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Create AI Agents for IT Infrastructure
Rating: 5.0 out of 5(2 ratings)
17 students

Create AI Agents for IT Infrastructure

Build LLM-powered AI assistants with LangChain, RAG, and AI agents to automate infrastructure and log analysis
Created byArtem Utkin
Last updated 8/2025
English

What you'll learn

  • Integrate Large Language Models (LLMs) into IT automation workflows using Python and the LangChain framework.
  • Analyze network logs and retrieve knowledge from documentation and corporate databases with Retrieval-Augmented Generation (RAG)
  • Build and deploy AI agents capable of managing network equipment and assisting with incident diagnostics.
  • Work with both cloud-based and local LLMs (e.g., OpenAI, Ollama) to create AI assistants for network administration tasks

Course content

3 sections • 12 lectures • 1h 33m total length

  • Introduction4:37
  • Introduction
  • Setting up the working environment5:30
  • Setting up the working environment
  • Getting started with OpenAI13:01
  • Getting started with OpenAI
  • LangChain and Streamlit7:36
  • LangChain and Streamlit
  • Local LLM9:02
  • Local LLM

Requirements

  • Basic understanding of Python (writing and running simple scripts)
  • Familiarity with networking concepts (IP address, VLAN, switches)
  • Basic knowledge of IT administration (API usage, logs, CMDB role)
  • Experience using Linux (SSH connection, Bash commands, running scripts)
  • Basic Docker Compose commands (e.g., docker compose up -d, docker ps)

Description

In this course, you will learn how to apply modern Large Language Models (LLM) for automation through practical, hands-on cases from network infrastructure administration.

Step by step, together we will integrate LLMs into traditional automation workflows using the LangChain framework, combining AI capabilities with proven automation practices. Along the way, you will gain skills in connecting LLMs to logging systems, retrieving data from knowledge bases, and orchestrating multiple automation tools through AI agents.

By the end of the course, you will have created a ready-to-use AI assistant that:

  • Communicates like ChatGPT, but with access to your internal documentation

  • Assists in configuring network equipment for routine tasks

  • Analyzes logs and accelerates incident diagnostics

  • Integrates with CMDB and other infrastructure tools

This approach transforms the traditional human–machine interaction into a smooth human–human chat, where your infrastructure responds like a live assistant.

The course is designed for network engineers, DevNetOps specialists, and IT administrators who want to bring AI into their workflows. With ~80% practice and ~20% theory, you will leave with working code, ready to adapt to your own environment, and a deep understanding of how to apply LLMs in real-world IT automation.

As a result, you will be ready to implement AI automation in production!

Who this course is for:

  • IT professionals seeking practical ways to apply LLMs in network administration
  • DevNetOps specialists looking to integrate LLMs into infrastructure workflows
  • Network engineers who want to automate routine tasks with AI tools
  • Automation engineers interested in building AI-powered assistants and agents
  • Anyone curious about combining AI + DevOps + Networking for next-generation IT automation

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