Building AI Agents for Network Operations
Design LLM-powered NetOps workflows with Python, Ollama, MCP, and tool calling
Created by Sif Baksh
Discover how to bring AI into network operations by building practical agents that help with troubleshooting and automation. You'll learn to use Python, Ollama, and MCP to create workflows that keep you in control while making your job easier. By the end, you'll know how to design, test, and review AI-powered NetOps solutions.
Packt | Jul 2026 | 226 min
What You Will Learn
You'll start with local LLM workflows and gradually add new capabilities in each section, such as prompt design, parsing CLI output, adding memory, and connecting to approved tools. Step by step, you'll build up to agentic troubleshooting loops, package solutions with MCP, and learn how to review and test these workflows for real-world readiness.
Key Features
- Build AI-powered NetOps workflows using Python, Ollama, and structured CLI data
- Create troubleshooting agents that use memory, approved tools, and clear evidence
- Package and test reusable network tools with MCP for safe, controlled pilots
Target Audience
Ideal for network engineers, NetOps professionals, NOC engineers, SREs, and DevOps engineers who want to apply AI to network troubleshooting without losing operational control. If you know basic networking, are comfortable with the CLI, and have some beginner Python skills, you'll be able to follow along and put these ideas into practice.
