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…
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 Our current enterprise compute infrastructure—rigid, siloed, and reliant on outdated protocols like BGP—is buckling under the weight of petabyte-scale networks, exponential AI growth, and unpredictable traffic patterns.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…
The shift to cloud, SaaS, and hybrid work is no longer breaking news. What is surprising? How many IT and network teams are still trying to stitch together architectures that weren’t designed for today’s distributed world. Data is everywhere. Users are everywhere. Applications live across SaaS, public cloud, and private data centers. Yet too often, traditional network and security architectures can’t keep up, creating bottlenecks, security gaps, and user frustration. Modern SASE and SD-WAN architectures promise agility, resilience, and better performance, but only when they’re…
AI infrastructure is growing at an unprecedented pace. Enterprises are racing to build clusters of GPUs, scale up AI workloads, and modernize their data pipelines. Yet one critical layer is often overlooked in these initiatives: the network. While AI and data leaders focus on compute, storage, and models, the network quietly becomes the bottleneck. Traditional, static networks—built for legacy application traffic—can’t handle the dynamic, latency-sensitive, high-throughput demands of distributed AI workloads. And without visibility, orchestration, and automation across the full stack, enterprise IT leaders are…
For years, enterprise network and security management has relied on a centralized model that’s starting to feel like a relic of a bygone era. Picture this: every networking device, every security appliance, every switch, router, and firewall diligently sending a flood of system logs, SNMP data, alerts, and threat indicators to a massive, centralized data lake. It’s a digital haystack—overflowing with terabytes of information, growing daily, and hiding the critical insights engineers need to keep the infrastructure humming and secure. When an incident strikes—say, a…
Lightyear’s new report highlights telecom industry and pricing trends observed in its proprietary dataset, illustrating how these factors may impact enterprise IT infrastructure strategies. Lightyear, the leading provider of enterprise telecom management software, release its highly anticipated 2025 State of Connectivity Report. This comprehensive report utilizes Lightyear’s proprietary dataset to provide timely insights on telecom industry and pricing trends. “We at Lightyear are rethinking the enterprise telecom experience from first principles, and a significant component of doing so involves helping enterprises better inform telecom decisions with…
