The Advantages of Running vSphere Kubernetes Service in a Private Cloud with VMware by Broadcom

Event: Tech Field Day at KubeCon North America 2025

Appearance: VMware by Broadcom Presents at Tech Field Day at KubeCon North America 2025

Company: VMware by Broadcom

Video Links:

Personnel: Jeremy Wolf

This session will emphasize the tangible benefits of running vSphere Kubernetes Service in a private cloud environment. We’ll reinforce how VKS simplifies operations within a private cloud context, highlighting minimal networking expertise required, self-service capabilities, and the powerful synergies with VCF services for an optimal private cloud solution.

Jeremy Wolf introduced VMware’s approach to bringing a cloud experience to private data centers with VCF (VMware Cloud Foundation). He emphasized that applications require more than just runtimes; they need a complete ecosystem. To that end, VCF offers three core runtime services out of the box: VKS (vSphere Kubernetes Service), VM Service (a cloud-like way to consume vSphere VMs), and Container Service (deploying containers without a complete Kubernetes cluster). The goal is to enable consumers, whether platform operators or app developers, to deploy workloads quickly and leverage the surrounding ecosystem, with a focus on extensibility, adaptability, and multi-tenancy.

The presentation elaborated on the architecture, illustrating how VCF provides a declarative API surface, the vSphere supervisor, to consume all resources through Kubernetes APIs. This enables users to leverage familiar tools, such as `kubectl`, and a new VCF CLI. The VCF CLI is designed to interact with resources using plugins, similar to those found in public cloud CLIs. A key benefit is that adding a new service to the ecosystem automatically makes it discoverable through the existing CLI or UI. The resources are like Lego blocks within the same bucket. They can be picked up and used to construct application and workload requirements.

A demo showcased a three-tiered application (MySQL database in a VM, VKS cluster with front-end and back-end apps) deployed entirely through Argo CD and GitOps principles. All the services are used, including the secret service, volume service, network service, and the VM image service. The application is deployed by simply pasting the application YAML, and Argo does its magic. While acknowledging the inherent complexity in deploying diverse application form factors, Wolf clarified that the intent isn’t to mandate such complexity, but rather to provide the flexibility to address specific needs through a unified API service and a namespace construct for isolation. This highlights the benefits of discoverability through the same API.


Deploying Your Apps in vSphere Kubernetes Service is Easy with VMware by Broadcom

Event: Tech Field Day at KubeCon North America 2025

Appearance: VMware by Broadcom Presents at Tech Field Day at KubeCon North America 2025

Company: VMware by Broadcom

Video Links:

Personnel: William Arroyo

This session showcases vSphere Kubernetes Service’s compatibility with existing setups, its Kubernetes-first API, and a rich ecosystem of Out-Of-The-Box (OOTB) services, such as ArgoCD. See a comprehensive, automated workflow in action, from provisioning a new VKS cluster with Terraform to deploying a sample microservices application via ArgoCD, and viewing metrics with integrated Prometheus and Telegraf. We’ll also show how platform engineers utilize Istio to connect, secure, control, and observe services across different infrastructures in VKS deployments.

VMware by Broadcom’s presentation at KubeCon North America 2025, delivered by William Arroyo, highlighted the ease of deploying applications in vSphere Kubernetes Service (VKS). The core of the presentation focused on demonstrating a streamlined workflow. This workflow begins with the platform engineer using Terraform to provision a new VKS cluster and subsequently deploying an example microservices application via ArgoCD. The session incorporated out-of-the-box (OOTB) services, such as ArgoCD, and integrated Prometheus and Telegraf to display metrics. The session also explained how Istio is utilized by platform engineers to connect, secure, control, and observe services across different infrastructures in VKS deployments.

The presentation walked through a complete workflow, combining the roles of an app developer and a platform engineer. The speaker utilized several components, like VKS, cloud services such as cert-manager, and Argo CD. These services were used to bootstrap the cluster with essential elements and applications, including Istio. Crucially, the speaker emphasized the use of APIs accessible via tools like `kubectl`, Terraform, and GitOps, allowing platform engineers flexibility. The demo showcased a Terraform apply command that created a supervisor namespace, an Argo CD instance, and a Kubernetes cluster.

In essence, the presentation focused on how VKS streamlines the entire lifecycle, making it easy to deploy, manage, and observe applications. Through the demonstration of Terraform and Argo CD integration, the speaker underscored the platform’s Kubernetes-first API approach and its compatibility with established tools and workflows. Further, the presentation highlighted the integration of fleet management features and cloud services. The session focused on the core components and the flexibility and power of the VMware by Broadcom offering.


vSphere Kubernetes Service for Platform Engineers – What You Didn’t Know with VMware by Broadcom

Event: Tech Field Day at KubeCon North America 2025

Appearance: VMware by Broadcom Presents at Tech Field Day at KubeCon North America 2025

Company: VMware by Broadcom

Video Links:

Personnel: Timmy Carr

This session will highlight the robust capabilities of VKS for platform engineers, focusing on deep technical insights and real-world value. Discover how VKS offers a fully conformant Kubernetes runtime, simplified lifecycle management with 2-click cluster deployment, and built-in guardrails to ensure stability. We’ll address common pain points and demonstrate how VKS directly solves them, leading to faster development cycles and reduced operational overhead.

