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Building Agentic AI Systems with Claude Code: A Practical Guide to MCP, Sub-Agents, Hooks, Computer Use, and Production-Ready AI Workflows in 2026
English | May 10, 2026 | ASIN: B0H18XCKPG | 357 pages | Epub | 1.18 MB
AI can do more than answer questions. It can read your codebase, plan multi-file changes, run your tests, fix its own mistakes, and ship working software. That shift from conversation to action is what agentic AI means in practice. This book shows you how to build, deploy, and operate agentic systems using Claude Code, the most capable agentic coding tool available today.This is not a theoretical overview or a collection of prompts. It is a field guide for builders. You will learn how the agentic loop works under the hood, how to write CLAUDE.md files that dramatically improve output quality, how to connect Claude Code to any external system through the Model Context Protocol (MCP), how to coordinate multiple agents using sub-agents and parallel execution, and how to package repeatable workflows with skills, hooks, and plugins.Part I covers the foundations: what agentic AI actually is, how the 2026 technology stack fits together, how to get started with Claude Code, and how the agentic loop of read, plan, execute, and evaluate drives every session.Part II dives deep into core workflows: code generation and large-scale refactoring (including real migration case studies), MCP server configuration and custom server development, sub-agent coordination patterns, skill and hook design, and browser-based computer use for end-to-end testing and verification.Part III tackles production systems: architecture patterns for multi-agent deployments (supervisor-worker, pipeline, swarm, evaluator-optimizer), building custom agents with the Agent SDK, memory and context management for large codebases, safety and governance frameworks, and testing and evaluation harnesses that measure real reliability.Part IV covers real-world applications: adoption patterns for software teams, practical workflows for non-engineers who want to build with Claude Code, enterprise governance and cost management, and a clear-eyed look at what comes next.The book includes four appendices: a glossary of agentic AI terminology, evaluation rubrics for scoring agent reliability, workflow templates for common tasks, and a prompt library with ready-to-use patterns for code generation, review, refactoring, and documentation.Whether you are a software engineer looking to 10x your development workflow, a technical leader evaluating agentic AI for your organization, or a non-engineer discovering that you can build real software by describing what you want, this book gives you the mental models, practical skills, and production discipline to do it right.18 chapters. Built for builders who ship.

Building AI Applications with Foundation Models: Create Real-World LLM, RAG, Agent, and Multimodal Apps from Prototype to Production
English | June 13, 2026 | ASIN: B0H575XJHN | 759 pages | Epub | 399.51 KB
Building AI Applications with Foundation Models: Create Real-World LLM, RAG, Agent, and Multimodal Apps from Prototype to ProductionBuild AI apps that move beyond flashy demos and hold up in real software projects.Many developers can call an AI API. Fewer know how to turn that model call into a reliable application with clean code, structured outputs, retrieval, tool use, evaluation, security, and deployment.Are you tired of tutorials that stop at a chatbot? Do you want to build LLM apps, RAG systems, AI agents, and multimodal workflows that can actually support users, documents, tools, and production requirements?Building AI Applications with Foundation Models gives you a practical path from setup to deployment. Instead of vague theory, this book shows how to build working AI application layers around foundation models using Python, FastAPI, prompt engineering, embeddings, vector search, retrieval-augmented generation, tool calling, agents, multimodal processing, testing, guardrails, and production workflows.You will learn how to:Set up a clean AI engineering workspaceBuild reusable model clients and structured prompt workflowsCreate semantic search and private-document RAG appsConnect AI systems to tools, files, databases, and APIsBuild controlled agents with limits, logs, and approval pointsAdd evaluation, security, monitoring, and deployment practicesThis book is written for developers, builders, technical founders, software teams, and advanced learners who want practical AI engineering skills without training models from scratch.Each chapter builds toward real-world application patterns: document assistants, business tools, multimodal review workflows, production APIs, and deployment-ready systems.Buy this book today and start building foundation-model applications with the structure, confidence, and practical discipline needed to move from prototype to production.

