9781808346835 - building ai agents for network operations: design llm-powered netops workflows with python, ollama, mcp, and tool calling de sif baksh (11 resultados)
- Tapa blanda
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de AmericaBargainBookStores
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 43,19
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 5 disponibles
Paperback or Softback. Condición: New. Building AI Agents for Network Operations: Design LLM-powered NetOps workflows with Python, Ollama, MCP, and tool calling. Book.
- Tapa blanda
Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 47,87
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa blanda
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 47,92
Envío por EUR 4,85Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
- Tapa blanda
Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 78,84
Envío por EUR 3,43Se envía dentro de Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New.
- Tapa blanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 60,00
Envío por EUR 30,50Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. Neuware - Build AI-assisted network troubleshooting workflows that parse CLI output, call approved tools, use MCP, and keep evidence visible for reviewKey Features: - Build local LLM workflows for NetOps using Python, Ollama, and validated CLI data- Create troubleshooting agents that use memory, appr…oved tools, and clear evidence- Package reusable network tools with MCP and plan controlled read-only pilotsBook 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 controlsWho 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.Table of Contents- Understanding AI Agents for Network Operations- LLM Fundamentals and Local Setup- Prompt Engineering for Network Automation- Parsing Network Outputs into Structured Data- Building a Network Chatbot with Memory- Designing Tools and Agentic Workflows- Building the Main Network Troubleshooting Agent- From Lab Agents to Reusable Tools with MCP- Moving Toward Production-Ready Network Agents- Appendix: AI Network Agent Design Toolkit.
- Tapa blanda
- Impresión bajo demanda
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 49,76
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Build AI-assisted network troubleshooting workflows that parse CLI output, call approved tools, use MCP, and keep evidence visible for reviewKey FeaturesBuild local LLM workflows for NetOps using Python, Ollama, and validated CLI dataCreate troubleshooting agents that use memory, approved to…ols, and clear evidencePackage reusable network tools with MCP and plan controlled read-only pilotsBook DescriptionNetwork 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 learnRun local LLM workflows with Ollama and PythonShape reliable NetOps prompts using RACEParse CLI and BGP output into structured JSONBuild chatbots that remember troubleshooting contextConnect AI agents to approved network toolsCreate evidence-based troubleshooting loopsExpose reusable network tools with MCPPlan read-only pilots with safety controlsWho this book is forThis 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Tapa blanda
- Impresión bajo demanda
Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 78,28
Envío por EUR 7,58Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. Print on Demand.
- Tapa blanda
- Impresión bajo demanda
Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 80,65
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. PRINT ON DEMAND.
- Tapa blanda
- Impresión bajo demanda
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 52,84
Envío por EUR 43,15Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Build AI-assisted network troubleshooting workflows that parse CLI output, call approved tools, use MCP, and keep evidence visible for reviewKey FeaturesBuild local LLM workflows for NetOps using Python, Ollama, and validated CLI dataCreate troubleshooting agents that use memory, approved to…ols, and clear evidencePackage reusable network tools with MCP and plan controlled read-only pilotsBook DescriptionNetwork 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 learnRun local LLM workflows with Ollama and PythonShape reliable NetOps prompts using RACEParse CLI and BGP output into structured JSONBuild chatbots that remember troubleshooting contextConnect AI agents to approved network toolsCreate evidence-based troubleshooting loopsExpose reusable network tools with MCPPlan read-only pilots with safety controlsWho this book is forThis 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Tapa blanda
- Impresión bajo demanda
Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 75,29
Envío por EUR 31,85Se envía de Australia a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Build AI-assisted network troubleshooting workflows that parse CLI output, call approved tools, use MCP, and keep evidence visible for reviewKey FeaturesBuild local LLM workflows for NetOps using Python, Ollama, and validated CLI dataCreate troubleshooting agents that use memory, approved to…ols, and clear evidencePackage reusable network tools with MCP and plan controlled read-only pilotsBook DescriptionNetwork 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 learnRun local LLM workflows with Ollama and PythonShape reliable NetOps prompts using RACEParse CLI and BGP output into structured JSONBuild chatbots that remember troubleshooting contextConnect AI agents to approved network toolsCreate evidence-based troubleshooting loopsExpose reusable network tools with MCPPlan read-only pilots with safety controlsWho this book is forThis 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Tapa blanda
- Impresión bajo demanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 57,95
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Building AI Agents for Network Operations | Design LLM-powered NetOps workflows with Python, Ollama, MCP, and tool calling | Sif Baksh | Taschenbuch | Englisch | 2026 | Packt Publishing | EAN 9781808346835 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]lib…ri[dot]de | Anbieter: preigu Print on Demand.