The VMware by Broadcom presentation at Tech Field Day at KubeCon North America 2025 focused on vSphere Kubernetes Service (VKS) within the VMware Cloud Foundation (VCF) framework. VKS delivers fully conformant, certified Kubernetes in a user’s data center, ensuring that applications running in other clouds can seamlessly operate on VKS. VCF acts as a cloud within the data center, offering services such as virtual machines, containers, and Kubernetes to end users. The presentation highlighted the role of the cloud administrator in enabling platform engineers to deliver Kubernetes through VKS.

VKS integrates deeply with the VCF infrastructure, managing dependencies for Kubernetes cluster upgrades and offering seamless integration with GPU, storage, and compute resources. VMware emphasizes the rapid delivery of vSphere Kubernetes Releases (VKRs), aiming to release new versions within two months of the upstream Kubernetes community’s releases. The speaker addressed a question about the “service” designation in VKS, clarifying that it refers to a Kubernetes service running on a broader cloud platform with capabilities such as network isolation, object storage, and managed databases. VKS supports multi-cluster management, allowing users to manage and lifecycle their Kubernetes clusters, introspect workloads, manage security policies, and protect data via Valero.

The presentation clearly distinguished VKS and VCF from Tanzu, explaining that Tanzu is a developer-focused platform for delivering code to production, potentially running on top of VCF but not included by default. In a demo, it was highlighted how VKS clusters could be deployed quickly by users via a self-service portal that leveraged upstream Cluster API, and that every aspect of Kubernetes cluster infrastructure could be customized and versioned for declarative management via tools like ArgoCD. The presenter emphasized that VCF delivers these services in a consumable fashion for end users, transforming the traditional virtualization platform into a self-service cloud.


Cisco AI Cluster Networking Operations DLB Demo with Paresh Gupta

Event: Networking Field Day 39

Appearance: Cisco Presents at Networking Field Day 39

Company: Cisco

Video Links:

Personnel: Paresh Gupta

Paresh Gupta concluded the deep dive by focusing on the most complex challenge in AI networking: congestion and load balancing in the backend GPU-to-GPU fabric. He explained that while operational simplicity and cabling are critical, the primary performance bottleneck, even in non-oversubscribed networks, is the failure of traditional ECMP load balancing. Because ECMP hashes flows randomly, it creates severe traffic imbalances, where one link may be congested at 105% capacity while another sits idle at 60%. This non-uniform utilization, not a lack of total capacity, creates congestion, triggers performance-killing pause frames, and can lead to out-of-order packets, which are devastating for tightly coupled collective communication jobs.

To solve this, Cisco has developed advanced load-balancing techniques, moving beyond simple ECMP. Gupta detailed a “flowlet” dynamic load balancing (DLB) approach, where the switch detects inter-packet gaps to identify a flowlet and sends the next flowlet on the current, least-congested link. More importantly, he highlighted a fully validated, joint-reference architecture codesigned with NVIDIA. This solution combines Cisco’s per-packet DLB, running on its switches, with NVIDIA’s adaptive routing and direct data placement capabilities on the SuperNIC. This handshake between the switch and the NIC is auto-negotiated, and Gupta showed video benchmarks of a 64-GPU cluster where this method improved application-level bus bandwidth by 35-40% and virtually eliminated pause frames compared to standard ECMP.

This advanced capability, Gupta explained, is made possible by the P4-programmable architecture of Cisco’s Silicon One ASIC, which allows new features to be delivered without a multi-year hardware respin. He framed this as the foundational work that is now being standardized by the Ultra Ethernet Consortium (UEC), of which Cisco is a steering member. By productizing these next-generation transport features today, Cisco is able to provide a consistent, high-performance, and validated networking experience for any AI environment, offering enterprises a turnkey solution that rivals the performance of complex, custom-built hyperscaler networks.


Cisco AI Networking Cluster Operations Deep Dive

Event: Networking Field Day 39

Appearance: Cisco Presents at Networking Field Day 39

Company: Cisco

Video Links:

Personnel: Paresh Gupta

Paresh Gupta’s deep dive on AI cluster operations focused on the extreme and unique challenges of high-performance backend networks. He explained that these networks, which primarily use RDMA over Converged Ethernet (ROCE), are exceptionally sensitive to both packet loss and network delay. Because ROCE is UDP-based, it lacks TCP’s native congestion control, meaning a single dropped packet can stall an entire collective communication operation, forcing a costly retransmission and wasting expensive GPU cycles. This problem is compounded by AI traffic patterns, such as checkpointing, where all GPUs write to storage simultaneously, creating massive incast congestion. Gupta emphasized that in these environments, where every nanosecond of delay matters, traditional network designs and operational practices are no longer sufficient.

