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Enterprise Cloud 2.0 Technology Solutions, Strategy and Use Cases

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Agentic AI: A Bold New Era for Enterprise Operations

Agentic AI—intelligent systems capable of proactive, context-aware decision-making—is reshaping how corporations operate. Rather than forcing organizations to rely on rigid, feature-laden enterprise software that only fulfills a fraction of their needs, Agentic AI learns a company’s unique business logic to generate predictive, customizable workflows in real time. This shift not only enables “digital twin” simulations of corporate operations—letting leaders test new ideas or products in a virtual environment before rolling them out—but also demands a radical rethinking of enterprise compute infrastructures. To fully harness Agentic…

Unlocking Enterprise Agility in the API Economy

The Network Lag in a Consumption-Driven IT World Over the last decade, compute, storage, and applications have evolved to on-demand, consumption-based models — reshaping how enterprises consume IT. Yet, one critical pillar has lagged: the network. While software-defined networking has made inroads, many enterprises still operate rigid, pre-provisioned networks. As applications become increasingly distributed and dynamic — from hybrid cloud to edge deployments — a programmable, on-demand network infrastructure has moved from a “nice to have” to a non-negotiable requirement. Across industries, enterprises are evolving…

Bulk Procurement of Internet / WAN Connectivity: Getting the Best Outcome

If you’ve gone through an MPLS to SD-WAN transition or simply operate a network with 100+ sites, you’ve likely undergone some form of “bulk” connectivity procurement in the past. Bulk procurement comes with drawbacks – complex RFP creation, tons of quoting to manage, lengthy contracting, and painfully complex installations. That said, I’ve found that IT leaders often espouse the benefits of bulk procurement. Why? The bulk discount! If you buy many circuits at once, you’ll end up with a lower price per Mbps on average…

Data Center Bridging is a Critical OPEN Technology for AI Data Centers

The evolution of artificial intelligence, particularly the rise of large language models (LLMs) and deep learning, has created an immense demand for computational power primarily driven by GPUs. However, maximizing GPU efficiency requires a robust networking infrastructure that ensures minimal latency and zero packet loss. This post highlights the critical role of Data Center Bridging (DCB) with a focus on its two key components; Priority-based Flow Control (PFC) and Explicit Congestion Notification (ECN) in achieving lossless and high-performance networks suitable for large-scale GPU deployments in…

The State of Agentic AI: Why the Industry Needs a Vendor-Independent Overlay Reference Architecture Now

As enterprise IT leaders race to integrate Agentic AI into their infrastructure, they are hitting a wall. The promise of AI agents autonomously driving operational decisions and orchestrating data movement across complex environments is tantalizing—but the practicalities of deploying such systems are proving overwhelming. Without a shared architectural blueprint, organizations are left navigating a maze of proprietary solutions, incomplete integrations, and security blind spots. This is the moment for a vendor-independent Agentic AI Overlay Reference Architecture. What IT Executives Are Struggling With Agentic AI isn’t…

Transforming WAN Procurement – Join the ONUG Collaborative WAN Connectivity API Working Group

The ONUG Collaborative has launched the WAN Connectivity API Working Group to redefine how enterprises procure WAN services. Chaired by Eric Powers of Citigroup and Chris Cheu of Evernorth, this user-driven initiative is focused on designing a vendor-neutral, open API framework that enables enterprises to dynamically discover, compare, and provision WAN connectivity in real time—just like cloud infrastructure. Project Goals The goal is simple yet transformational: automate and standardize WAN procurement with an API-first model. The API will support: Service Discovery – Real-time visibility into…

Why You Can’t Afford to Miss the AI Networking Summit NYC This Fall

If you’ve been paying even the slightest attention to the AI gold rush, you know that every tech event now tries to slap “AI” onto its agenda. But here’s the truth: most of what you’ll hear is the same generic hype: more slideware than substance, more marketing than mastery. That’s exactly why the AI Networking Summit, hosted by the ONUG Community this October in New York City, is different. Over two days, you’ll get an unfiltered look at how Agentic AI is transforming the very…

Reimagining Networking: My Vision for a Secure Data Exchange with an Agentic AI Overlay

A New Era for Networking Last week at the AI Networking Summit in Dallas, I shared a vision for the future of networking that challenges us to rethink what our industry is fundamentally about. We’re no longer just moving packets between devices; we’re orchestrating a secure data exchange that powers the AI era. Our current enterprise compute infrastructure—rigid, siloed, and reliant on outdated protocols like BGP—is buckling under the weight of petabyte-scale networks, exponential AI growth, and unpredictable traffic patterns. To meet these demands, we…

The New Era of Cloud Compute: Why IT Leaders Must Rethink GPU Infrastructure Now

For the past decade, IT leaders have driven innovation by migrating workloads to the cloud. But now, as AI, machine learning, and high-performance computing take center stage, infrastructure needs have shifted. Compute is no longer just about flexibility and scale—it’s about raw power, speed, and efficiency. And that’s where traditional cloud models are falling short. The Challenge: Cloud Isn’t Keeping Up With AI When cloud computing became mainstream, it promised elasticity and simplicity. But that promise is starting to break under the weight of generative…

Why It’s Time to Get Proactive About IT Problems—Before They Break Your Business

In today’s digital-first world, the question for IT leaders isn’t if problems will arise—it’s how early you can spot them, and how fast you can resolve them. Yet despite massive investments in observability, ticketing platforms, AIOps solutions and automation, too many organizations remain stuck in firefighting mode—managing symptoms instead of solving root causes. It’s time to break the reactive cycle. With Cognitive AI Learning, IT teams can proactively identify and resolve recurring issues before they impact services. From Reactive Chaos to Proactive Control The traditional model of problem management is reactive…