Agentic Technical Reference Vol by Patel Rashmi (9 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. Neuware.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Agentic AI Technical Reference - Volume 2 is the practical implementation guide for engineers who want to move beyond concepts and build production-ready AI agents using today's leading frameworks.While Volume 1 introduced the foundations of agentic AI, this volume focuses on deep framework mastery. Through progressively structured chapters, production patterns, architectural guidance, real-world code examples, troubleshooting advice, and complete capstone projects, you'll learn how to design, deploy, optimize, and scale enterprise-grade AI agents.Inside this volume you'll gain hands-on expertise with: - LangChain architecture, LCEL, tools, memory, and production RAG- LangGraph for stateful workflows, routing, reflection loops, streaming, and multi-agent orchestration- CrewAI for role-based autonomous teams, YAML configuration, Flows, and multi-LLM systems- AutoGen conversational agents, GroupChat, code execution, and autonomous engineering teams- OpenAI Agents SDK including handoffs, tracing, runners, and guardrails- Agentic RAG including Corrective RAG, Self-RAG, Adaptive RAG, RAPTOR, and multi-hop reasoning- Function calling, structured outputs, and advanced agent memory architectures- Agent swarms, debate patterns, Constitutional AI, and alignment strategies- Cost optimization through caching, model routing, token budgeting, and reliability engineering- Low-code automation using n8n, LangFlow, and Flowise- Security, prompt injection defense, guardrails, evaluation, benchmarking, testing, and observability- Docker, Kubernetes, asynchronous execution, production monitoring, and enterprise deployment- Industry applications across finance, healthcare, coding assistants, customer support, research, and engineering automation- Multiple end-to-end production projects with complete implementations and deployment guidanceUnlike introductory AI books, this reference emphasizes production engineering. Every chapter combines conceptual understanding with practical implementation, common failure scenarios, debugging strategies, and best practices drawn from real-world AI systems.Whether you're building enterprise copilots, autonomous workflows, multi-agent platforms, AI research assistants, or production RAG systems, this volume provides the knowledge needed to build reliable, scalable, and maintainable agentic applications.Ideal for: AI EngineersGenAI EngineersMachine Learning EngineersSoftware EngineersBackend DevelopersAI ArchitectsTechnical LeadsData ScientistsStudents transitioning into Agentic AIProfessionals building production LLM applicationsContinue your journey from understanding AI agents to engineering production-ready autonomous systems with Agentic AI Technical Reference - Volume 2. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Build production-ready AI agents with confidence.Agentic AI is transforming how intelligent software is designed. Modern AI systems no longer generate text alone-they reason, plan, use tools, maintain memory, collaborate with other agents, recover from failures, and execute complex workflows with minimal human intervention.This technical reference explains how these systems are designed, implemented, tested, secured, and deployed in real-world production environments.Rather than focusing on isolated concepts, this book connects the complete Agentic AI ecosystem-from autonomous reasoning and multi-agent orchestration to deployment infrastructure, evaluation frameworks, security controls, and domain-specific applications.Inside this reference you will learn how to design intelligent systems using modern frameworks including LangGraph, CrewAI, Model Context Protocol (MCP), Claude Code, AWS Bedrock, Docker, Kubernetes, and other production technologies. Every major topic is supported with architecture diagrams, implementation guidance, engineering practices, and practical Python examples.Topics CoveredAI agents and autonomous reasoningAgent architectures and design patternsMulti-agent collaboration and orchestrationAgent memory and state managementTool calling and Model Context Protocol (MCP)LangGraph, CrewAI, Claude Code, and AWS BedrockProduction deployment with Docker and KubernetesAgent monitoring, observability, and scalabilitySecurity, guardrails, prompt injection defense, and privacyEvaluation, benchmarking, testing, and red teamingHuman-in-the-loop systems and advanced engineering patternsProduction architectures for finance, healthcare, and software engineeringInterview questions and complete implementation projectsWho This Book Is ForAI EngineersMachine Learning EngineersSoftware EngineersGenAI DevelopersSolution ArchitectsMLOps EngineersTechnical LeadsResearchers and graduate studentsWhether you are building autonomous AI applications, designing enterprise agent platforms, preparing for technical interviews, or expanding your expertise in modern AI engineering, this book provides a structured reference that bridges foundational concepts with production-ready implementation.Designed as a long-term technical companion, Agentic AI combines engineering principles, practical examples, architectural guidance, and real-world deployment practices into a single reference for professionals building the next generation of intelligent systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …