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How AI Agents Are Transforming IT Ops
AI agents are moving from hype to the heart of enterprise IT. In this episode of the AI Proving Ground Podcast, Eric Jones and Ruben Ambrose — two leading AI experts — explore how intelligent, human-guided systems are transforming IT service management, incident response and operational scale to deliver faster resolutions, stronger security and smarter decisions across the enterprise.
AI Essentials
Welcome to the AI Essentials Learning Series. This series is designed to equip you with the knowledge and skills necessary to understand key aspects of Artificial Intelligence (AI). As AI continues to revolutionize industries and transform the way we live and work, it is crucial to understand the fundamental components that enable its remarkable performance. This series of learning paths will provide you with a comprehensive overview of AI, High Performance Storage, High Performance Networking, High Performance Compute, and Security, all tailored to the specific needs of AI applications.
WWT Agentic Network Assistant
Watch a multi-agent AI system run network operations on a live Cisco leaf-spine fabric. It detects configuration drift against a golden baseline, diagnoses root cause from live telemetry, and proposes Ansible remediation — but executes nothing without your approval. Work five realistic incidents, from a fat-finger mistake to a deliberate attack, and judge where agentic automation belongs.
Hermes Agent - The Agent That Remembers You
Learn to deploy the Nous Research Hermes Agent, backed by NVIDIA Nemotron-3-Super-120B-A12B, as a persistent infrastructure co-pilot. This hands-on lab guides you through configuring persistent memory across multiple surfaces (the terminal, Mattermost, and a desktop app), enforcing custom engineering standards, and compiling repeatable infrastructure workflows into reusable skills.
ATC+
NVIDIA Run:ai Researcher Sandbox
This hands-on lab provides a comprehensive introduction to NVIDIA Run:ai, a powerful platform for managing AI workloads on Kubernetes. Designed for AI practitioners, data scientists, and researchers, this lab will guide you through the core concepts and practical applications of Run:ai's workload management system.
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High Performance Storage for AI
Explore the critical role of high-performance storage in AI infrastructure. Gain insights into storage requirements for AI/ML workloads, architectures like distributed file systems and all-flash arrays, and strategies to optimize storage for model training and inference. Stay ahead with emerging trends shaping the future of AI storage solutions.
Hermes Agent - Operation Storm Watch Advanced Lab
Run Hermes Agent as an on-call operations team for a storefront outage. Fan out parallel investigators, adjudicate a risky change with a Mixture-of-Agents board, execute the fix under a goal contract with machine-verified proof, and review the agent run afterward.
Intro to Agentic Design Patterns
In this lab you build two agent architectures from scratch in Python, with no framework hiding the mechanics: an orchestrator that routes work to specialized workers, and a ReAct loop that reasons, acts, and observes until it has an answer. You'll expose the strengths and weaknesses of these architectures first hand. You'll leave understanding how each pattern works under the hood, and gain a feel for how and where these patterns are best applied.
Ansible Development Tools MCP Server
The learner is guided through the installation, configuration, and operations of the Ansible Dev Tools MCP Server. The lab guide walks through using the Ansible Dev Tools MCP server to build an ansible role, validate it against ansible-lint rules and best practices, and ultimately publish it as trusted content.
ATC+
NVIDIA Run:ai Researcher Sandbox
This hands-on lab provides a comprehensive introduction to NVIDIA Run:ai, a powerful platform for managing AI workloads on Kubernetes. Designed for AI practitioners, data scientists, and researchers, this lab will guide you through the core concepts and practical applications of Run:ai's workload management system.
NVIDIA Blueprint: Enterprise RAG
NVIDIA's AI Blueprint for RAG is a foundational guide for developers to build powerful data extraction and retrieval pipelines. It leverages NVIDIA NeMo Retriever models to create scalable and customizable RAG (Retrieval-Augmented Generation) applications.
This blueprint allows you to connect large language models (LLMs) to a wide range of enterprise data, including text, tables, charts, and infographics within millions of PDFs. The result is context-aware responses that can unlock valuable insights.
By using this blueprint, you can achieve 15x faster multimodal PDF data extraction and reduce incorrect answers by 50%. This boost in performance and accuracy helps enterprises drive productivity and get actionable insights from their data.
Introduction to LLM Routing
Introduces LLM routing as a pattern for avoiding one-size-fits-all model usage, covering cost-based, complexity-based, and latency-based routing strategies and when adopting a router is (and isn't) worth it.
NVIDIA Switchyard
Routing LLM Requests with NVIDIA Switchyard teaches config-driven LLM routing through a hands-on Docker Compose lab. Students deploy an OpenAI-compatible proxy plus dashboard, use passthrough routes, binary and category-based LLM classifiers, then measure real cost, latency, and quality tradeoffs between routed and frontier-baseline models across six progressive modules.
