In multitenant AI clusters, bandwidth is not the SLA—Job Completion Time (JCT) is. In this joint session, NextHop.ai and Keysight present a real-world study of how load balancing modes, ECN thresholds, and congestion control tuning directly impact JCT in shared AI Ethernet fabrics. Using production-class switching and traffic emulation, we demonstrate how overly conservative settings can double completion times and waste GPU cycles—and how systematic tuning makes JCT predictable. Attendees will gain a practical framework for designing and validating AI networks around measurable performance outcomes,…
AI has evolved from passive insights to active decision-making—drafting emails, generating code, and executing tasks across enterprise systems. As AI agents grow more powerful, they also introduce unpredictability, expanding attack surfaces and compliance risks. In this session, Netskope shows how to move beyond simply enabling AI to securing it at every touchpoint. We’ll outline a practical blueprint to protect AI agents end-to-end across users, applications, and data. Learn how to maintain visibility into agent behavior, apply advanced DLP and threat protection to reduce misuse, and…
GenAI is powerful in network operations—but it’s probabilistic by nature, meaning it produces “most likely” answers that can vary with small prompt changes, making it risky as a direct control mechanism for repeatable, auditable change workflows. In contrast, deterministic automation (RPA in ops contexts) is built for consistent execution: enforcing config standards, running verified OS upgrade workflows, and remediating drift with predictable outcomes. In this session, you’ll get a simple decision framework for when to use GenAI vs. deterministic automation, why trusted data (a network…
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…
Modern IT environments generate petabytes of data from applications, endpoints, and cloud services. AI-powered enterprises need to enable movement of this data for training of AI models and reasoning via thousands instances of agentic AI across enterprise data center and cloud locations. This keynote dives into the Riverbed Data Fabric as a purpose-built enterprise data plane for large-scale data movement to enable the scaled deployment of enterprise AI. We’ll explore our vision and plans for a data fabric that can move massive data sets from…
IT teams struggle with complex, siloed troubleshooting. See how Zscaler Digital Experience (ZDX) ends the blame game with a unique advantage: a single, lightweight agent providing end-to-end Zero Trust for both security and comprehensive monitoring. This unified data powers our AI Agents, delivering Tier-3 expertise directly to your Service Desk. Join us to see live examples of how to instantly isolate root causes and accelerate resolution from hours to minutes.
This presentation explores the collapse of the legacy, reactive NOC and the rise of the AI-driven Agentic Operations Center (AOC). It outlines how enterprises must strategically delegate operational toil to AI—automating telemetry correlation, triage, and analysis—while preserving human oversight for high-stakes decisions. The session highlights Human-in-the-Loop design, agent-to-agent collaboration, and Zero-Trust guardrails as foundations for safe closed-loop automation. Ultimately, it frames the AOC as both a technological and organizational transformation, elevating engineers from reactive troubleshooting to strategic architecture and autonomous network design.
Enterprises are deploying agentic AI, but risk controls remain incomplete. Observability tools map which agents exist and how they interact, while gateways filter prompts and responses. Yet neither addresses the core problem: what the agent actually executes inside enterprise systems. The real challenge is controlling and verifying actions in production. In this session, we’ll share real-world examples of monitoring the three W’s of enterprise agent behavior—who accessed a system, what they accessed, and why the action was taken. We’ll demonstrate new technologies that enable precise,…
Sound AIOps for network and data center infrastructure must have reliable, accurate, and well-curated data. Yet, in too many infrastructure and network teams, so much focus is placed on cool automation and AI tools that teams lose track of an essential element: the quality and readiness of infrastructure data. Always-accurate Infrastructure and network intent data that makes it easy for humans, machines, and LLMs to understand how the network should behave, is key to success. This talk explores why a knowledge graph approach that integrates…
Let’s start with an amazing fact : despite massive investment in AI infrastructure, most AI clusters operate at just 50–70% average GPU utilization, even during peak periods, highlighting an efficiency gap between theoretical performance and real-world AI infrastructure – we will break down the building blocks of AI clusters and how the most critical parts can be optimized for peak performance.