Cisco’s strategy to solve these problems is built on prescriptive, end-to-end validated reference architectures, which are tested with NVIDIA, AMD, Intel Gaudi, and all major storage vendors. Gupta detailed the critical importance of a specific Rail-Optimized Design, a non-blocking topology engineered to ensure single-hop connectivity between all GPUs within a scalable unit. This design minimizes latency by keeping traffic off the spine switches, but its performance is entirely dependent on perfect physical cabling. He explained that these architectures are built on Cisco’s smart switches, which use Silicon One ASICs and are optimized with fine-tuned thresholds for congestion-notification protocols like ECN and PFC.

The most critical innovations, however, are in operational simplicity, delivered via Nexus Dashboard and HyperFabric AI. These platforms automate and hide the underlying network complexity. Gupta highlighted the automated cabling check feature. The system generates a precise cabling plan for the rail-optimized design and provides a task list to on-site technicians; the management UI will only show a port as green when it is cabled to the exact correct port, solving the pervasive and performance-crippling problem of miscabling. This feature, which customers reported reduced deployment time by 90%, is combined with job scheduler integration to detect and flag performance-degrading anomalies, such as a single job being inefficiently spread across multiple scalable units.


Introduction to Cisco AI Cluster Networking Design with Paresh Gupta

Event: Networking Field Day 39

Appearance: Cisco Presents at Networking Field Day 39

Company: Cisco

Video Links:

Personnel: Paresh Gupta

Paresh Gupta, a principal engineer at Cisco focusing on AI infrastructure, began by outlining the diverse landscape of AI adoption, which spans from hyperscalers with hundreds of thousands of GPUs to enterprises just starting with a few hundred. He categorized these environments by scale—scale-up within a server, scale-out across servers, and scale-across between data centers—and by use case, such as foundational model training versus fine-tuning or inferencing. Gupta emphasized that the solutions for these different segments must vary, as the massive R&D budgets and custom software of a hyperscaler are not available to an enterprise, which needs a simpler, more turnkey solution.

Gupta then deconstructed the modern AI cluster, starting with the immense computational power of GPU servers, which can now generate 6.4 terabits of line-rate traffic per server. He detailed the multiple, distinct networks required, highlighting a recent shift in best practices: the front-end network and the storage network are now often converged. This change is driven by cost savings and the realization that front-end traffic is typically low, making it practical to share the high-bandwidth 400-gig fabric. This converged network is distinct from the inter-GPU backend network, which is dedicated solely to GPU-to-GPU communication for distributed jobs, as well as a separate management network and potentially a backend storage network for specific high-performance storage platforms.

Finally, Gupta presented a simplified, end-to-end traffic flow to illustrate the complete operational picture. A user request does not just hit a GPU; it first traverses a standard data center fabric, interacts with applications and centralized services like identity and billing, and only then reaches the AI cluster’s front-end network. From there, the GPU node may access high-performance storage, standard storage for logs, or organization-wide data. If the job is distributed, it ignites the inter-GPU backend network. This complete flow, he explained, is crucial for understanding that solving AI networking challenges requires innovations at every point of entry and exit, not just in the inter-GPU backend.


The Age of Operations: Third Party Views of DCF Innovation

Event: Networking Field Day 39

Appearance: Nokia Presents at Networking Field Day 39

Company: Nokia

Video Links:

Personnel: Scott Robohn

Scott Robohn of the consulting firm Solutional provided a third-party, operational perspective on data center innovation, based on a collaboration with The Futurum Group and Nokia. He introduced his background as a former network engineer and Tech Field Day delegate, now focused on NetOps and AI adoption. Robohn’s central thesis is that with AI driving new infrastructure builds and hardware becoming normalized, the next major performance gains in data centers will come from a relentless focus on improving operations. The joint project between Solutional, Futurum, and Nokia aimed to validate this by looking at AI’s dual role: both as a driver for new network infrastructure and as a set of tools to be used for network operations.

Robohn detailed the market trends shaping this new age of operations, starting with AI as a durable technology driving massive fabric build-outs by hyperscalers and a new class of NeoCloud providers. He defined NeoClouds as specialized providers focused on renting expensive, complex GPU-interconnect infrastructure for workloads like model training and video rendering. He then argued that as data center hardware has normalized around merchant silicon and stable architectures like Spine-Leaf, the hardware itself is no longer the key differentiator. This consistency makes automation more achievable and shifts the entire industry’s focus to operations as the critical area for innovation and reliability.

