Max Reedwell (28 results)
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Paperback. Condition: new. Paperback. Frustrated by the high costs, slow latency, and data privacy risks of proprietary cloud LLM APIs? This book is the definitive, hands-on guide for AI developers, DevOps engineers, and technical leaders who are ready to take full control of their AI stack. Ollama & Local AI provides a practica…l, code-driven roadmap to self-hosting, fine-tuning, and deploying powerful open-source models like Llama and Mistral directly on your own hardware. Move beyond simple API consumption, gain absolute data sovereignty, and dramatically reduce your inference costs.This is not a high-level overview; it's a complete production playbook. Inside, you will find the precise, step-by-step instructions to: Master Installation: Set up and manage complete Ollama and LocalAI ecosystems, from simple scripts to production-ready Docker and Kubernetes deployments.Fine-Tune Custom Models: Learn to perform efficient LoRA and QLoRA fine-tuning using modern tools like Unsloth and Axolotl to create models with specialized skills.Optimize and Deploy: Convert, quantize, and merge models into the high-performance GGUF format using llama.cpp workflows for deployment in both Ollama and LocalAI.Build Secure APIs: Architect secure, high-throughput REST APIs for your models using an Nginx reverse proxy for enterprise-grade authentication.Orchestrate Workflows: Integrate your local models into complex LangChain pipelines to build powerful applications like Retrieval-Augmented Generation (RAG).Troubleshoot Like a Pro: Diagnose and solve common pitfalls in VRAM management, CUDA conflicts, and performance bottlenecks.Stop renting your AI. Build, deploy, and own your high-performance LLM infrastructure today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condition: new. Paperback. The first wave of AI-assisted coding is over. Tools like GitHub Copilot made us faster typists, but they've hit a ceiling. We are still "AI snippet managers," stuck with the full cognitive load of our project's architecture. The AI suggests a line, but it can't reason about the entire system….This book is about the second revolution: the shift from AI-assisted to agent-driven development.Cursor 2.0 is the first IDE built for this new era. It's not a plugin; it's an AI-first collaborator designed to understand your entire repository, deploy teams of AI agents, and help you build, test, and secure code at a project-wide scale.But using Cursor like a super-charged VS Code is like using a smartphone only to make calls-you're missing 99% of its power. To unlock its true potential, you must change your mindset. You must learn to direct AI, not just prompt it.Cursor 2.0 in Action is not a theoretical manual; it is a hands-on, practical playbook for the working developer. We don't just look at features; we implement solutions.Inside, you will master the playbooks to: Reason Across Your Entire Codebase: Use at to ask architectural questions, find dependencies, and instantly understand legacy projects.Execute Large-Scale Refactoring: Confidently migrate frameworks (e.g., Express to Fastify) or modernize legacy code (callbacks to async/await) in a fraction of the time.Implement Full-Stack Features from a Spec: Go from a one-page "spec sheet" to a fully scaffolded feature-database schema, API, and UI-in a single operation.Master Multi-Agent Orchestration: Deploy a "team" of AI agents to solve a complex problem in parallel, then review their competing, sandboxed solutions.Perform AI-Driven Security Audits: Use agents as "security reviewers" to find and fix critical vulnerabilities like SQL Injection, XSS, and Prompt Injection before they ever hit production.Engineer Advanced Prompts: Go beyond simple questions and master structured templates like "Role, Context, Task, Format" and "Tests-First" prompting to get predictable, high-quality results.This book is for the professional developer, solutions architect, or engineering manager who recognizes that the barrier to entry is no longer can it be built, but how fast and how securely it can be built.Stop being a coder. Start being an orchestrator. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
- Print on Demand
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: new. Paperback. The prototype phase is over. Your organization doesn't need another chatbot that writes poetry; it needs an intelligent agent that can query databases, analyze 500-page contracts, and execute complex workflows without hallucinating or leaking data.Anthropic Claude for the Enterprise is the f…ield manual for senior engineers and architects who are done with "vibes-based" AI. This isn't a collection of clever prompts; it is a blueprint for building the nervous system of a modern enterprise. You will learn to leverage the Model Context Protocol (MCP) and Claude 4.5 to construct secure, deterministic agentic systems that survive the rigors of production traffic.From managing the economics of the 200k+ token window to deploying containerized tools, this book strips away the hype to focus on the