Agentic Engineering

Spring AI gives you one API for chat models, embeddings, vector stores and tools, and swaps the provider with a single property. This book teaches it by building: thirteen chapters, seventeen complete Spring Boot projects, and every program run on a free local model and on a paid cloud model before a word was written about it. Start chapter 1 with no account, no API key and no credit card.

Agentic AI is everywhere, but what does it actually mean? This book cuts through the hype and explains how AI agents work, how they are built and where they can go wrong. Starting from the basics, it gives you a clear, practical understanding of the technology and what it takes to trust an agent in the real world.

What happens when AI starts making the decisions behind your database queries? This practical guide explores how modern optimizers work, then shows how machine learning and reinforcement learning can improve them. Learn to build, test and deploy AI-driven optimization systems that deliver real performance gains.

An accessible guide to how large language models become agents that can reason, use tools, remember, plan, interact with software, and collaborate with other agents. Written for readers who understand the basics of LLMs but do not want a mathematics-heavy treatment.

AI can generate an answer. But can it safely take action?AI agents are changing software by giving intelligent systems the ability to plan, use tools, interact with external systems, and execute multi-step workflows.Moving from an impressive prototype to a dependable production system, however, requires much more than a powerful model.AI Agents & Agentic Workflows in Production explores the engineering principles behind production-grade agentic systems—from architecture and tool use to memory, planning, orchestration, evaluation, security, reliability, observability, scalability, governance, and continuous operation.Learn how to build agents that don't just act—but act reliably, safely, measurably, and under control.

Build AI agents that actually hold up in production. This practical guide covers agent architecture, tool use, multi-agent systems, security, reliability, observability and operations, with working code throughout. Learn how to build agents that are dependable, cost-aware and ready for the real world.

Network traffic can look like noise until you know what to look for. This practical guide shows you how to investigate unfamiliar protocols, analyze packet captures and build reliable workflows with Claude Code. Learn to reverse engineer traffic, test your assumptions and turn raw network data into findings you can trust.

Rapid-development frameworks across Ruby on Rails, Node.js, PHP, Python, and .NET revolutionized how quickly we can ship software. But what happens when your business domain grows complex? Relying on Active Record and heavy ORM patterns tightly couples your database to your business logic. As your product matures, this coupling creates brittle, difficult-to-maintain codebases that resist change and slow down your team. The solution is the Domain Model Pattern (the foundation of Domain-Driven Design). It isolates business rules from infrastructure, creating a pure, scalable architecture. Historically, however, this pattern required a massive amount of effort and boilerplate, making it too slow for fast-paced development. That changes today. We are at an AI inflection point. Modern AI coding assistants excel at generating boilerplate and wiring infrastructure, effectively eliminating the traditional drawbacks of DDD. AI finally bridges the gap between the rapid speed of framework scaffolding and the architectural purity of a Domain Model. But to harness AI reliably, you cannot simply let it generate code unguided. You need a structured methodology to guide the machine. In this book, you will learn:How to build reliable, maintainable software using a strict, AI-assisted pipeline: Use Cases → Gherkin → Event Storming → Strategic & Tactical DDD.How to create implementation-agnostic class diagrams that serve as universal blueprints, allowing you to generate code in OOP languages (Java, C#, PHP, Python) or Functional languages (Elixir, Clojure, F#).How to future-proof your systems with a timeless methodology that prioritizes the longevity of business logic over the ephemeral nature of web frameworks.How to scale your architecture gracefully from a Majestic Monolith to Microservices without a complete rewrite. The methodology is language agnostics but in this edition all codes samples are in TypeScript. Whether you want to accelerate your development velocity, improve your system's resilience, or simply reclaim your craft by building beautiful, majestic systems, this book provides the blueprint.Discover how to combine the timeless principles of DDD with the power of AI.

Your manager calls you into a meeting. Everyone is cheerful. The Head of Sales is there. “We heard AI is really useful for boosting productivity. We’d love to explore how it can help us in Sales.” Your manager agrees. “This is the perfect pilot. Please give us a concept until next week so we can see what we can do.”The follow-up arrives quickly. Sales is overloaded. Everyone is busy serving customers. Nobody has time for a workshop, but they do have Sales.xlsx. That is what you get. The clock is ticking.And then you open Sales.xlsx.You have nothing. No requirements, no workflows, not even a proper data model. What you have is a snapshot of the messy reality Sales is dealing with every day. Customer names, contacts, opportunity values, dates, products, notes, colors that apparently mean something, and a field called Status_Final_2, which tells you enough about its history that asking about Status_Final_1 would probably ruin the afternoon.And then it strikes you: they are not even using the CRM. You do not know why. Nothing you know from IT seems to apply in Sales, and they do not particularly care. They have customers to serve. Whatever happened between the shiny CRM rollout and this spreadsheet happened years ago. Today, Sales.xlsx is the truth.And now, just because AI promises productivity, they are asking for the benefits.How is that even supposed to work?

Behind every breakthrough in Generative AI isn't magic—it's mathematics."​Ever wondered what actually happens inside a Large Language Model when it generates the next token? Beyond the hype and high-level API calls lies an elegant foundation of probability, vector geometry, and linear algebra.​In The Mathematics of Generative AI, Ahmed Adawy strips away the academic jargon to reveal the exact core equations driving modern LLMs. From conditional probability and high-dimensional embeddings to Query-Key-Value attention mechanisms, this capsule equips software engineers and AI practitioners with the intuition needed to build, debug, and innovate with confidence.​Stop treating AI like a black box. Master the equations beneath the intelligence.

Os agentes de programação com IA avançam rápido. O fluxo de trabalho do Git, baseado em staging e commits, nem sempre consegue acompanhar esse ritmo.Juju-chu! mostra como usar o Jujutsu🐦‍⬛ para registrar commits automaticamente, desfazer operações com confiança e reorganizar com mais segurança as alterações difíceis de organizar que a IA gera. Tudo isso mantendo a compatibilidade com o Git e o GitHub. Para desenvolvedores que já usam o Git e querem um fluxo de trabalho com IA mais tranquilo.

AI can sound certain even when it is completely wrong. This book takes a practical approach to building RAG systems and AI agents that ground answers in evidence, verify what they generate and know when to stop. Learn how to make AI more reliable in the places where getting it wrong really matters.

Los agentes de programación con IA pueden avanzar más rápido de lo que permite el flujo de trabajo de Git basado en staging y commits.Juju-chu! muestra cómo Jujutsu🐦‍⬛ te da commits automáticos, una forma confiable de deshacer operaciones y una manera más segura de reorganizar los cambios desordenados que genera la IA, todo con compatibilidad total con Git y GitHub. Para desarrolladores que ya usan Git y quieren un flujo de trabajo con IA más tranquilo.

AI agents are moving from experiments into real production systems. This book shows experienced engineers how to design, deploy and operate reliable agent systems that can scale. From architecture and infrastructure to security, reliability and governance, it provides practical patterns and production-ready examples for building autonomous systems that work in the real world.

Static code analysis is more than running a linter and fixing warnings. This book shows how to build practical analysis pipelines with Claude Code and deterministic tools, combining AI-driven insights with reliable checks to improve code quality and security across projects of any size.