Partner POV | Agentic AI: Is your inference infrastructure ready to deliver productivity at scale?

This article was written by Adam Saenger, Global Head of Product at Digital Realty. 

Technology shapes how we work, live, and interact. As the evolution of technology rapidly evolves, leveraging it to advance business automation and digital transformation has remained a core focus of IT investment.

Agentic artificial intelligence (AI) refers to autonomous agents that independently reason, plan, and execute multi-step tasks without ongoing human input. These agents rely on inference, applying trained models to new and unseen data.

Agentic AI could be an evolutionary leap in Straight Through Processing (STP), integrating human knowledge for edge cases. These agents carry out quick, repeatable workflows that fast-track decision-making, optimize operations, and scale personalization.

Enterprises are building agentic AI systems anticipating this scaled efficiency. According to IDC, 50% of organizations say that "building customized AI agents to automate business processes" is a top area where they expect to increase spending.1

Real-time inference, which powers agentic AI, requires working with and moving large amounts of data. When data processing isn't optimized for location and bandwidth, performance and productivity suffer. The result? Botched workflows that can cost customer trust and revenue.

Metro-based inference is an AI deployment pattern where inference compute (GPU/CPU + model serving) is placed in metro-area data centers close to end users, devices, and enterprise data sources. As businesses scale their agentic workflows, metro-based inference will need to be at the heart of the AI infrastructure foundation.

Let's dive into how AI workloads at the edge power inference and explore solutions for the challenges of performance, reliability, and security.

Data processing and latency: the IT infrastructure and productivity bottleneck

Data exchange is fundamental to AI inference. With agentic AI, models create multi-step reasoning and micro-inferences with real-time information retrieval. Agentic AI runs inference in repeated loops, which makes inference always-on and latency-sensitive.

In industries like high-frequency trading in financial services, where continuity and consistency are paramount, even a few milliseconds of delay make a difference. When insights from AI take too long to arrive, opportunities are missed and productivity drops. Centralized clouds can impair performance by forcing data to travel long distances before processing and adding jurisdictional data privacy and sovereignty complexities.

Cloud collaboration tools can also strain bandwidth. Large files and continuous batching consume capacity, particularly for distributed teams. This is part of Data Gravity, where moving large amounts of data can challenge performance, customer experience, and security. That lag can stall collaboration and creativity, while the costs of upgrading infrastructure to handle heavy data loads can add up quickly.

Customer service tools face similar challenges. When AI chatbots or response systems lag, both employees and customers feel it. As adoption of these tools rises, queuing delays will also grow, slowing time-to-first token. Technology meant to improve efficiency can undermine user experiences.

The latency bottleneck problem is significant, but it's further complicated by the state of modern workplaces.

The dispersed workforce challenge

Hybrid and remote have redefined what it means to stay connected. Video conferencing is now business-critical and increasingly uses AI assistants to streamline collaboration.

Yet lag, buffering, and dropped calls can zap energy and engagement. Simply put, the more you have geographically dispersed humans, the more apps and systems interacting with the AI model—compounding inference performance. Reliable, high-bandwidth infrastructure is no longer a nice-to-have; it's fundamental to productivity.

Employees need consistent experiences wherever they work, and that depends on systems that sync instantly and handle data efficiently. Workers often need access to real-time data and visualizations to make faster decisions, but these applications only work well when latency is minimal. Metro-based inference delivers that responsiveness by processing information locally, ensuring real-time performance.

The tools we use to facilitate dispersed working are evolving, too. Once again, edge processing is increasingly providing the platform they need.

Agentic AI and low-latency collaboration

Metro-based inference transforms these tools by cutting down the distance data travels. Real-time updates and analytics happen near the user rather than in faraway data centers, creating smoother interactions for dispersed teams.