Building AI Agents with n8n: Automate Workflows, Integrate LLMs, and Orchestrate Tools at Scale
English | June 12, 2025 | ASIN: B0FD2VKB6X | 353 pages | Epub | 319.46 KB
Building AI Agents with n8n: Automate Workflows, Integrate LLMs, and Orchestrate Tools at ScaleThe future of automation is intelligent. Traditional rule-based workflows are giving way to autonomous agents that reason, adapt, and act across complex systems. Powered by large language models like GPT-4, Claude, and Gemini, these AI agents are transforming business operations, customer support, research, and decision-making.n8n - the leading open-source automation platform - enables you to build powerful AI workflows that connect APIs, process data, and trigger intelligent behavior at scale. This book is your complete blueprint to design, deploy, and orchestrate AI agents using n8n.What This Book DeliversYou'll learn how to go beyond basic automation and build fully functional AI agents using real-world tools and APIs. This includes:Connecting to LLM APIs like OpenAI, Anthropic, and Google GeminiStructuring multi-step reasoning chains using low-code nodesImplementing Retrieval-Augmented Generation (RAG) with vector databasesDesigning modular tools with HTTP, Webhook, and Function nodesPersisting agent memory with Redis and databasesDebugging workflows, handling failures, and monitoring performanceDeploying scalable workflows across cloud and on-prem environmentsEvery chapter is crafted for deep technical clarity - with flowcharts, pseudocode, prompt templates, and implementation-ready guidance. You'll gain hands-on expertise to design agents that operate across Slack, Notion, Pinecone, PostgreSQL, and more.Who This Book Is ForThis guide is built for AI developers, automation engineers, low-code builders, DevOps teams, and workflow architects who want to bridge LLM reasoning with real-world tooling. Whether you're integrating agents into business operations or experimenting with autonomous assistants, you'll get a proven framework to build systems that are modular, scalable, and production-ready.What You'll GainBy the end of this book, you'll be able to:Build intelligent agents that reason, retrieve, and actOrchestrate tools and APIs using n8n's visual programming modelCreate reusable subflows, prompt systems, and memory handlersSecure, scale, and debug agents across environmentsApply best practices in prompt engineering, fallback design, and RAG pipelinesTake the Next Step in Agent AutomationIf you're ready to master the art of intelligent workflow design, this book will give you the architecture, tools, and real-world patterns to lead the AI agent revolution.

Building AI Agents with OpenClaw: From Installation to Real-World Automation : A complete, real-world guide to building AI agents with OpenClaw
English | February 4, 2026 | ASIN: B0GLHRLKHV | 102 pages | Epub | 546.95 KB
Stop reading about AI agents. Start building one.AI agents are everywhere, but most guides explain ideas without showing how to build something useful. This book is different.Building AI Agents with OpenClaw teaches you how to design, control, and deploy AI agents that perform real tasks-using practical examples you can run on your own system.Inside this book, you will:Install and run an AI agent on your computer or Raspberry PiControl your agent using WhatsApp messagesAutomate reminders, reports, and system checksSchedule tasks that run without manual inputExtend the agent with custom skillsDeploy the agent to run 24/7Debug problems and fix performance issuesSecure your setup and avoid common mistakesReal-world use cases you will build:A personal AI assistant that replies on WhatsAppA daily calendar and reminder automationA home or server monitoring agentA content drafting assistantA small business operations botEach chapter builds on the previous one, making it easy to follow even if you are new to AI agents or automation.This book is ideal for:Beginners exploring AI agentsDevelopers and automation enthusiastsRaspberry Pi usersStudents and self-learnersAnyone who wants AI to save time, not create confusionIf you want a practical, real-world introduction to AI agents, this book gives you everything you need to get started.

