The Onug Blog
Enterprise Cloud 2.0 Technology Solutions, Strategy and Use Cases
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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…
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…
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…
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…
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…
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…
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…
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…
Trigger Warning: This article contains discussions of mental health challenges, including suicide. BRIANNA: This article is difficult to write. On one hand, I’m part of an Artificial Intelligence community that’s on the cutting edge of technology, where we explore the latest advancements and trends in AI. We bring together Fortune 500 leaders across the globe to discuss AI’s incredible potential: from groundbreaking cancer treatments to enhancing cybersecurity and creating safer environments for both organizations and individuals alike. I am a firm advocate of AI for…
The introduction of production level AI has become the trigger for IT organizations to re-evaluate their entire approach to providing business services across the organization. Decades of incremental growth using proprietary technologies in networking and compute is now being parked, in favor of fresh blank-canvas approaches to IT. And for the networking practitioners, it became clear that that original promise of seamless integration and single-vendor support came at a steep price – years of vendor lock-in, limited customization, missed capabilities, and greatly inflated costs. But…