hard engineering required to make Large Language Models (LLMs) safe and useful.Inside, you will discover how to: Master the Model Context Protocol (MCP): Stop writing brittle glue code. Build standardized, secure servers that connect Claude to your internal databases, Slack workspaces, and GitHub repos without rewriting the wheel.Engineer "Extended Thinking" Programmatically control Claude 4.5's reasoning budget to force planning, self-correction, and citation enforcement before a single line of code is written.Scale Context Economically: Implement context caching strategies to process massive technical manuals and logs while slashing latency and token costs by 90%.Implement "Human-in-the-Loop" Brakes: Design middleware that intercepts high-risk tool calls-like database deletions-and automatically pauses execution for human approval.Deploy with Confidence: Move from localhost to production with battle-tested patterns for Dockerizing MCP servers, tracing agent "thoughts" with observability tools, and enforcing hard cost limits. Stop renting intelligence and start engineering it. Secure your copy today and build the agents your enterprise can actually trust. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
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Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: new. Paperback. The era of flat retrieval is ending. It is time to give your AI agents a brain, not just a search bar.For the past two years, the industry has relied on Vector RAG to ground Large Language Models. We chopped documents into chunks, embedded them, and hoped that semantic similarity would be en…ough to answer every user question. For simple queries, it worked. But as we push LLMs into complex domains like financial forensics, legal discovery, and scientific research, the cracks are showing. Vector databases are excellent at finding needles in haystacks, but they fail to understand how the hay is stacked. They struggle with multi-hop reasoning, often missing the critical "disconnected facts" that link a cause to an effect across different documents.GraphRAG in Production is the engineering manual for the next generation of intelligent systems.This book is not a theoretical academic text. It is a hands-on guide for software architects and data scientists who are ready to move beyond simple prototypes. It details the architectural shift from retrieving data based on what it looks like (vectors) to retrieving data based on what it is connected to (graphs). You will learn how to construct a dual-memory system that fuses the fuzzy flexibility of vectors with the rigid logic of knowledge graphs, enabling your agents to traverse complex relationships and answer questions that stump standard RAG systems.Inside, you will engineer: The Hybrid Search Engine: Implement Reciprocal Rank Fusion (RRF) to mathematically combine vector similarity scores with graph centrality metrics, ensuring your retrieval is both broad and precise.The Agentic Control Plane: Move beyond brittle linear chains. Build stateful, self-correcting agents using LangGraph that can plan research, write and fix their own Cypher queries, and retry failed searches automatically.The Indexing Pipeline: Stop paying premium prices for menial tasks. Architect a tiered ingestion system that uses small, fine-tuned models for entity extraction and large reasoning models for synthesis, reducing your token costs by up to 80%.Advanced Graph Algorithms: Implement the Leiden algorithm for community detection and "Map-Reduce" style global summarization, allowing your system to answer high-level thematic questions across your entire dataset.Production Infrastructure: Deploy a containerized stack using Docker, comparing the trade-offs between Neo4j, FalkorDB, and Memgraph for specific LLM workloads.Who This Book Is ForThis text is written for Python-proficient engineers, AI architects, and technical leads. If you have hit the ceiling of what standard RAG can do and are looking for a reliable, scalable way to implement structured reasoning, this is your blueprint.Stop building chatbots that guess. Start building agents that know. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
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Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: new. Paperback. Stop Copy-Pasting Code. Start Architecting Intelligence.For years, the workflow has been broken. You leave your terminal, open a browser, paste a stack trace into a chatbot, wait, copy the code, paste it back, and pray it works. The power of Generative AI was trapped behind a chat window-unt…il now.The Gemini CLI (gcli) changes the game. It is not just a chatbot in your shell; it is a fully autonomous agent with "hands" to edit files, "eyes" to search the web, and the reasoning power to execute complex, multi-step workflows on your behalf.The Gemini CLI: A Practitioner's Guide is the definitive handbook for developers, DevOps engineers, and data scientists who want to move beyond simple prompts and build agentic networks that understand their code, automate their drudgery, and secure their workflows.In