Building AI Agents with CrewAI: From Fundamentals to Production-Ready Multi-Agent Systems with Real-World Financial AI Applications
English | May 20, 2026 | ASIN: B0H2FPD5SX | 1259 pages | Epub | 656.64 KB
Master the next evolution of Artificial Intelligence - Autonomous Multi-Agent Systems.Artificial Intelligence is rapidly moving beyond single prompts and chatbots into a new era of intelligent, collaborative AI agents capable of reasoning, planning, analyzing, and executing complex workflows autonomously.In Building AI Agents with CrewAI, acclaimed technologist AJAI KUMAR MEDHAVI delivers a practical, industry-focused guide to designing, building, deploying, and scaling production-ready multi-agent AI systems using CrewAI.This comprehensive hands-on book takes readers from foundational concepts to advanced real-world implementations with a strong focus on financial AI applications, intelligent automation, agent orchestration, risk analysis, research workflows, and enterprise-grade deployment architectures.Inside this book, you will learn:✔ Fundamentals of AI agents and autonomous systems✔ CrewAI architecture and multi-agent orchestration✔ Designing collaborative AI agent teams✔ Memory, planning, tools, and reasoning workflows✔ Building research, analysis, and reporting agents✔ Financial AI use cases and intelligent automation✔ Integrating LLMs, APIs, vector databases, and tools✔ Production deployment, monitoring, and scaling✔ Security, reliability, governance, and observability✔ Advanced real-world AI engineering patternsWhether you are an AI engineer, software developer, researcher, startup founder, fintech innovator, or enterprise architect, this book provides the practical knowledge and production strategies needed to build sophisticated AI agent ecosystems for real-world applications.If you want to move beyond simple chatbots and build the future of autonomous AI systems, this book is your complete roadmap.

Building AI Agents: Platforms, Frameworks, and Real-World Implementations: How to Build AI Agents Using LangChain, LangGraph, CrewAI, LlamaIndex, Vertex AI, Google Opal, Copilot Studio, n8n, and More
English | December 16, 2025 | ASIN: B0G7BQT516 | 176 pages | Epub | 2.86 MB
AI agents are no longer experimental-they are becoming core building blocks of modern software systems. From document analysis and workflow automation to enterprise decision support, AI agents now operate at the intersection of reasoning, data, and action.Building AI Agents Using Modern Platforms and Frameworks is a practical, architecture-first guide to designing, implementing, and scaling AI agent systems across today's most widely used tools and platforms.Rather than focusing on a single vendor or framework, this book teaches you how to think like an AI agent architect. You will learn how to design agents that are reliable, governable, and production-ready-whether you are using developer frameworks, no-code platforms, or enterprise cloud services.What You'll LearnWhat truly distinguishes AI agents from chatbots and automationsCore agent design patterns, including reactive, role-based, workflow, and human-in-the-loop architecturesHow to implement the same real-world use case across multiple platforms and frameworksHow to choose the right agent platform based on risk, scale, governance, and complexityBest practices for security, compliance, and auditability in agent systemsHow to future-proof agent designs as tools and models evolvePlatforms and Frameworks CoveredLangChain and LangGraphCrewAILlamaIndexOpenAI Custom GPTs and experimental agent frameworksGoogle OpalMicrosoft Copilot StudioGoogle Vertex AI Agent Buildern8n, Zapier, and MakeA Real-World, End-to-End Use CaseThroughout the book, a single enterprise use case-document intake, analysis, and decision support-is implemented repeatedly across different platforms. This approach reveals what stays consistent in agent design and what changes depending on the tool.Who This Book Is ForSoftware engineers and solution architectsEnterprise IT and automation teamsTechnical product managersAI practitioners moving from prototypes to productionAnyone responsible for building AI systems that must be reliable and auditableThis is not a prompt collection or a vendor sales guide. It is a practical engineering reference for building AI agents that work in the real world-safely, responsibly, and at scale.If you want to move beyond experimentation and start building AI agents you can trust, this book is your guide.

