AI Operations (AIOps): Using Agentic AI to Operate Your Network (Video Course)

Transition from traditional IT operations to AI-driven automation using agentic AI for scalable network management.

Overview

AI Operations (AIOps): Using Agentic AI to Operate Your Network is a technically focused video course designed for IT professionals who want to modernize how networks and infrastructure are operated. Youll learn how agentic AIintelligent systems that can act autonomouslycan take on Day 1 (setup and configuration) and Day 2 (monitoring and maintenance) operational tasks across both on-premises and cloud environments.

The course explains how AI systems evolve from supervised tools into semi-supervised and fully autonomous agents, and what that evolution means for real-world operations in NOC and SOC environments. Rather than treating AI as a black box, this course emphasizes architectures, frameworks, and best practices that help teams evaluate where automation makes sense, where human oversight remains critical, and how to transition responsibly.

With a strong grounding in current operational realities, the course serves both as a reference for evaluating AI operations and a practical guide for getting started with agent-based systems. Youll gain insight into how AI agents can reduce manual troubleshooting, improve reliability, and help control operational costs that often consume a significant portion of IT budgets.

Learn How To

• Explain what agentic AI is and how AI agents differ from traditional automation tools.
• Analyze Day 1 and Day 2 operations and identify tasks suitable for agent-based automation.
• Evaluate common agent architectures and frameworks used in modern AI operations.
• Apply visual language models and natural language models, and graph databases within agent systems.
• Compare centralized versus distributed and federated agent deployments.
• Design environments that support secure agent-to-agent communication and hosting.
• Incorporate explainability and observability into AI-driven operational workflows.
• Plan a transition from supervised to semi-supervised and autonomous execution safely.

Who Should Take This Course

• Network operations professionals working in NOC or SOC environments.
• System and cloud operations engineers responsible for Day 1 and Day 2 tasks
• DevOps engineers exploring AI-driven automation strategies
• Cloud architects designing scalable, AI-ready infrastructure
• IT leaders and technical decision-makers evaluating AIOps and agent-based systems
• Technically inclined professionals interested in applying agentic AI to real operations

Course Requirements

• Working knowledge of networking, systems, or cloud infrastructure concepts
• Familiarity with IT operations workflows (NOC, SOC, or DevOps environments)
• Comfort with technical architecture discussions and conceptual frameworks
• Interest in evaluating and adopting AI-driven automation responsibly

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Table of Contents

Lesson 1: Network and Security Operations Today

1.1 Network operations primer

1.2 Division of tasks

1.3 Popular ITSM and collaboration tooling

1.4 Sources of data

1.5 Management of data

1.6 Dealing with PPI

Lesson 2: Agent Architectures and Frameworks

2.1 Foundation concepts of artificial intelligence

2.2 Usage of agents with Generative AI

2.3 Inference in depth

2.4 Using agent frameworks

2.5 Model Context Protocol (MCP)

2.6 Agent2Agent protocol

2.7 Identity (AGNTCY)

Lesson 3: Visual Language Models and Natural Language Models

3.1 Multi-modality

3.2 Usage of vision models

3.3 SLMs vs. LLMs

3.4 Finetuning LLMs

3.5 Distillation

Lesson 4: Graph and Vector Databases

4.1 Retrieval Augmented Generation (RAG)

4.2 Neuro-symbolic AI

4.3 Graph databases

4.4 Vector databases

Lesson 5: Agent-hosting Environments

5.1 Daemons

5.2 Docker

5.3 Web assembly

5.4 Kubernetes

5.5 Kubernetes form factors

5.6 App hosting on Cisco architectures

Lesson 6: Distributed Usage and Federation of Agents vs. Centralized

6.1 Network-based distribution of function

6.2 Cilium container network interface

6.3 Service mesh

6.4 SPIFFE / SPIRE

6.5 SLIM (Core)

6.6 Web3 -  smart contracts

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