WWT Agentic Network Assistant
Watch a multi-agent AI system run network operations on a live Cisco leaf-spine fabric. It detects configuration drift against a golden baseline, diagnoses root cause from live telemetry, and proposes Ansible remediation — but executes nothing without your approval. Work five realistic incidents, from a fat-finger mistake to a deliberate attack, and judge where agentic automation belongs.
What is Physical AI?
Physical AI transcends generative AI by interacting with the real world, requiring perception, prediction and action in three-dimensional space. This embodiment leads to irreversible consequences, posing unique challenges like the sim-to-real gap. Already in use across robotics and autonomous vehicles, it demands continuous adaptation and innovative infrastructure solutions.
LLM Routing Lab
A hands-on lab where learners configure LiteLLM as a local reverse proxy that intelligently routes requests between multiple LLM backends — a general model for simple prompts, a coding model and a reasoning model for complex ones — instead of hard-coding a single model into an application.
The Tokenomics of AI-Native Engineering: The Cost You Didn't Budget For
The productivity case for AI-Native Engineering is real. So is the API bill. How you architect your model usage, including which model handles what, when you escalate and what you cache, is becoming as important as the code itself.
InfiniBand for AI Fabrics
Understand InfiniBand AI fabric through its lossless architecture, SHARP in-network computing, and real-world economics. Then experience a full operational lifecycle from day-zero design through UFM deployment and predictive maintenance, reinforced with hands-on lab practice. Learn how self-driving operations and InfiniBand technologies are shaping the next generation of AI factories.
Hermes Agent - The Agent That Remembers You
Learn to deploy the Nous Research Hermes Agent, backed by NVIDIA Nemotron-3-Super-120B-A12B, as a persistent infrastructure co-pilot. This hands-on lab guides you through configuring persistent memory across multiple surfaces (the terminal, Mattermost, and a desktop app), enforcing custom engineering standards, and compiling repeatable infrastructure workflows into reusable skills.
How to Pick the Right LLM
Welcome to the How to Pick the Right LLM Learning Path! Designed for engineers and technical practitioners, this course moves beyond benchmark-driven defaults to build a repeatable approach to model selection, rooted in a core truth: there is no single best model, only the right model for a specific task. You will first master a framework built around six key selection factors: use case, performance, latency, cost at scale, deployment, and security. From there, the path dives into the distinct LLM call types that power agentic systems—including classification, planning, and tool dispatch—and explores how architectural properties like reasoning mode and structured-output compliance dictate fit. Finally, you will jump into a hands-on JupyterLab environment to benchmark models across four capability tiers on canonical agent tasks. By measuring real-world latency and token consumption, you will build a data-driven scorecard to confidently design optimized, multi-tier model architectures.
World Wide Technology Ai Roadshow Dublin
Join us in Dublin for an Ai Roadshow, part of World Wide Technology's global series of events focused on moving AI from ambition to execution.
The Ai Roadshow brings together business and technology leaders for a focused afternoon of insight, dialogue and real-world perspective on enterprise AI adoption. Drawing on expert viewpoints, customer and industry examples, and structured discussion, the programme explores what it takes to accelerate AI outcomes, build AI solutions aligned to business priorities, and scale AI responsibly.
Building on two years of global delivery, Ai Roadshow reflects the lessons, patterns and realities emerging from enterprise AI adoption, offering a practical view of what is working, what is changing, and what comes next. During the afternoon you will have the opportunity to talk to our experts about your AI journey.
World Wide Technology Ai Roadshow Edinburgh
Join us in Edinburgh for Ai Roadshow, part of World Wide Technology's global series of events focused on moving AI from ambition to execution.
The Ai Roadshow brings together business and technology leaders for a focused afternoon of insight, dialogue and real-world perspective on enterprise AI adoption. Drawing on expert viewpoints, customer and industry examples, and structured discussion, the programme explores what it takes to accelerate AI outcomes, build AI solutions aligned to business priorities, and scale AI responsibly.
Building on two years of global delivery, Ai Roadshow reflects the lessons, patterns and realities emerging from enterprise AI adoption, offering a practical view of what is working, what is changing, and what comes next. During the session you will have the opportunity to talk to our experts about your AI journey.
LLMaaS ChatBot
Compare large language models side by side using WWT's AI Proving Ground GPU cluster. Send the same prompt to multiple models at once and evaluate their responses in real time — measuring response speed, reasoning quality, and output style across leading open-source and proprietary models.
No setup required. The lab environment connects automatically to the LLMaaS AI Gateway, giving you direct access to production-grade inference endpoints used by WWT's AI engineering teams.
Kubeflow
Kubeflow is an open-source machine learning platform dedicated to making deployments of machine learning workflows on Kubernetes simple, portable, and scalable. This learning path is structured to provide both theoretical knowledge and practical, hands-on experience with the core components of the Kubeflow ecosystem.
How to Choose the Right LLM
This hands-on lab teaches enterprise engineers how to systematically evaluate, benchmark, and select optimal large language models for distinct steps inside an agentic control plane.