this practical guide, you will learn how to: Master the "Reason and Act" (ReAct) Loop: Understand how agents break down complex goals into actionable plans, executing shell commands and file operations autonomously."Ask Your Codebase" (1 Million+ Tokens): Learn strategies to load entire repositories into context, enabling you to trace features across files, generate architectural diagrams, and perform semantic refactoring in minutes.Automate the Boring Stuff: Build intelligent Git Pre-Commit Hooks that review your code for security flaws before you push, and create Daily Research Agents that scour the web and write briefings while you sleep.Customize Your Agent: Use the GEMINI.md context file to enforce project-specific personas, style guides, and "Do Not" rules that ensure every line of code matches your team's standards.Extend Capabilities with MCP: Dive deep into the Model Context Protocol to build secure, custom tools that let your AI interact with private GitHub repos, Jira tickets, and production databases.Secure Your Workflow: Master the "Human-in-the-Loop" security model to prevent Prompt Injection and Data Exfiltration risks while giving your agent the power to work freely.Who This Book Is ForThis is is for practitioners-software engineers who live in Bash or PowerShell and want to integrate world-class reasoning engines directly into their scripts, CI/CD pipelines, and daily workflows.Whether you want to refactor a legacy Express app to FastAPI, write end-to-end tests from user stories, or build a self-correcting research bot, this book provides the production-ready code and architectural patterns you need.The command line gave us power. Generative AI gave us intelligence. This book shows you how to combine them.Start building the future of automation today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
- Print on Demand
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: new. Paperback. Stop building chatbots. Start engineering digital workforces.You have likely built a prototype. You've written a prompt, connected an API, and watched a Large Language Model (LLM) perform a task like magic. But when you try to deploy that script to production, the magic fades. The model hall…ucinates, the logic breaks, and the system fails to handle the chaos of the real world.The era of the single-agent "God Model"-trying to force one prompt to do everything-is over. The future belongs to Multi-Agent Systems (MAS): networks of specialized, autonomous agents that collaborate, debate, and self-correct to solve problems complexity beyond the reach of any single model.The Multi-Agent Systems Blueprint is not a theoretical textbook. It is a field manual for software architects, senior developers, and CTOs who are ready to bridge the gap between experimental AI and enterprise-grade engineering.This book dismantles the hype and replaces it with rigorous architectural patterns. You will move beyond simple "prompt engineering" to master Agentic Orchestration-designing state machines, persistent memory fabrics, and fault-tolerant loops that turn unreliable models into reliable software.Inside, you will discover how to: Master the "Big Three" Frameworks: deep dives into LangGraph, CrewAI, and Microsoft AutoGen, helping you choose the right tool for the right topology.Architect for Reliability: Replace fragile linear scripts with self-correcting loops using Debate Patterns, Critique-Reflect cycles, and Hierarchical Supervision.Build "Day 2" Operations: Go beyond the code. Learn to implement Semantic Monitoring, structured logging, and Docker/Kubernetes deployment pipelines specifically tuned for agentic workloads.Implement Real-World Swarms: Detailed code walkthroughs for building Autonomous Research Teams, Self-Healing DevOps Bots, and Intelligent Finance Auditors.Secure Your Agents: Implement "Human-in-the-Loop" circuit breakers, Pydantic schema enforcement, and enterprise-grade guardrails to prevent hallucinations and prompt injection.Whether you are automating invoice processing in Finance, building a semantic resume screener for HR, or designing the next generation of SaaS products, this book provides the code, the concepts, and the confidence to build systems that think.Don't just write scripts. Architect intelligence. Build the future of autonomous software today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condition: new. Paperback. Stop Writing Scripts. Start Engineering Intelligence.The simple chatbot era is over. The future of AI belongs to autonomous agents-systems that can perceive, reason, plan, and act to solve complex problems.But there is a massive gap between a cool demo and a production-grade system. Most AI…tutorials stop at "magic prompts" and simple API calls. They don't tell you what happens when the model hallucinates, gets stuck in an infinite loop, or outputs broken JSON that crashes your backend.Python for AI Agents is the missing manual for the modern AI Engineer.Written for backend developers, software architects, and Python practitioners, this book skips the hype and focuses on the hard engineering realities of building