To validate this operational focus, the project stood on four legs. First, a Futurum reliability survey which found that network reliability is the number one purchasing criterion for data center infrastructure, far outweighing cost, and that human error remains a major issue. Second, a collaboration with Bell Labs on a reliability model, which concluded the most significant reduction in downtime comes from fixing operations-related issues. Third, interviews with Nokia’s own 80,000-employee internal enterprise IT team, who are successfully migrating their complex, high-stakes manufacturing and office networks to Nokia’s SR Linux and EDA platform. Finally, general market engagement confirming a palpable, industry-wide appetite for a new class of automation and AIOps tools.


Root Cause Analysis with Nokia AI Operations Automation

Event: Networking Field Day 39

Appearance: Nokia Presents at Networking Field Day 39

Company: Nokia

Video Links:

Personnel: Clayton Wagar

Clayton Wagar introduced Nokia’s AI-driven approach to root cause analysis, focusing on solving difficult day-two operational challenges. The presentation highlighted the chronic pain of hidden impairments or gray failures, where traditional monitoring systems fail because physical links appear active while protocols or services are down. The goal of Nokia’s deep RCA tool is to move beyond simple port-up/port-down alarming by correlating end-to-end application connectivity (from VM to VM) with all layers of the network, including the underlay, overlay, and control plane, to dramatically compress troubleshooting time.

A live demonstration was shown on the EDA SaaS platform using a real hardware Spine-Leaf network. The team introduced a gray failure by impairing a fiber link in a way that kept the interface status “up” but caused the BFD and BGP protocols to fail. Wagar explained that the AI’s multi-agent workflow correctly diagnosed this. Instead of using one large, monolithic model, a planning model first determines which tool-calling agents to deploy. These agents gather specific, relevant data from logs, topology, and configuration, which is then filtered and passed to a reasoning model. This agentic-based curation of data is Nokia’s key to reducing costs and avoiding the AI hallucinations that would otherwise be a risk in mission-critical networks.

The tool’s capabilities were further demonstrated by successfully identifying a classic, hard-to-find MTU mismatch. Another key feature highlighted was the “Time Machine,” which allows an operator to select a past timeframe, such as thirty minutes prior to an event, and run the same AI-driven root cause analysis on the historical data from that moment. The entire process concludes with the AI generating a comprehensive report that provides a human-readable summary, a confidence score, and the specific evidence gathered by the agents, effectively solving a complex logic puzzle that would have taken an engineer hours or days to manually diagnose.


Delivering Nokia Enhanced AIOps with the Right Foundations

Event: Networking Field Day 39

Appearance: Nokia Presents at Networking Field Day 39

Company: Nokia

Video Links:

Personnel: Bruce Wallis

Bruce Wallis pivoted the discussion to Nokia’s AIOps capabilities, centered on a new natural language interface called Ask EDA. This feature, which resembles a ChatGPT for the network, allows operators to interact with the EDA platform through a simple chat box. The core idea is to abstract the complexity of network operations, enabling users to ask plain-English questions, such as “list my SRL interfaces” or “is BFD enabled?”, and receive back live, structured data, tables, and even on-the-fly visualizations like pie charts and line graphs. This approach removes the need for operators to understand the complex underlying Yang models or schemas for each vendor, as the AI handles the translation from human language to machine query.

The right foundation for this capability, as Wallis explained, is not a single, monolithic, trained model, but a flexible agentic AI framework. In this model, the central LLM acts as a brain that coordinates a set of pluggable agents, or tools, each with a specific function. The most powerful aspect of this design is its real-time extensibility. Wallis demonstrated this by first showing the AI failing to understand a request to “enable the locator LED.” He then installed a new support application from EDA’s App Store; when asked again, the AI agent immediately recognized and used this new tool to successfully execute the command. This app-based approach allows Nokia to add new troubleshooting workflows and capabilities on the fly, without retraining the model or upgrading the core platform.

This agentic framework is applied directly to troubleshooting and operations. Wallis showed how “Ask EDA” can be used to investigate “deviations,” or configuration drift, where the running config no longer matches the intended state. In another example, with a BGP peer alarm active, the AI was asked to investigate. It used its agents to query various resources, analyze the topology, and correctly identified that the BGP manager process had crashed and restarted, providing a direct link to the deviation. Wallis emphasized that this method of using the LLM to query factual data from live telemetry and tools is how Nokia is addressing the problem of hallucinations, ensuring the AI’s answers are grounded in reality.