Industries such as healthcare, construction, and logistics are already seeing game-changing results from agentic AI. Field engineers can receive instant diagnostic feedback from AI-powered apps, making quick, informed decisions even with limited connectivity. By processing data locally, enterprises can keep tools running smoothly and avoid downtime that can derail productivity.

Agentic AI performs best with specialized infrastructure designed to handle complex workloads.

Agentic AI and inference workloads evolve data center needs

As AI reaches a pivotal inflection point, there is a growing realization that digital infrastructure would need to support the increased power densities, cooling needs, and connectivity required to run graphics processing unit (GPU) inference reliably.

Those data centers that are not equipped for AI share similar limitations:

-They are only built for 5-10 kW per rack.

-Cooling is often air-first, not designed for GPU heat loads.

-Power distribution isn't built for high-density clusters.

-Network patterns are not designed for production-ready AI.

-They are in places where power and land might be more affordable, but latency is not a prioritized factor.

Metro-based data centers: powering performance

To make low latency possible at scale, enterprises are turning to metro-based data centers—decentralized facilities that process information closer to end users. This shortens data travel distances and greatly improves the performance of software as a service (SaaS) platforms, analytics tools, and video conferencing systems.

Metro-based data centers can ensure that intelligent applications function effectively even in low-connectivity regions. They process and filter information locally, sending only essential updates back to the cloud. This can improve speed and strengthen reliability.

Major cloud providers are investing heavily in this model. AWS (with Outposts and Wavelength) and Microsoft (through Azure Edge Zones and Azure Stack) now enable organizations to extend cloud capabilities to the edge. These hybrid infrastructures combine the scalability of cloud systems with the responsiveness of local computing, helping businesses of all sizes operate efficiently, anywhere in the world.

Digital Realty can give you the scale, reach, and capacity to place your AI workloads where they need to be. Our 300+ strategically located data centers span 55+ metros, providing a unique opportunity for putting inference close to your users and workflows.

But there's an important caveat to this new world of high-performance infrastructure— connectivity.

The connectivity layer

Edge computing still depends on robust connections to keep everything synchronized. Data must move seamlessly between edge nodes, data centers, and the cloud to ensure accuracy and real-time insight.

Metro-based data centers don't operate in isolation. For true efficiency, each data center must form part of a distributed network, or Connected Data Communities™, linked by high-speed fiber and dynamic connections to multiple endpoints.

This interconnection allows workloads to be dynamically balanced and replicated across sites, ensuring that data remains synchronized and available even if one node experiences downtime. It also enables these metro data centers to share compute resources and maintain consistent performance globally. In effect, businesses aren't relying on a single facility but a mesh of edge nodes: a coordinated network designed to deliver the benefits of local processing while retaining the resilience and scalability of the cloud.

Fiber-optic networks provide the backbone for this connectivity. Their speed and reliability enable lightning-fast data transfer, supporting everything from autonomous systems to real-time analytics.

Continuous synchronization ensures AI models remain current—vital for industries like healthcare and finance, where decisions rely on precise data. Network redundancy also matters: multiple connections can prevent downtime if one link fails, keeping hybrid teams connected and productive.

Finally, security and speed go hand in hand. Low-latency networks must also be secure, with encryption and advanced protocols protecting data without slowing collaboration. This is critical for applications like live video calls, remote training, and virtual diagnostics, where both immediacy and confidentiality matter.

Metro-based inference powers productivity

Agentic AI is offering speed and scalability benefits that are solidifying it as a cornerstone of the modern digital workforce. It represents more than a technical upgrade; it's a new way of working that enables an unprecedented wave of productivity and efficiency.

For IT leaders who want to future-proof their digital workforce, they must prioritize the AI foundation that supports it.

By shifting AI closer to where data is created, businesses can cut delays, lower costs, and protect privacy without sacrificing performance.

It's a shift from centralized control to sharing global AI intelligence—one that supports remote work, real-time decision-making, and collaboration. In short, agentic AI is setting the stage for a more agile, connected, and productive future of work.