reliable, deterministic, and scalable agentic workflows. You won't just learn how to make an LLM generate text; you will learn how to architect systems that can reliably execute code, manage their own state, and collaborate in autonomous swarms.Inside, you will master the "Stack" for AI Engineering: From Prompts to Logic: Move beyond fragile string manipulation. Learn to use Pydantic to enforce strict input/output schemas, turning probabilistic LLM responses into structured, type-safe data your application can actually use.The Agentic Loop: Build the ReAct (Reason + Act) pattern from scratch. Understand the cognitive architecture required for an agent to plan a task, execute tools, observe the results, and correct its own errors.Graph-Based Orchestration: Stop building linear chains that break easily. Master LangGraph to design stateful, cyclic workflows where agents can loop, retry, and handle complex logic branches.Tool Use & Function Calling: Teach your agents to use the "hands" of your code. Build safe, secure tool registries that allow LLMs to query databases, call APIs, and execute Python functions.Autonomous Multi-Agent Systems: Go beyond the single agent. Architect Supervisor and Hierarchical patterns where a manager agent delegates tasks to specialized worker agents (coders, researchers, reviewers) to solve complex goals.Production Readiness: This isn't just theory. Learn the vital "boring" stuff that keeps systems alive: unit testing probabilistic code, managing long-term memory with Vector DBs (Chroma/Qdrant), tracing execution with observability tools, and preventing wallet-draining loops.Whether you are building a smart customer support bot, an automated data analyst, or a full-scale coding assistant, this book provides the architectural blueprints you need.Stop relying on luck. Start building with rigor, engineer the future of autonomous systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condition: new. Paperback. Want to go beyond theory and start building real AI applications? This hands-on guide shows you how to use Hugging Face Transformers with Python to create working NLP and machine-learning projects from scratch.Inside you'll discover step-by-step tutorials on everything from fine-tuning pretr…ained models to deploying NLP models to the cloud. You'll learn to work with Gradio and Hugging Face Spaces, build and share interactive demos, and understand best practices for environment setup, version control, and reproducible experiments.By the end of this book you'll be able to: Build and customize AI apps with PythonRun natural language processing projects in Python using pipelines, tokenizers and datasetsFine-tune and evaluate machine-learning models on your own dataDeploy and showcase models on Hugging Face Spaces and other platformsClear explanations, runnable code, and practical tips make this the perfect resource for developers, data scientists, and students who want to quickly master modern NLP tools and bring their ideas to life.Whether you're a beginner exploring Python AI and machine learning projects or a professional adding cutting-edge NLP to your stack, this book gives you the roadmap and tools to succeed. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. Condition: new. Paperback. The first wave of AI-assisted coding is over. Tools like GitHub Copilot made us faster typists, but they've hit a ceiling. We are still "AI snippet managers," stuck with the full cognitive load of our project's architecture. The AI suggests a line, but it can't reason about the entire system….This book is about the second revolution: the shift from AI-assisted to agent-driven development.Cursor 2.0 is the first IDE built for this new era. It's not a plugin; it's an AI-first collaborator designed to understand your entire repository, deploy teams of AI agents, and help you build, test, and secure code at a project-wide scale.But using Cursor like a super-charged VS Code is like using a smartphone only to make calls-you're missing 99% of its power. To unlock its true potential, you must change your mindset. You must learn to direct AI, not just prompt it.Cursor 2.0 in Action is not a theoretical manual; it is a hands-on, practical playbook for the working developer. We don't just look at features; we implement solutions.Inside, you will master the playbooks to: Reason Across Your Entire Codebase: Use at to ask architectural questions, find dependencies, and instantly understand legacy projects.Execute Large-Scale Refactoring: Confidently migrate frameworks (e.g., Express to Fastify) or modernize legacy code (callbacks to async/await) in a fraction of the time.Implement Full-Stack Features from a Spec: Go from a one-page "spec sheet" to a fully scaffolded feature-database schema, API, and UI-in a single operation.Master Multi-Agent Orchestration: Deploy a "team" of AI agents to solve a complex problem in parallel, then review their competing, sandboxed solutions.Perform AI-Driven Security Audits: Use agents as "security reviewers" to find and fix critical vulnerabilities like SQL Injection, XSS, and Prompt Injection before they ever hit production.Engineer Advanced Prompts: Go beyond simple questions and master structured templates like "Role, Context, Task, Format" and "Tests-First" prompting to get predictable, high-quality results.This book is for the professional developer, solutions architect, or engineering manager who recognizes that the barrier to entry is no longer can it be built, but how fast and how securely it can be built.Stop being a coder. Start being an orchestrator. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print on Demand
