Building AI Agents for Network Operations Design LLM - powered NetOps workflows with Python
Category: eBooks
- views: 184
- date: 31 July 2026
- posted by: BeMyLove
Building AI Agents for Network Operations Design LLM - powered NetOps workflows with Python | 20.99 MB
Title: Building AI Agents for Network Operations Design LLM - powered NetOps workflows with Python
Author: Sif Baksh
Category: Nonfiction, Computers, Networking & Communications, Advanced Computing, Artificial Intelligence
Language: English | 226 Pages | ISBN: 9781808346828
Description:
Build AI-assisted network troubleshooting workflows that parse CLI output, call approved tools, use MCP, and keep evidence visible for review
Key Features
Book Description
Network troubleshooting is full of clues, but they are often buried in noisy alerts, long CLI output, missing topology context, and incomplete handoffs. Building AI Agents for Network Operations shows how to use AI agents, LLMs, and network automation in a controlled way, so engineers can get clearer evidence without giving up validation or operational control. You will start with local LLM workflows using Ollama and Python, then use a simple RACE prompt structure to make repeatable NetOps tasks clearer, safer, and easier to review. You will parse interface and BGP output into structured data, build a chatbot that keeps troubleshooting context, and connect the model to approved tools for device status, interfaces, reachability, topology, and BGP health. You will then build an agentic troubleshooting loop, package reusable network tools with MCP, and learn how to evaluate these workflows against logging, approvals, observability, runbooks, feature flags, and read-only pilot readiness. By the end of this book, you will have a practical path for turning AI ideas into NetOps workflows that can be tested in a lab, reviewed by your team, and adapted toward real-world network operations with the right controls.
What you will learn
Who this book is for
This book is for network engineers, NetOps engineers, NOC engineers, SREs, DevOps engineers, and network automation professionals who want to apply AI to troubleshooting without losing control. Basic networking and CLI familiarity will help, beginner Python knowledge is useful for following the labs.
DOWNLOAD:
https://rapidgator.net/file/a8c8f80292b775cdb489144a3487d3b8/Building_AI_Agents_for_Network_Operations_Design_LLM-powered_NetOps_workflows_with_Python.rar
https://nitroflare.com/view/714DA41FA6ADBA7/Building_AI_Agents_for_Network_Operations_Design_LLM-powered_NetOps_workflows_with_Python.rar
Build AI-assisted network troubleshooting workflows that parse CLI output, call approved tools, use MCP, and keep evidence visible for review
Key Features
- Build local LLM workflows for NetOps using Python, Ollama, and validated CLI data
- Create troubleshooting agents that use memory, approved tools, and clear evidence
- Package reusable network tools with MCP and plan controlled read-only pilots
Book Description
Network troubleshooting is full of clues, but they are often buried in noisy alerts, long CLI output, missing topology context, and incomplete handoffs. Building AI Agents for Network Operations shows how to use AI agents, LLMs, and network automation in a controlled way, so engineers can get clearer evidence without giving up validation or operational control. You will start with local LLM workflows using Ollama and Python, then use a simple RACE prompt structure to make repeatable NetOps tasks clearer, safer, and easier to review. You will parse interface and BGP output into structured data, build a chatbot that keeps troubleshooting context, and connect the model to approved tools for device status, interfaces, reachability, topology, and BGP health. You will then build an agentic troubleshooting loop, package reusable network tools with MCP, and learn how to evaluate these workflows against logging, approvals, observability, runbooks, feature flags, and read-only pilot readiness. By the end of this book, you will have a practical path for turning AI ideas into NetOps workflows that can be tested in a lab, reviewed by your team, and adapted toward real-world network operations with the right controls.
What you will learn
- Run local LLM workflows with Ollama and Python
- Shape reliable NetOps prompts using RACE
- Parse CLI and BGP output into structured JSON
- Build chatbots that remember troubleshooting context
- Connect AI agents to approved network tools
- Create evidence-based troubleshooting loops
- Expose reusable network tools with MCP
- Plan read-only pilots with safety controls
Who this book is for
This book is for network engineers, NetOps engineers, NOC engineers, SREs, DevOps engineers, and network automation professionals who want to apply AI to troubleshooting without losing control. Basic networking and CLI familiarity will help, beginner Python knowledge is useful for following the labs.
DOWNLOAD:
https://rapidgator.net/file/a8c8f80292b775cdb489144a3487d3b8/Building_AI_Agents_for_Network_Operations_Design_LLM-powered_NetOps_workflows_with_Python.rar
https://nitroflare.com/view/714DA41FA6ADBA7/Building_AI_Agents_for_Network_Operations_Design_LLM-powered_NetOps_workflows_with_Python.rar
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