Infrastructure for Agentic AI: What CIOs Need to Solve Now

Agentic AI has left the slide deck. Agents that read data, make decisions, and carry out tasks across systems have become a priority on practically every technology committee’s agenda. But there’s a point where enthusiasm meets reality: infrastructure. In the agentic era, companies that don’t prepare their foundation will watch their agents stay stuck in pilot.

The numbers in Google Cloud’s report, State of infrastructure in the agentic AI era, show the size of the challenge. The research, published on July 7, 2026, surveyed 1,402 IT leaders in 12 countries, including Brazil and Mexico. 93% recognize the value of AI. But 83% say their infrastructure needs upgrades to support agentic systems in production. Only 17% report full confidence in running mission-critical agents today.

In this article, we pull together what the research reveals, why pilots hit a ceiling, and the practical path to getting agents out of the lab with security, governance, and costs under control.

The functional ceiling: why pilots don’t scale

A chatbot answers a question. An agent does far more: a single instruction can trigger hundreds of actions, with queries, API calls, and decisions across multiple systems at once. Think of a supply chain agent that spots low inventory ahead of a sales peak, cross-checks historical data, negotiates with transportation agents, and prepares the purchase order for approval.

That kind of workload changes the logic of infrastructure. According to IDC data cited in the report, inference now accounts for 47% of AI operations, compared with 28% for training. In other words, the weight is now on keeping agents working around the clock, not on training models once in a while.

The problem is that most environments were designed for applications that move at the pace of a person clicking. When leaders point to their biggest gaps, three barriers stand out:

• 43% struggle to integrate legacy APIs and data sources, such as ERPs and CRMs that weren’t built for machines to talk to each other;
• 36% lack specialized, high-throughput vector databases to give agents memory and context;
• 35% lack adequate security for an agent to access multiple systems.

Then there are the costs that don’t show up on the compute bill. 81% cite operational complexity and engineering hours spent on manual integration. 62% report data egress fees, growing storage, and idle hardware, the so-called “inference tax” of legacy stacks. 57% feel the weight of a shortage of specialized talent. It’s no surprise that 96% say cost efficiency matters in AI infrastructure decisions.

Governance stopped being a brake and became a prerequisite

Agents are built to act: they read emails, query databases, and make API calls. The question every CIO has to answer is how to give them enough freedom to be useful, with enough guardrails to be safe.

79% of leaders point to security, governance, and model operations (MLOps) as the biggest obstacle to scaling inference. Traditional security was designed for people, not for autonomous agents exposed to new vectors such as indirect prompt injection and tool poisoning.

The response from more mature companies has been to consolidate. 80% of leaders say data compliance is the main factor in choosing infrastructure, and 69% already consider a full-stack platform an essential requirement. Reliance on the primary cloud provider has also grown: 78% of organizations buy generative AI solutions directly from their cloud partner, up from 48% in 2025.

In practice, that means a single control plane for agent permissions, workflows, and human oversight. On Google Cloud, this runs through the Secure AI Framework (SAIF) and capabilities such as Security Command Center and Model Armor, which protect AI applications against prompt injection and data leakage. For anyone operating under Brazil’s LGPD, it’s the difference between a pilot blocked by legal and an agent in production.

Hybrid, edge, and sovereignty: where agents will run

The debate between public cloud and on-premises has converged. 52% of organizations already use hybrid multicloud, and 90% consider it important to bring AI to the edge, closer to where data is created. The reasons are concrete: lower latency for agents working with voice, video, or field operations; lower cost per token; and resilience when the connection drops.

Add digital sovereignty to that. 48% of leaders prioritize infrastructure with data residency controls, and Gartner projects that 75% of companies outside the U.S. will have a sovereignty strategy by 2030. For Latin American companies dealing with LGPD and local rules in every country where they operate, designing the architecture with jurisdiction in mind from day one avoids costly rework.

Finally, efficiency has become a business criterion: 91% of leaders already consider power consumption when choosing hardware. A Brazilian example appears in the report itself: AXIA Energia uses climate models on Google Cloud, with TPUs [validate], to predict extreme weather events up to 10 days ahead, protecting supply for 60 million people.

From pilot to production: a roadmap in four moves

The research’s conclusion is direct: success in the agentic era doesn’t belong to whoever trains the biggest model, but to whoever builds the most resilient, secure, and integrated foundation to run it. Maplink Cloud, which has spent more than 25 years bringing together geolocation, cloud, and data intelligence, and is a Premier Partner for Google Cloud and Google Maps Platform, with operations in Brazil, Argentina, Chile, and Mexico, organizes this journey in four moves, always with a Cloud & AI First approach.

1. Diagnose before you scale

Not every company needs a complete rebuild. Cloud Discovery is a quick assessment that shows where the bottlenecks are and what the cost and effort potential looks like. When efficiency is the topic, the Cost Assessment maps current consumption and points to where the “inference tax” is weighing most. [validate service names]

2. Build an agent-ready foundation

With Landing Zone and Google Cloud Foundation, we structure networking, identity, permissions, monitoring, and automation from the start. That’s where governance stops being an add-on and becomes part of the architecture.

3. Give agents data they can use

An agent without context is an agent without memory. We migrate and modernize legacy databases to managed services and build the analytics layer on BigQuery, with Looker to give the business visibility. That unified foundation is what solves the integration difficulty cited by 43% of leaders.

4. Take agents to production

We develop AI agents and Gemini-based solutions on the Gemini Enterprise Agent Platform (formerly Vertex AI), with MLOps pipelines to monitor, version, and evolve models after go-live. The focus is on escaping “pilot purgatory” with use cases that deliver measurable results.

There’s also a differentiator few partners can offer: geospatial context. With Google Maps Platform, Maplink Cloud connects location data, BigQuery, and the Gemini Enterprise Agent Platform to build agents that understand where things happen. For logistics, insurance, retail, and field services, knowing where the inventory, the customer, or the technician is turns a generic answer into a decision that delivers results.

All of this with a local team, support in Portuguese and Spanish, and billing in each country’s currency.

Infrastructure is the strategy

In the agentic era, the question is no longer “should we use agents?” but “can our infrastructure handle agents in production?” For 83% of companies, the honest answer is still no. The good news is that the path is well known: a clear assessment, a governed foundation, unified data, and operations that carry agents from pilot to results.

Want to know where your company stands? Talk to Maplink Cloud and start with a Cloud Discovery to map what’s missing to put your agents into production.

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Sources

Google Cloud, State of infrastructure in the agentic AI era (2026), research with 1,402 IT decision-makers in 12 countries, conducted with GBK Collective and published on July 7, 2026. Includes data cited from IDC (2026) and Gartner (2025).