Optimizing Agentic AI Workflows Through Full-Stack Infrastructure

As agentic AI makes its way into enterprises, businesses need to modernize their infrastructure to adapt to the demands of continuous, multi-step workflows. Leveraging heterogenous, integrated full-stack infrastructure simplifies the modernization process and reduces time-to-value.  

When generative AI (GenAI) made the big time a little over three years ago, the world was rightfully amazed. Today, organizations are in a new era of AI that promises to be even more transformational and have an ever greater impact on IT teams. 

With agentic AI, businesses are getting not just another tool, but a whole new way of automating and operating business systems. From the perspective of IT teams, that means getting ready for an entirely new user base – the agents themselves – while rearchitecting their systems to support continuous, multi-step agentic execution.

What are the key infrastructure considerations around agentic workflows?

GenAI workflows are closed, finite processes. A user prompt is sent to an AI model which then formulates a response before returning that response to the user. It’s a simple back and forth between the prompter and the model. The LLM runs entirely in the cloud infrastructure of the AI lab in question. 

Agentic workflows are significantly more complex. In this case, LLMs service multi-step executions that continuously invoke the model, call tools, and grow context over time. 

The agents run in continuous loops, evaluating outputs, checking for errors, calling tools, and repeatedly running inference until they complete their assigned task. In this context, enterprises are moving toward a hybrid architecture, running both massive frontier models as well as open-weight models on their own infrastructure. 

What will this infrastructure look like? From a compute perspective, whereas GenAI workloads typically require a 1:4-8 CPU-to-GPU ratio, agentic AI is moving towards a 1:1 ratio – perhaps even tipping in favor of CPUs in some instances. 

In GenAI, the CPU acts as a simple headnode for a GPU cluster. In agentic AI, CPUs are responsible for a broad range of key processes including GPU orchestration and data supply, agent execution and tool calls, triggering APIs and enterprise software, and running policy and security checks on autonomous actions. 

One vital consideration in this shift is ensuring that expensive GPU accelerators are not left idle waiting for database queries, web scraping, code execution, and other necessary inputs. The whole compute system must work seamlessly and at low latency to ensure the highest levels of effectiveness and efficiency. That means investing in high-performance processors.

Enabling agentic workflows will also require integrating AI software, hybrid and multicloud infrastructure, Kubernetes, security, and operational tools from multiple vendors. This is a major task which, if handled incorrectly, leads only to fragmented AI environments, slower deployments, governance challenges, inefficient GPU utilization, and infrastructure complexity. Agentic workloads will suffer as a result of these performance issues and businesses will be unable to fully realize the outcomes they expected from their investments.

Why is legacy cloud infrastructure poorly suited to agentic AI deployments?

The cloud offers enterprises a simple way to modernize their infrastructure so that it is ready to support enterprise agentic AI at scale. However, the choice of cloud platform is vital. Traditional hyperscalers lock enterprises into their ecosystems through technical, data, financial, and operational dependencies. This can be a major headache if the hyperscaler increases prices, bundles unwanted services, or modifies model behavior, as enterprises are not easily able to migrate their agents to alternative infrastructure. 

There’s also a consideration around where workloads run. When deploying AI workloads that use sensitive data, organizations need governance, sovereignty, and deployment flexibility to meet regulatory and security requirements. 

In fact, our latest AI maturity report found that governance and compliance ranked as the top obstacle to greater AI maturity, with 64% of the AI decision-makers surveyed calling it very or extremely challenging. Meeting those requirements depends on having real control over where data lives and how workloads are deployed. This isn’t always achievable using traditional hyperscaler platforms. 

How to optimize agentic AI workloads with full-stack infrastructure

Infrastructure from alternative cloud providers offer a new route to bringing enterprise agentic AI into production. The key is finding an approach that combines the simplicity and flexibility businesses need to operationalize agentic AI at pace with the necessary security and governance controls. 

Vultr full-stack enterprise AI infrastructure is such a solution. The approach integrates AMD EPYC™ CPUs and AMD Instinct™ GPUs to meet the full processing requirement of agentic systems; VAST Data’s AI operating system for storing, managing, accessing, and processing data; and the SUSE AI Factory for a standardized AI platform that supports the full AI lifecycle from development to production across cloud, edge, on-premises, and sovereign environments.

This full-stack model is a turnkey approach to agentic AI infrastructure. Leveraging Vultr Cloud Compute, Bare Metal, or Kubernetes Engine, the platform provides businesses with an open ecosystem to deploy AI on their own terms. Organizations run agentic AI workloads where it makes most sense for their business and ensure alignment with data privacy and sovereignty requirements. 

This is a stack where validated blueprints, lifecycle management, governance, and scalable infrastructure combine to accelerate enterprise AI deployment. Once in production, Vultr’s infrastructure combined with VAST Data’s OS ensures that data flows to processors efficiently to ensure optimal GPU utilization. Meanwhile, by boosting capacity for core cloud workloads with Vultr VX1™ Cloud Compute, the full-stack platform ensures that business applications are resilient and capable of supporting the increase in demand that will come as scores, hundreds, or even thousands of agents interact with them concurrently. 

Optimizing workloads in this way also optimizes costs. Enterprises can make the most of their GPU investments through effective resource utilization. Meanwhile, by running core workloads on the efficiency-optimized Vultr VX1™ Cloud Compute, businesses can reduce costs associated with core applications, unlocking the budget to invest in new AI infrastructure. 

A seamless stack for accelerating agentic AI 

The result of deploying Vultr’s full-stack AI infrastructure is that businesses can reduce the time it takes to implement their infrastructure modernization programs and start seeing a return on their investment. Ultimately, optimized workflows lead to enhanced business outcomes and a significant competitive advantage.