Nokia Event-Driven Automation (EDA) Multi Vendor – Deliver on the Promise

Event: Networking Field Day 39

Appearance: Nokia Presents at Networking Field Day 39

Company: Nokia

Video Links:

Personnel: Bruce Wallis

Bruce Wallis, Product Manager for Nokia’s EDA (Event-Driven Automation) platform, began by revisiting the core assertions made when the product was unveiled a year prior. He reiterated that EDA was built on the same successful principles as Kubernetes: abstraction and a declarative model. The goal was to apply this logic to networking, creating a ubiquitous platform that could define a unit of work for the network, just as Kubernetes did for compute workloads. This approach aims to normalize networking primitives like interfaces and BGP peers, allowing an operator to declare the desired end state without scripting the specific sequential steps, letting the platform handle the how.

The presentation’s main focus was delivering on the multi-vendor promise made at the previous event. Wallis conducted a live demo, bootstrapping an eight-node, dual-stack fabric underlay using a single 58-line YAML file. This high-level abstract definition was automatically reconciled by EDA, which then generated and pushed the correct, vendor-specific configurations to four different operating systems running on the leaf switches: Nokia SR Linux, Nokia SROS, Cisco Nexus, and Arista EOS. This demonstrated the platform’s ability to manage a heterogeneous network through one common model.

Finally, Wallis addressed other key platform features, including the ability to bubble operational state up into the abstract model, allowing operators to view the “health” of the entire fabric rather than just individual components. He also clarified for delegates that while he used YAML for demo speed, the platform is fully operable via a form-based UI for users unfamiliar with programmatic inputs. He concluded the demo by successfully deploying a complex EVPN overlay network across the newly built, multi-vendor underlay, again using a single, simple declarative input.


Exploring the Power and Potential of Enhanced AIOps with Nokia

Event: Networking Field Day 39

Appearance: Nokia Presents at Networking Field Day 39

Company: Nokia

Video Links:

Personnel: Clayton Wagar

Clayton Wagar, leading the AI practice for Nokia’s IP division, framed his presentation as creating a “through line” connecting the history of network operations to its AI-driven future. Recalling his own start managing a 911 data center with manual, CLI-driven processes, he traced the evolution of the industry as operators moved from mastering workflows in their heads to writing them down and eventually using automation. Wagar emphasized that as AI is introduced, it’s crucial to be prescriptive about its use and to understand its two main facets: the “plumbing” aspect of building massive networks to support AI, and the application of AI tools to network operations itself.

To illustrate the challenge of applying AI to mission-critical systems, Wagar told a story about a 1943 discussion at Bell Labs between Claude Shannon and Alan Turing. They faced a choice: build a computer like a human brain (a neural network) or like an adding machine (a deterministic system). They chose the adding machine, not only because the technology for neural networks didn’t exist, but critically because telcos and governments required predictable, deterministic outputs, not a system that might hallucinate. This historical context highlights the primary challenge Nokia addresses today: reducing AI hallucinations to make the technology safe for essential, real-world networks where reliability is paramount.

Wagar then connected this to the modern concept of autonomous networks, such as the levels defined by TM Forum. He proposed that these frameworks were largely developed before modern AI and assumed a deterministic path, whereas AI introduces a new, separate plane of capability. He pointed to Google’s public journey toward autonomous networking, which leverages custom-built AI agents to move beyond simple event-driven workflows to a truly autonomous SRE model. Wagar concluded by positioning Nokia’s strategy as learning from these leaders, blending AI and traditional automation to inform their product development and establish new best practices for the industry.


Introduction to Nokia with Andy Lapteff

Event: Networking Field Day 39

Appearance: Nokia Presents at Networking Field Day 39

Company: Nokia

Video Links:

Personnel: Andy Lapteff

Andy Lapteff, a network engineer who became a product marketing manager for Nokia Data Center, introduced his presentation by sharing his personal journey. He admitted that, like many, he previously only knew Nokia for its indestructible cell phones and was unaware of its significant presence in the data center market. It was at a previous Tech Field Day event, attending as a delegate, that he learned Nokia had been building mission-critical networking infrastructure for decades for entities like air traffic control and power grids, and was now applying those same principles of ultra-reliability to the data center. This revelation was so compelling that it motivated him to join the company.

Lapteff contrasted Nokia’s approach with his own past experiences working midnights in a network operations center (NOC), where networks were complex, fragile, and “on fire all the time,” and a “no change Fridays” mentality was common. His motivation at Nokia is driven by the memory of those challenges, aiming to build more robust and reliable networks for data center operators. He expressed genuine excitement for the value Nokia provides, which he feels is real and tangible, unlike other vendor jobs he has held.

The presentation set the stage for a deeper dive into the future of networking, particularly focusing on the often-confusing landscape of AI, AIOps, and the different levels of network autonomy. Lapteff noted that while the industry is largely at a manual or low-automation level, Nokia is pushing toward full autonomous operations. He concluded by previewing the day’s key topics, promising impressive demonstrations on “Agentic AI ops in action,” AI-driven root cause analysis, the automation of AI back-end fabrics, and a much-anticipated update on Nokia’s potential support for non-Nokia hardware.