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Contact seller5-star sellerCondition: New
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Paperback. Condition: new. Paperback. Frustrated by the high costs, slow latency, and data privacy risks of proprietary cloud LLM APIs? This book is the definitive, hands-on guide for AI developers, DevOps engineers, and technical leaders who are ready to take full control of their AI stack. Ollama & Local AI provides a practica…l, code-driven roadmap to self-hosting, fine-tuning, and deploying powerful open-source models like Llama and Mistral directly on your own hardware. Move beyond simple API consumption, gain absolute data sovereignty, and dramatically reduce your inference costs.This is not a high-level overview; it's a complete production playbook. Inside, you will find the precise, step-by-step instructions to: Master Installation: Set up and manage complete Ollama and LocalAI ecosystems, from simple scripts to production-ready Docker and Kubernetes deployments.Fine-Tune Custom Models: Learn to perform efficient LoRA and QLoRA fine-tuning using modern tools like Unsloth and Axolotl to create models with specialized skills.Optimize and Deploy: Convert, quantize, and merge models into the high-performance GGUF format using llama.cpp workflows for deployment in both Ollama and LocalAI.Build Secure APIs: Architect secure, high-throughput REST APIs for your models using an Nginx reverse proxy for enterprise-grade authentication.Orchestrate Workflows: Integrate your local models into complex LangChain pipelines to build powerful applications like Retrieval-Augmented Generation (RAG).Troubleshoot Like a Pro: Diagnose and solve common pitfalls in VRAM management, CUDA conflicts, and performance bottlenecks.Stop renting your AI. Build, deploy, and own your high-performance LLM infrastructure today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
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Paperback. Condition: new. Paperback. The prototype phase is over. Your organization doesn't need another chatbot that writes poetry; it needs an intelligent agent that can query databases, analyze 500-page contracts, and execute complex workflows without hallucinating or leaking data.Anthropic Claude for the Enterprise is the f…ield manual for senior engineers and architects who are done with "vibes-based" AI. This isn't a collection of clever prompts; it is a blueprint for building the nervous system of a modern enterprise. You will learn to leverage the Model Context Protocol (MCP) and Claude 4.5 to construct secure, deterministic agentic systems that survive the rigors of production traffic.From managing the economics of the 200k+ token window to deploying containerized tools, this book strips away the hype to focus on the hard engineering required to make Large Language Models (LLMs) safe and useful.Inside, you will discover how to: Master the Model Context Protocol (MCP): Stop writing brittle glue code. Build standardized, secure servers that connect Claude to your internal databases, Slack workspaces, and GitHub repos without rewriting the wheel.Engineer "Extended Thinking" Programmatically control Claude 4.5's reasoning budget to force planning, self-correction, and citation enforcement before a single line of code is written.Scale Context Economically: Implement context caching strategies to process massive technical manuals and logs while slashing latency and token costs by 90%.Implement "Human-in-the-Loop" Brakes: Design middleware that intercepts high-risk tool calls-like database deletions-and automatically pauses execution for human approval.Deploy with Confidence: Move from localhost to production with battle-tested patterns for Dockerizing MCP servers, tracing agent "thoughts" with observability tools, and enforcing hard cost limits. Stop renting intelligence and start engineering it. Secure your copy today and build the agents your enterprise can actually trust. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
US$ 32.38