Build a GenAI Desktop Assistant Using Streamlit: A Complete, Production-Ready, Zero-Cost AI Application - From Blank Screen to Live URL (The Weekend Developer Series)
English | June 13, 2026 | ASIN: B0H56JWBMB | 221 pages | Epub | 15.18 MB
Syntax is cheap. Execution is everything.You watched the tutorials and know Python. You marveled at AI generating code in seconds. Then you open a blank file to build something real - and freeze.Connect the model securely to a web interface, stop it from forgetting everything on refresh, or host it publicly without a cloud bill.You realize what no tutorial prepared you for: knowing syntax is not the same as knowing how to ship software.This book is the bridge.What You Will BuildThis is no toy script. You will build Aura Workspace AI - a professional, secure, multimodal assistant with a polished interface, persistent memory, live streaming, document analysis, voice input, web search, and a local database.Using only Python. Deployed to the public internet. For zero cost.This book is part of The Weekend Developer SeriesLife is overwhelmingly busy. The Weekend Developer Series is built for this reality.Our promise: start on a Saturday morning and you will have a functional AI app deployed live on the public internet before Monday.Across 8 Chapters and 4 Sections, You WillSection I - The Foundation: Configure an isolated Python environment and build the Streamlit interface with custom sidebars and upload zonesSection II - The Brain: Securely connect the Gemini API, defeat the Streamlit amnesia trap with a session memory vault, and implement real-time streaming and document analysisSection III - The Launch: Push to GitHub and deploy live on Streamlit Community Cloud - at absolute zero costSection IV - The Masterclass: Scale with browser-native voice controls, live web search grounding, and permanent SQLite database storageWhat Makes This Book DifferentMost development books fall into one of two failure modes: the academic textbook that drowns you in theory, or the recipe book of copy-paste snippets that break the moment something changes. This book is neither.Every chapter follows the same repeatable protocol: real-world analogy → architecture map → exact terminal commands → complete unredacted code → rigorous test plan → milestone checklist.The Technical Skills You Will OwnThe Streamlit reactive execution loop - mastered, not fearedSession state persistence vaults that survive browser refreshesReal-time stream generator traversal for live typing effectsMultimodal file payload injection for the Gemini vision engineSecure .env credential management that never leaks into public repositoriesCloud deployment confidence - local terminal to global URL, mapped correctlyWho This Book Is ForData analysts who can parse a CSV but feel lost when a web dashboard is neededSelf-taught developers who need a portfolio asset proving real full-stack AI capabilityHobbyists tired of monthly subscriptions for chatbots they could build themselvesStudents who want to see academic Python translate into shipped softwareYou do not need a machine learning background. You do not need a CS degree. You need basic Python logic and the ability to open a terminal. We handle everything else.Your Toolkit - 100% FreePython 3.14+ * Streamlit * Google Gemini API (free tier) * Visual Studio Code * Streamlit Community CloudAbout the AuthorsSharanam and Vaishali Shah are Mumbai-based authors with over 23 years of hands-on experience in application architecture and AI systems. Together they have co-authored over 40 technical books, built on one absolute belief: the best way to learn is to build.

Build a GPT-Style AI: From Scratch with PyTorch, CUDA AMP, LoRA, QLoRA, Evaluation and Local Serving
English | May 6, 2026 | ASIN: B0GY9BBLCW | 631 pages | Epub | 2.77 MB
Most LLM books show either high-level concepts or finished tools. This book teaches the full engineering path.Train a GPT-Style LLM starts from an empty folder and builds a language model project step by step. You will create the environment, tokeniser, dataset pipeline, decoder-only transformer, training loop, checkpoint system, validation reports, mixed precision path, generation module, LoRA workflow, QLoRA workflow, release checklist, and local API.The emphasis is practical understanding. You will see why tokenisation comes before modelling, how next-token training works, how checkpoints protect long runs, why validation loss is not enough, how CUDA AMP uses torch.autocast and GradScaler, why BF16 usually does not need gradient scaling in the same way as FP16, and how CPU and MPS fallbacks should be handled cleanly.The book is honest about scale. A 0.5B model is large enough to teach real engineering pressure. A 7B model changes the hardware conversation. Frontier commercial systems such as GPT-5.5 and Claude require industrial infrastructure, proprietary data pipelines, post-training, safety systems, evaluation programmes, and serving platforms. This book does not pretend otherwise. Instead, it gives you the machinery and judgement to understand how LLM systems are built.You will learn how to:Build a GPT-style transformer directly in PyTorch.Train and inspect a tokeniser.Prepare datasets for next-token prediction.Implement training, validation, checkpointing, and resume.Use CUDA AMP with torch.autocast and GradScaler.Understand BF16, FP16, CPU fallback, and Apple Silicon MPS behaviour.Design a 0.5B model configuration before wasting compute.Reason about scaling from 0.5B to 3B and 7B.Fine tune existing models with LoRA and QLoRA.Evaluate, release, quantise, and serve a model locally.The companion code is available at

https://github.com/riccione83/tiny-llm.This

is a practical technical book for software engineers, machine learning practitioners, students, founders, and serious builders who want to understand LLMs by constructing the system beneath them.

