The Shift to AI Infrastructure Is Underway. Is Your Enterprise Ready?

Enterprise IT leaders have moved past the AI hype cycle and into execution.

The conversation has changed from what AI can do to what it takes to run it. That shift is driving unprecedented focus on the foundational layers of IT and the AI Networking Summit cover it all:  Infrastructure, Networking, Automation, and Security. The AI Networking Summit brings these domains together into four dedicated tracks designed to help enterprises move from AI pilots to scalable, secure deployment. This is where strategy meets implementation and where the AI-ready enterprise takes shape.

AI Infrastructure

Build the foundation for enterprise AI. This track covers private infrastructure, cost models, and scaling AI workloads in production.

We are currently accepting applications for this Roundtable. If you are a  CIO, CTO, Heads/VPs of network Infrastructure, Sr Manager, or Director of Network Infrastructure, apply to join here: https://share.hsforms.com/1cmAOVo8hTW-CKdWr6pehLA3kiev 

As enterprises operationalize AI—from generative copilots to real-time analytics and autonomous workflows—the underlying network is emerging as a critical enabler of AI performance, scale, and security. AI workloads are distributed across hybrid and multi-cloud environments, rely on massive data movement, and demand ultra-low latency connectivity between data, compute, and users. Traditional network architectures, designed primarily for predictable application traffic, are increasingly being stretched by these new patterns.

This breakfast roundtable will explore how enterprise network infrastructure must evolve to support AI-driven organizations. The discussion will examine the architectural implications of AI workloads on WAN, cloud connectivity, and edge infrastructure, as well as operational considerations such as observability, security, and automation.

Participants will also reflect on how CIOs and network leaders are aligning network strategy with AI adoption roadmaps—ensuring the network becomes a strategic platform for innovation rather than a bottleneck. The session aims to exchange practical perspectives on preparing enterprise networks for the scale, speed, and intelligence required in the AI era.

Discussion Points

1. AI Workload Architecture: What Changes for the Network?

  • AI applications move large volumes of data between data lakes, training clusters, inference engines, and users across multiple clouds and regions.
  • How are AI workloads changing traffic patterns compared to traditional enterprise apps?
  • What new requirements are emerging for bandwidth, latency, and inter-cloud connectivity?

2. The Network as the AI Data Pipeline

  • AI performance often depends less on compute and more on how quickly data can move across environments.
  • How are enterprises thinking about WAN, backbone connectivity, and cloud interconnects as “data pipelines” for AI?

3. Multi-Cloud, Sovereignty, and the AI Geography Problem

  • AI workloads often run across multiple regions due to regulatory, cost, or compute availability constraints.
  • How are enterprises addressing connectivity challenges between regions, regulated markets, and hyperscalers?

4. AI for the Network vs. Network for AI

  • AI is not only consuming networks—it is also transforming network operations.
  • How are enterprises applying AI for network observability, traffic optimization, and incident response?
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As AI models continue to scale, both training and inference are growing rapidly in operational importance. Training pushes the limits of compute density and interconnect scale, while inference now dominates production workloads. Together, these forces are reshaping AI system architectures.

Meeting these demands requires a next-generation networking fabric that can:

  • Scale up within and across a small number of racks to tightly couple XPUs for high-throughput training and low-latency inference
  • Scale out across entire data centers using flat, high-performance topologies that support large-scale training and high-fanout inference workloads
  • Scale across geographically distributed data centers, enabling unified AI fabrics that support million-plus-XPU training and inference environments.

We will present the latest advancements in industry initiatives—including Ethernet Scale-Up Networking (ESUN), Scale-Up Ethernet Transport (SUE-T), and Open Cluster Design for AI—and show how Ethernet is democratizing large-scale AI deployments through insights from G42 and other AI operators.

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Agentic AI overlays promise networks that sense, reason, and act—but the real challenge is treating AI agents as first-class identities in the network. Building on ONUG’s A2A and Agentic Overlay concepts, this session explores how to standardize identity, trust, and messaging among agents, and how those agents interact with the underlying network fabric.
Key Questions: – What should an A2A reference architecture look like in a large enterprise (broker vs. mesh, policy dialects, schemas)? – How do we provide Zero-Trust identity and least-privilege capability routing for agents issuing network changes? – What telemetry is needed to link agent intents → network changes → workload outcomes → cost? – How do you avoid “agent sprawl” and conflicting policies across multiple vendors’ AI assistants?
Takeaways: – A clear mental model of Agentic Overlays and A2A in networking. – Governance patterns for letting agents safely orchestrate NaaS, routing, and provisioning.

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Enterprise AI is moving from experimentation to production—and infrastructure teams now control whether their companies win or fall behind. We are living in a moment of exponential progress driven by AI, where capabilities accelerate rapidly and traditional planning cycles no longer apply. As agentic systems take on Tier 1 and Tier 2 operations, generative infrastructure designs itself, and governance frameworks mature, a new reality is emerging: AI is driving a workload repatriation wave—as data gravity, IP protection, control, and security concerns force new architectural and operating models. In exponential times, there is no luxury of delay—this keynote sets the stakes for moving fast to AI-enable your enterprise-controlled infrastructure and frames the economic, architectural, and operational shifts that will define the next two days.

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AI Networking

Redesign the network for the AI era with agentic overlays, A2A fabrics, and architectures built for autonomous systems.

ISP incidents remain one of the fastest ways to impact user experience and one of the hardest to prove quickly. In this session, we’ll walk through a real customer example and show how we detected an ISP issue early by comparing user-to-app path performance across regions and ISPs. We’ll also cover how “peer” telemetry (crowd signals across many environments) can help you validate whether an issue is inside your network, at an ISP, or specific to a single site.

We’ll focus on the practical workflow: spotting the anomaly, confirming it’s an ISP event, narrowing blast radius, and taking action—like rerouting users to a healthier egress/data center—to cut troubleshooting time dramatically.

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Agentic AI overlays promise networks that sense, reason, and act—but the real challenge is treating AI agents as first-class identities in the network. Building on ONUG’s A2A and Agentic Overlay concepts, this session explores how to standardize identity, trust, and messaging among agents, and how those agents interact with the underlying network fabric.
Key Questions: – What should an A2A reference architecture look like in a large enterprise (broker vs. mesh, policy dialects, schemas)? – How do we provide Zero-Trust identity and least-privilege capability routing for agents issuing network changes? – What telemetry is needed to link agent intents → network changes → workload outcomes → cost? – How do you avoid “agent sprawl” and conflicting policies across multiple vendors’ AI assistants?
Takeaways: – A clear mental model of Agentic Overlays and A2A in networking. – Governance patterns for letting agents safely orchestrate NaaS, routing, and provisioning.

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Enterprise AI is moving from experimentation to production—and infrastructure teams now control whether their companies win or fall behind. We are living in a moment of exponential progress driven by AI, where capabilities accelerate rapidly and traditional planning cycles no longer apply. As agentic systems take on Tier 1 and Tier 2 operations, generative infrastructure designs itself, and governance frameworks mature, a new reality is emerging: AI is driving a workload repatriation wave—as data gravity, IP protection, control, and security concerns force new architectural and operating models. In exponential times, there is no luxury of delay—this keynote sets the stakes for moving fast to AI-enable your enterprise-controlled infrastructure and frames the economic, architectural, and operational shifts that will define the next two days.

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AI Security

Secure the rise of AI agents with zero-trust frameworks, guardrails, and full visibility into machine-driven actions.

AI Automation

Transform operations from NOC to AOC with trusted automation, human-in-the-loop design, and agent-driven workflows at scale.

Special Programs

The Special Programs Track highlights the breakthrough technologies and initiatives shaping the future of enterprise IT including SONiC and Quantum Computing

This session explores the growing risk posed by quantum-enabled decryption, referred to as Q-Day, the point at which sufficiently powerful quantum computers could undermine widely used public-key cryptosystems. Attendees will examine what this shift means for today’s networks, including the “harvest now, decrypt later” risks and the operational impact of large-scale cryptographic compromise. The discussion will also look at quantum technologies as part of a forward-looking security strategy. Panelists will discuss where these approaches are most relevant, how they can complement broader migration efforts, and what organizations should be doing now to build resilient, tamper-evident communications that can withstand future quantum adversaries.

Who Should Attend:
Cybersecurity professionals, network engineers, IT security architects, and executives responsible for data protection in AI-driven environments. This session is especially valuable for those in finance, healthcare, government, and critical infrastructure sectors where encryption compromise would have severe consequences.

What You Will Learn:

  • The mechanics and realistic timelines of quantum decryption threats, including the impact of algorithms like Shor’s on current cryptographic standards.
  • Core principles of quantum networking, such as QKD and entanglement distribution, and how they differ from and complement classical security methods.
  • Strategies for assessing organizational exposure to quantum threats and planning migration to quantum-resistant and quantum-secured architectures.
  • Practical first steps for evaluating, piloting, and implementing quantum networking solutions to protect sensitive data flows against future decryption risks.
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This session examines quantum-safe networking as an emerging approach to strengthening security for high-bandwidth connections across large-scale enterprise data center environments. We will define key concepts in quantum-safe networking – including the role of quantum phenomena such as entanglement in supporting secure, tamper-evident communications over fiber infrastructure and hybrid solutions.
With cyber risk increasing and AI workloads driving demand for large-scale data movement, the discussion will explore where conventional cryptographic approaches face operational and lifecycle challenges on high-speed links, and how quantum-safe architectures may complement existing security models. The session will also address practical adoption considerations, including readiness assessments, implementation planning, and hybrid quantum-classical deployment strategies beyond PQC transitions already underway.
Attendees will leave with a clearer understanding of the technology landscape, relevant use cases, and a pragmatic framework for evaluating quantum networking within enterprise infrastructure roadmaps.

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AI Factories are expensive ecosystems. Design, Deployment, and workload scheduling need to be automated to ensure the desired outcome. This session shows how Aviz ONES delivers the blueprint for SONIC networks and end-end orchestration experience through partner integrations.

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eBay operates one of the most advanced enterprise networks built on SONiC and large-scale automation. In this technical deep dive, Rick Casarez provides an inside look at the architecture, design principles, and operational decisions behind eBay’s production SONiC deployment.

Rick will also demonstrate how eBay is applying AI-powered LLMs to assist and automate NOC workflows, enabling engineers to investigate incidents faster, interpret telemetry, and streamline troubleshooting runbooks. The session concludes with a look at how eBay’s network architecture is evolving to support AI workloads and more autonomous network operations.

Key Topics Covered
• Architecture of eBay’s production SONiC-based network
• Design principles behind eBay’s disaggregated networking strategy
• Hardware abstraction and multi-vendor switching architecture
• Automation frameworks and infrastructure-as-code for network operations
• Telemetry, observability, and operational tooling at scale
• Applying LLMs to assist and automate NOC workflows
• AI-assisted troubleshooting, incident investigation, and operational runbooks
• Preparing enterprise networks for AI infrastructure and high-performance workloads

Who Should Attend
• Enterprise network engineers and architects evaluating or deploying SONiC
• NOC and operations leaders exploring AI-assisted operations
• Infrastructure engineers responsible for automation and network reliability
• Platform and SRE teams operating large-scale distributed infrastructure
• IT leaders preparing networks to support AI and high-performance compute environments

 

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