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Paperback. Condition: new. Paperback. Stop Copy-Pasting Code. Start Architecting Intelligence.For years, the workflow has been broken. You leave your terminal, open a browser, paste a stack trace into a chatbot, wait, copy the code, paste it back, and pray it works. The power of Generative AI was trapped behind a chat window-unt…il now.The Gemini CLI (gcli) changes the game. It is not just a chatbot in your shell; it is a fully autonomous agent with "hands" to edit files, "eyes" to search the web, and the reasoning power to execute complex, multi-step workflows on your behalf.The Gemini CLI: A Practitioner's Guide is the definitive handbook for developers, DevOps engineers, and data scientists who want to move beyond simple prompts and build agentic networks that understand their code, automate their drudgery, and secure their workflows.In this practical guide, you will learn how to: Master the "Reason and Act" (ReAct) Loop: Understand how agents break down complex goals into actionable plans, executing shell commands and file operations autonomously."Ask Your Codebase" (1 Million+ Tokens): Learn strategies to load entire repositories into context, enabling you to trace features across files, generate architectural diagrams, and perform semantic refactoring in minutes.Automate the Boring Stuff: Build intelligent Git Pre-Commit Hooks that review your code for security flaws before you push, and create Daily Research Agents that scour the web and write briefings while you sleep.Customize Your Agent: Use the GEMINI.md context file to enforce project-specific personas, style guides, and "Do Not" rules that ensure every line of code matches your team's standards.Extend Capabilities with MCP: Dive deep into the Model Context Protocol to build secure, custom tools that let your AI interact with private GitHub repos, Jira tickets, and production databases.Secure Your Workflow: Master the "Human-in-the-Loop" security model to prevent Prompt Injection and Data Exfiltration risks while giving your agent the power to work freely.Who This Book Is ForThis is is for practitioners-software engineers who live in Bash or PowerShell and want to integrate world-class reasoning engines directly into their scripts, CI/CD pipelines, and daily workflows.Whether you want to refactor a legacy Express app to FastAPI, write end-to-end tests from user stories, or build a self-correcting research bot, this book provides the production-ready code and architectural patterns you need.The command line gave us power. Generative AI gave us intelligence. This book shows you how to combine them.Start building the future of automation today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
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Paperback. Condition: new. Paperback. Stop building chatbots. Start engineering digital workforces.You have likely built a prototype. You've written a prompt, connected an API, and watched a Large Language Model (LLM) perform a task like magic. But when you try to deploy that script to production, the magic fades. The model hall…ucinates, the logic breaks, and the system fails to handle the chaos of the real world.The era of the single-agent "God Model"-trying to force one prompt to do everything-is over. The future belongs to Multi-Agent Systems (MAS): networks of specialized, autonomous agents that collaborate, debate, and self-correct to solve problems complexity beyond the reach of any single model.The Multi-Agent Systems Blueprint is not a theoretical textbook. It is a field manual for software architects, senior developers, and CTOs who are ready to bridge the gap between experimental AI and enterprise-grade engineering.This book dismantles the hype and replaces it with rigorous architectural patterns. You will move beyond simple "prompt engineering" to master Agentic Orchestration-designing state machines, persistent memory fabrics, and fault-tolerant loops that turn unreliable models into reliable software.Inside, you will discover how to: Master the "Big Three" Frameworks: deep dives into LangGraph, CrewAI, and Microsoft AutoGen, helping you choose the right tool for the right topology.Architect for Reliability: Replace fragile linear scripts with self-correcting loops using Debate Patterns, Critique-Reflect cycles, and Hierarchical Supervision.Build "Day 2" Operations: Go beyond the code. Learn to implement Semantic Monitoring, structured logging, and Docker/Kubernetes deployment pipelines specifically tuned for agentic workloads.Implement Real-World Swarms: Detailed code walkthroughs for building Autonomous Research Teams, Self-Healing DevOps Bots, and Intelligent Finance Auditors.Secure Your Agents: Implement "Human-in-the-Loop" circuit breakers, Pydantic schema enforcement, and enterprise-grade guardrails to prevent hallucinations and prompt injection.Whether you are automating invoice processing in Finance, building a semantic resume screener for HR, or designing the next generation of SaaS products, this book provides the code, the concepts, and the confidence to build systems that think.Don't just write scripts. Architect intelligence. Build the future of autonomous software today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Condition: new. Paperback. The era of flat retrieval is ending. It is time to give your AI agents a brain, not just a search bar.For the past two years, the industry has relied on Vector RAG to ground Large Language Models. We chopped documents into chunks, embedded them, and hoped that semantic similarity would be en…ough to answer every user question. For simple queries, it worked. But as we push LLMs into complex domains