Build a DeepSeek Model from Scratch : Design, Train, and Scale High-Performance LLMs with MoE, Long Context, and Efficient Attention (AI & Applied ML Book 2)
English | December 16, 2025 | ASIN: B0G7JSJZ5D | 183 pages | Epub | 1.75 MB
Build a DeepSeek Model from Scratch addresses a hard truth many AI engineers face today: most resources explain what large language models are, but very few show how to actually build one that scales, stays stable, and performs competitively under real-world constraints. If you've tried to move beyond toy models-only to hit walls around memory limits, training instability, slow attention, or runaway costs-this book is written for you.This book delivers a complete, production-minded blueprint for designing and training DeepSeek-class large language models from the ground up. It walks through the full lifecycle of modern LLM engineering: defining an efficient decoder-only architecture, integrating Mixture of Experts for scale, enabling long-context reasoning with efficient attention, and deploying models that can be served reliably and cost-effectively. Every design choice is explained from an engineering perspective, grounded in practices that work at billion-parameter scale.You'll learn how to move from architectural intent to operational reality-without hand-waving, fragile shortcuts, or purely academic abstractions.By the end of this book, you'll be able to:Design a DeepSeek-style LLM architecture optimized for throughput, memory, and costImplement and scale Mixture of Experts layers without load collapse or routing instabilityTrain long-context models using efficient attention and KV cache strategiesBuild streaming data pipelines that scale cleanly and remain reproducibleStabilize billion-parameter training with the right optimizers, precision, and recovery workflowsEvaluate reasoning, language, and code performance without benchmark overfittingDeploy and serve large models using quantization and modern inference patternsWritten for AI engineers, ML researchers, and systems builders, this book emphasizes practical execution over theory and replaces guesswork with tested engineering patterns. It assumes you want to build, not just experiment-and that reliability, performance, and scalability matter as much as raw capability.

Build Your Own AI Agent with NotebookLM & Claude: A No-Code AI Workflow to Turn 2 Hours of Daily Routine into 10 Minutes
English | May 14, 2026 | ASIN: B0H1TJCBQ6 | 103 pages | Epub | 37.71 MB
Imagine this: It's 4:15 PM on a Tuesday, exactly three months from today.You aren't hunched over your keyboard,frantically re-explaining your job to a chatbot for the fifth time this morning. Instead, you're sitting on your patio, the sun warming your shoulders, casually glancing at your phone.You just received a notification: your custom AI Agent-the one you built without writing a single line of code-has just finished a deep-dive competitive analysis and drafted three high-level proposals while you were out for a walk.■ The Concrete Shift: From Typing to DesigningYou open your laptop, but there's no "blank page" anxiety. You don't see a blinking cursor waiting for you to type another two-paragraph prompt. You see:A "Fully Stocked Kitchen":Your AI already knows your industry, your tone, and your boss's exact quality standards.The 10-Minute Reality:A task that used to anchor you to your desk for two hours is now polished and ready to send in ten minutes.Expert-Level Precision:Your colleagues are asking, "How did you get this so smart?" because your AI now thinks like a seasoned veteran, not a digital goldfish.■ Deep Emotion: The End of Decision FatigueThat heavy, "vaguely exhausted" feeling that used to sit in your chest every Monday morning?It's gone. It has been replaced by an unshakable confidence.You no longer feel like a "master chef" who has to restock the entire kitchen every morning just to cook one meal. You feel in control.You aren't working for the AI anymore; the system is working for you.■ The Ultimate ParadoxIt's strange, isn't it?You used to think that "getting better at AI" meant learning to write longer, more complex prompts.Now, you've realized the truth:The most successful people use the fewest prompts.You aren't typing more; you're achieving 10x the results because you finally built a system that barely requires you to prompt at all.■ The Future Fork: Which Path Will You Choose?You stand at a crossroads today.The Vicious Cycle:You can stay in the "Tired Club," spending 72% of your energy for a measly 5% gain in time, forever trapped in the "copy-paste dance" of manual AI work.The Automation Blueprint:Or, you can spend the next 48 hours building a permanent digital asset that recovers its own "build time" in less than a month-and pays you dividends for the rest of your career.Don't let the insight fade.Your first personal AI agent is waiting for its instructions.CLICK "BUY NOW" TO TURN 2 HOURS OF ROUTINE INTO 10 MINUTES OF IMPACT