Building AI Agents for Network Operations

Practical AI Agents for NetOps

Building AI Agents for Network Operations is a hands-on technical guide for network engineers, NetOps teams, and automation practitioners who want to use LLMs in a controlled, production-minded way. Rather than treating AI as a magic answer, the book shows where agents can genuinely help with troubleshooting, parsing, summarizing, and tool-assisted investigation—and where a traditional script or workflow is still the better choice.

The through-line is refreshingly grounded: build useful agentic workflows, keep validation in place, and make sure humans remain part of the loop when operational risk is on the table.

From Local LLMs to Agentic Workflows

The book begins with the essentials: local setup with Ollama, Python-based experimentation, prompt engineering, and structured output. From there, it moves into memory-enabled chatbots, tool calling, and MCP-backed workflows, showing how each piece fits into a larger network-operations use case.

What the Book Covers

• LLM fundamentals for network operations
• Prompt design for predictable responses
• Parsing network output into structured data
• Chatbots with memory for operational conversations
• Tool use and agentic troubleshooting workflows
• MCP as a way to package reusable network tools
• Production-readiness concerns such as validation, approval, and control

A Grounded Approach to AI in Networking

One of the book’s strengths is its insistence on evidence. The examples are framed around practical investigation and safe boundaries, not hype-driven autonomy. That makes it a strong fit for readers who want to understand how AI agents can support network operations without turning troubleshooting into guesswork.

Who It’s Best For

This title is especially relevant for network engineers, NetOps engineers, NOC teams, SREs, DevOps professionals, and automation-minded infrastructure practitioners who are exploring agentic AI in a real operational context.

If you are interested in building LLM-powered network workflows that remain testable, observable, and reviewable, this book offers a clear place to start.