based processors like Google Axion, organizations can cost-effectively run reinforcement learning simulations and orchestrate agents.

Managing agent sprawl with centralized governance Agents are designed to act autonomously - reading emails, querying databases, and executing workflows across your business. But as agentic AI scales, organizations are facing a new challenge: agent sprawl. How do you manage thousands of autonomous agents scattered across diverse platforms, without losing visibility and control?

It's no surprise that 79% of tech leaders cite security, governance, and MLOps as their top challenge to scaling inference. In the agentic era, you need a mature governance strategy before you can innovate. This entails creating a centralized control plane that provides a single system of record for agent permissions, identity, and workflows.

Instead of patching together disparate tools, leading enterprises are relying on solutions like Agent Gateway to enforce enterprise-grade governance. Agent Gateway gives you the visibility you need to see exactly how agents are sharing data. It lets you define precise read/write scopes and maintain full audit trails of every interaction, and it provides human-in-the-loop oversight for when an agent needs approval before taking a critical action.

This drive for unified, straightforward governance explains why 78% of organizations now source their gen AI solutions directly from their primary cloud partner - a 30 point increase from 2025. A unified data layer Agents perform reasoning, meaning they constantly run heavy queries across your organization. If your data is fragmented across silos, your AI is effectively flying blind.