The Resource Costs of AI

Event: Networking Field Day 39

Appearance: Networking Field Day 39 Delegate Roundtable Discussion

Company: Tech Field Day

Video Links:

Personnel: Tom Hollingsworth

The Networking Field Day 39 roundtable, led by Tom Hollingsworth, dove straight into the massive resource drain caused by the current AI boom. The delegates discussed how the industry is already behind the eight ball on power, with AI’s exponential demand making the existing, outdated power grid’s problems significantly worse. This isn’t just a data center issue. It’s causing widespread component shortages for essentials like RAM and GPUs, affecting everyone from enterprise users to home gamers. The conversation highlighted that consumers are ultimately paying for this AI race, not just through new tech, but through soaring power and water bills as AI companies with deep pockets outbid ordinary consumers for finite resources.

In response to this power crisis, the discussion shifted to solutions. The delegates noted a serious new investigation into small modular nuclear reactors and even restarting old plants like Three Mile Island, things unthinkable just a few years ago, alongside more hopeful developments in solar. On the networking side, this resource demand is forcing the creation of entirely new, expensive technologies like Ultra Ethernet and massive 800-gig switches just to keep these AI data centers fed. These come with huge R&D costs, which will inevitably be passed down, raising the question of whether networking costs are about to go through the roof for reasons outside the network engineer’s control.

Finally, the panel debated the future of AI itself, noting the buzzword is becoming meaningless as the industry pivots from massive LLMs to more efficient, domain-specific smaller models (SLMs) and agentic AI. The group observed that AI might move from giant cloud data centers to running locally on devices with new NPUs, or even on standard laptops. Ultimately, the delegates concluded that the future of AI won’t just be decided by the hyperscalers. It will be shaped by consumers and engineers through the products they choose to use and the companies they criticize for wasting resources.


Graphiant Demos with Vinay Prabhu

Event: Networking Field Day 39

Appearance: Graphiant Presents at Networking Field Day 39

Company: Graphiant

Video Links:

Personnel: Vinay Prabhu

Chief Product Officer Vinay Prabhu demonstrated the Graphiant infrastructure, first focusing on the network for AI. He framed AI as a massive publisher-subscriber problem and demoed a B2B data exchange where services, like GPU farms, can be published to a personal marketplace. This allows partners (both on and off-network) to be securely connected in minutes, automating complex routing, NAT, and security. This capability is then monitored by the Data Assurance Dashboard, which uses a real-time telemetry pipeline (correlating NetFlow, DNS, and DPI) to provide deep visibility without decrypting payloads. This dashboard identifies malicious threats, provides full auditability, and offers an “Uber-like” spatial and temporal view, allowing operators to prove an application’s exact path and confirm compliance with geofencing policies.

This visibility enables absolute control, where users can define policies for performance, path, or risk. Prabhu confirmed customers can enforce policies to drop traffic rather than failover to a non-compliant path, ensuring governance is never compromised. The presentation concluded with the AI for the network component: GINA, the Graphiant Intelligent Network Assistant. GINA acts as a virtual team member, capable of running a “60-minute stand-up in 60 seconds” by generating guided operational and compliance reports. Prabhu stressed that GINA does not train on customer data; it uses Generative AI to interpret queries and accesses information strictly through the user’s existing role-based access control (RBAC) APIs, ensuring data remains secure.


Graphiant: The AI Strategy

Event: Networking Field Day 39

Appearance: Graphiant Presents at Networking Field Day 39

Company: Graphiant

Video Links:

Personnel: Vinay Prabhu

Vinay Prabhu, Chief Product Officer at Graphiant, outlined a two-part strategy encompassing network for AI and AI for network. The network for AI pillar addresses the challenge of AI being a massive distributed publisher and subscriber problem, where data is generated in one location and inference happens in another, often across different business boundaries. To manage this, Graphiant provides a platform for secure data exchange, analogous to financial or healthcare workloads. Prabhu emphasized that simplifying exchange is insufficient without trust, using a ride-sharing app analogy: just as a passenger needs to see the driver and the path, enterprises need real-time observability, auditability, and centralized control to program governance policies directly onto the global fabric.

The second pillar, AI for the network, is embodied by GINA (Graphiant Intelligent Network Assistant). GINA is designed to act as a virtual member of the operations team, automating complex, time-consuming tasks. Prabhu gave the example of a CSO requesting a monthly compliance report, a task that might take an hour to manually collate data from various dashboards and databases. GINA can generate this report, along with threat intelligence and infrastructure insights, almost instantly. Prabhu summarized GINA’s value as running a 60-minute stand-up in 60 seconds, buying back valuable time for practitioners to focus on innovation rather than manual data gathering.