like financial forensics, legal discovery, and scientific research, the cracks are showing. Vector databases are excellent at finding needles in haystacks, but they fail to understand how the hay is stacked. They struggle with multi-hop reasoning, often missing the critical "disconnected facts" that link a cause to an effect across different documents.GraphRAG in Production is the engineering manual for the next generation of intelligent systems.This book is not a theoretical academic text. It is a hands-on guide for software architects and data scientists who are ready to move beyond simple prototypes. It details the architectural shift from retrieving data based on what it looks like (vectors) to retrieving data based on what it is connected to (graphs). You will learn how to construct a dual-memory system that fuses the fuzzy flexibility of vectors with the rigid logic of knowledge graphs, enabling your agents to traverse complex relationships and answer questions that stump standard RAG systems.Inside, you will engineer: The Hybrid Search Engine: Implement Reciprocal Rank Fusion (RRF) to mathematically combine vector similarity scores with graph centrality metrics, ensuring your retrieval is both broad and precise.The Agentic Control Plane: Move beyond brittle linear chains. Build stateful, self-correcting agents using LangGraph that can plan research, write and fix their own Cypher queries, and retry failed searches automatically.The Indexing Pipeline: Stop paying premium prices for menial tasks. Architect a tiered ingestion system that uses small, fine-tuned models for entity extraction and large reasoning models for synthesis, reducing your token costs by up to 80%.Advanced Graph Algorithms: Implement the Leiden algorithm for community detection and "Map-Reduce" style global summarization, allowing your system to answer high-level thematic questions across your entire dataset.Production Infrastructure: Deploy a containerized stack using Docker, comparing the trade-offs between Neo4j, FalkorDB, and Memgraph for specific LLM workloads.Who This Book Is ForThis text is written for Python-proficient engineers, AI architects, and technical leads. If you have hit the ceiling of what standard RAG can do and are looking for a reliable, scalable way to implement structured reasoning, this is your blueprint.Stop building chatbots that guess. Start building agents that know. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Condition: new. Paperback. Stop Writing Scripts. Start Engineering Intelligence.The simple chatbot era is over. The future of AI belongs to autonomous agents-systems that can perceive, reason, plan, and act to solve complex problems.But there is a massive gap between a cool demo and a production-grade system. Most AI…tutorials stop at "magic prompts" and simple API calls. They don't tell you what happens when the model hallucinates, gets stuck in an infinite loop, or outputs broken JSON that crashes your backend.Python for AI Agents is the missing manual for the modern AI Engineer.Written for backend developers, software architects, and Python practitioners, this book skips the hype and focuses on the hard engineering realities of building reliable, deterministic, and scalable agentic workflows. You won't just learn how to make an LLM generate text; you will learn how to architect systems that can reliably execute code, manage their own state, and collaborate in autonomous swarms.Inside, you will master the "Stack" for AI Engineering: From Prompts to Logic: Move beyond fragile string manipulation. Learn to use Pydantic to enforce strict input/output schemas, turning probabilistic LLM responses into structured, type-safe data your application can actually use.The Agentic Loop: Build the ReAct (Reason + Act) pattern from scratch. Understand the cognitive architecture required for an agent to plan a task, execute tools, observe the results, and correct its own errors.Graph-Based Orchestration: Stop building linear chains that break easily. Master LangGraph to design stateful, cyclic workflows where agents can loop, retry, and handle complex logic branches.Tool Use & Function Calling: Teach your agents to use the "hands" of your code. Build safe, secure tool registries that allow LLMs to query databases, call APIs, and execute Python functions.Autonomous Multi-Agent Systems: Go beyond the single agent. Architect Supervisor and Hierarchical patterns where a manager agent delegates tasks to specialized worker agents (coders, researchers, reviewers) to solve complex goals.Production Readiness: This isn't just theory. Learn the vital "boring" stuff that keeps systems alive: unit testing probabilistic code, managing long-term memory with Vector DBs (Chroma/Qdrant), tracing execution with observability tools, and preventing wallet-draining loops.Whether you are building a smart customer support bot, an automated data analyst, or a full-scale coding assistant, this book provides the architectural blueprints you need.Stop relying on luck. Start building with rigor, engineer the future of autonomous 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.