Graphiant Use Cases with Arsalan Khan

Event: Networking Field Day 39

Appearance: Graphiant Presents at Networking Field Day 39

Company: Graphiant

Video Links:

Personnel: Arsalan Mustafa Khan

Graphiant’s CSO, Arsalan Khan, detailed use cases focused on this challenge, beginning with unified connectivity. He explained that the Graphiant fabric treats all endpoints, public clouds, data centers, and emerging AI neoclouds, as part of a single any-to-any fabric. This model eliminates the need for traffic backhauling, providing lower latency and guaranteed paths for high-bandwidth AI workloads, all while ensuring data privacy with end-to-end encryption that is never decrypted in transit.

Khan then highlighted business-to-business data exchange and data assurance as key enablers for AI. The platform simplifies partner collaboration, which is critical for many AI ecosystems, by handling network complexities like IP overlapping and NAT. This capability extends to partners not on the Graphiant platform and includes the ability to dynamically revoke access if a partner is breached. The core data assurance use case provides a centralized tool for CISO and governance teams. Using role-based access control, they can enforce network-level policies, such as ensuring specific data never leaves a geographical boundary, rather than relying on individual application developers to implement compliance.

Finally, Khan addressed how this infrastructure specifically serves AI workloads. He clarified the strategy is “networking for AI,” meaning the platform is designed to offload the complex burden of security and data governance from AI applications to the network itself. This accelerates AI deployment by simplifying compliance. The system supports this with threat intelligence that, without inspecting encrypted payloads, uses public feeds and behavioral analysis. By classifying normal application flows, the network can detect and flag erratic behavior, providing an essential layer of security for moving and processing the large, sensitive data “haystacks” required by AI.


About Graphiant NaaS with Arsalan Khan

Event: Networking Field Day 39

Appearance: Graphiant Presents at Networking Field Day 39

Company: Graphiant

Video Links:

Personnel: Arsalan Mustafa Khan

As enterprises accelerate AI adoption, data governance and network security have become inseparable. In Graphiant’s Networking Field Day presentation titled, “Why Data Governance Demands a Unified and Secure Approach to AI Networking,” Graphiant explores how a secure, compliant, and unified networking infrastructure is essential to enable responsible AI at scale.

Arsalan Khan framed the core problem: enterprises are investing heavily in AI, but their data is siloed across on-prem data centers, multiple clouds, and emerging neocloud providers. This creates massive infrastructure headaches, high costs, and significant security risks. The challenge is compounded by complex data governance regulations, especially for sensitive PII in finance and healthcare. Khan noted that while traditional networking struggles to “catch up” to new technologies, AI demands moving the “whole haystack,” not just finding needles, making network-level control and compliance essential from the start.

Graphiant’s solution is a Network-as-a-Service (NaaS) built on a stateless core, which functions as an overlay/underlay network operated by Graphiant using leased fiber. This provides a single, ubiquitous fabric for any-to-any connectivity with SLA guarantees. The key, Khan emphasized, is simplifying the “plumbing” so businesses can focus on their AI goals. The platform provides centralized visibility and control over metadata, allowing enterprises to see traffic paths and applications (without decrypting payloads) and enforce granular policies, such as guaranteeing that specific data never leaves a geographical boundary. This approach aims to provide the auditability, security, and cost-effectiveness required to manage modern AI data flows.


Futurum Signal – Agentic AI Platforms for Enterprise

Event: AI Field Day 7

Appearance: Futurum Signal Presentation at AI Field Day 7

Company: The Futurum Group

Video Links:

Personnel: Stephen Foskett

In his presentation at AI Field Day 7, Stephen Foskett, President of Tech Field Day at The Futurum Group, introduced the Futurum Signal, a groundbreaking vendor evaluation survey designed to challenge traditional analyst methodologies. The Signal leverages Agentic AI to provide a fresh perspective on evaluating major enterprise AI platforms. Unlike traditional, manual processes that involve extended data collection from vendors and often lead to out-of-date reports, this new method utilizes a combination of proprietary data, industry analysis, AI-driven insights, and human intelligence to generate timely, comprehensive assessments. The process is streamlined to offer enterprise decision-makers updated insights, highlighting the agility of AI-enhanced analytics in evolving technical landscapes.

Foskett shared the latest Signal Report focusing on Agentic AI platforms for enterprises, evaluating major players and identifying strategic partners best suited for enterprise buyers aiming to revolutionize business processes with AI. Through a sophisticated AI-driven system, analysts within the Futurum Research Group assess a pool of significant companies to determine their fit as partners in the AI space. This evaluation considers data integrity, collaboration among multiple agents, governance, and enterprise-oriented controls, all while illuminating promising trends for advanced AI deployment. The report places Microsoft and Salesforce as top contenders in the elite zone, recognized for their comprehensive suite of tools suitable for the largest enterprise clients. Google, IBM, SAP, and ServiceNow are also notable, while AWS and Oracle occupy the established zone, reflecting the dynamic and competitive landscape of agent-based enterprise AI solutions.

The integration of AI into the analytical process allows for real-time data processing and the generation of reports that incorporate recent and relevant updates, such as financial results or organizational changes within evaluated companies. This capability ensures that the information remains fresh and actionable for decision-makers. Futurum’s commitment to leveraging AI as a foundational element in their signal reports underscores a strategic shift toward more responsive, data-enriched analyses. Foskett emphasized the importance of timely and frequent updates, projecting that future reports, including those for Tech Field Day, will be heavily influenced by insights gathered from AI-driven data, aiming for transformative impacts in technology evaluation and enterprise strategy.


Battle of the Bots – Which AI Assistant Delivers with Calvin Hendryx-Parker

Event: AI Field Day 7

Appearance: Calvin Hendryx-Parker Presents at AI Field Day 7

Company: Ignite, Six Feet Up

Video Links:

Personnel: Calvin Hendryx-Parker

Calvin Hendryx-Parker, Co-Founder and CTO of Six Feet Up, delivered an insightful presentation at AI Field Day 7, evaluating the efficacy of various AI coding assistants in real-world developer workflows. His talk built upon an earlier session by exploring updates in agentic AI tools, which have become indispensable in modern coding practices. These tools, including Aider, Goose, Claude Code, Cursor, Juni, and OpenAI Codex, interact with a developer’s environment via APIs, leveraging protocols such as the Model Context Protocol (MCP) to enable autonomous or semi-autonomous coding assistance. Each AI tool has unique strengths, such as differential context management capabilities, sub-agent functionality, and tool-specific interfaces, which can deeply affect a developer’s productivity and workflow efficiency.

Hendryx-Parker’s discussion emphasized the transformative impact of AI assistants on developers’ operational efficiencies, highlighting specific products and protocols. Aider, for instance, is noted for its integration with Git and can handle local models such as Llama to ensure data privacy, while also providing a semiautonomous coding experience through its architect and code modes. Goose by Block is lauded for its inclusivity on multiple models, including support for OpenRouter. It stands out with its recipes for repeated task automation and container isolation to mitigate risk during operations. Claude Code, developed by Anthropic, supports proprietary tools and is inherently more empathetic, which can be advantageous during discussions or negotiations, despite its non-open-source nature.

The presentation culminated in an analysis of the trajectory and potential dominators among these AI tools. Goose and Claude Code were seen as potential leaders due to their robust feature sets and wide-ranging usefulness for enterprise and individual users alike. Goose’s integration with GUI tools indicates a focus on a wider market, possibly covering both professional developers and office workers with coding needs. Hendryx-Parker also touched upon innovations such as the Agent Control Protocol (ACP) for enhanced tool interoperability and pointed to the necessity for developers, especially juniors, to familiarize themselves with these tools to maintain a competitive edge in the rapidly evolving technological landscape. The talk was a comprehensive overview of the AI coding assistant landscape, providing a detailed insight into each tool’s unique capabilities and potential for streamlining developer productivity.


Cloud Field Day 24 Delegate Roundtable Discussion

Event: Cloud Field Day 24

Appearance: Cloud Field Day 24 Delegate Roundtable Discussion

Company: Tech Field Day

Video Links:

Personnel: Alastair Cooke

This final session of Cloud Field Day 24 features a roundtable discussion with the delegates to explore their impressions of the event and delve into topics not thoroughly covered during the presentations. The delegates, who practically work with these products in complex environments, aim to discuss pertinent issues and the right solutions for their customers, particularly regarding the evolution of hybrid or private cloud and how it differs from public cloud.

The discussion centered on the shift towards on-premises cloud solutions, highlighted by companies like Oxide and Morpheus, that mimic cloud functionality without replicating the public cloud model. A key theme was the concept of multi-cloud, which enables workload placement and movement based on specific needs, alongside essential observability and management capabilities. Data management, particularly data sovereignty, was identified as a major driver for on-premise solutions, due to the physics involved in data transfer and the vendors’ belief that current cloud services often fail to meet enterprise data sovereignty requirements.

Further topics included the financial implications of diverse platforms, the rising costs of cloud-based AI agents, and the need for simplification in IT operations amid increasing complexity and staffing challenges. The discussion also addressed the security vulnerabilities in AI and the importance of incorporating security into AI infrastructures from the outset, rather than as an afterthought. Finally, the panel discussed the distinct approaches to cloud solutions, contrasting single-source providers like Oxide with vendors that enable best-of-breed integrations, acknowledging the challenges of integrating new solutions into complex, legacy environments.


« Previous Page
Next Page »