Agentic Engineering

Context Engineering mostra como estruturar a informação do projeto para que a IA produza software consistente, previsível e alinhado com os teus padrões de engenharia.

This book presents an architecture-first approach to designing trustworthy GenAI applications. Using Digital Forensics and Incident Response (DFIR) as a continuous case study, you will progressively build an AI-assisted investigation system. If you want to move beyond building AI applications that simply work, and start architecting AI systems that professionals can trust, this book is for you.

What if agentic AI was less about hype and more about work you can actually inspect?OpenClaw and Hermes for Agentic AI is a practical field guide to building with local agents, memory, tools, sessions, cron jobs, and ACP bridges. It shows how to turn fuzzy ideas into reliable workflows with clear boundaries, real artifacts, and human review where it matters most.If you want a grounded look at how agent systems really operate, this book starts there.

You leave Claude Code running and come back to something missing. This book is arranged by the accident, not the mechanism: databases dropped, files deleted, git history rewritten, the rewind command taking work with it. Three of the four get the exact hook that blocks the command before it runs, with the recovery if it already happened; no PreToolUse hook sees the rewind command, and the book gives the settings-level lever and names the one place a refusal could still live. Appendix B prints all 195 commands the guards were fired at, refusals and allowances both.

Your agent passed every load test and timed out in production anyway. This free practitioner's guide shows why the traditional performance playbook breaks for agentic AI, and what replaces it: latency budgets, bounded autonomy, token SLOs, and production-grade observability, from a performance engineering and observability architect with two decades in production.

A IA mudou a forma de escrever código, mas o verdadeiro impacto está na engenharia de software. Este livro explica por que especificação, arquitetura e contexto passaram a ocupar o centro do desenvolvimento moderno.

Most agent books teach prompts and frameworks. Agent Engineering teaches the judgment and engineering discipline required to build AI agents that are reliable, secure, testable, and ready for production. Follow a practical Claude-based project from first agent to production-grade autonomous system—and receive every future update to this early-access edition.

ဥပမာအားဖြင့်၊ သင်က AI ကို "မိုးရွာရင် ထီးယူသွားပါ" ဟု ခိုင်းထားလျှင်၊ မိုးသည်းထန်စွာ ရွာနေသော်လည်း အိမ်ခေါင်မိုး ပြိုကျနေပါက ၎င်းသည် ထီးကိုသာ ကိုင်၍ အိမ်ထဲတွင် ငုတ်တုတ်ထိုင်နေပေလိမ့်မည်။ အဘယ်ကြောင့်ဆိုသော် "အိမ်ခေါင်မိုး ပြိုလျှင် ပြေးပါ" ဟူသော စည်းမျဉ်းကို သင်က ထည့်မပေးထား သောကြောင့် ဖြစ်သည်။

You have been using AI as a faster keyboard.The engineers who will define the next decade are using it as a cognitive workforce they direct, constrain, and govern. The gap between those two practices is not a matter of better prompts. It is a matter of an entirely different mental model.This book is that mental model. Built from first principles. Illustrated through 28 chapters of real architectural decisions, real failures, and real production systems.From execution to orchestration. The complete practitioner guide.

Your AI coding agent starts every session knowing nothing about your project, so it guesses - and the guess is sometimes expensive. The AGENTS.md Cookbook shows you how to write short, specific rules files, how to test them with a simple before-and-after method, and includes twelve ready-to-adapt recipes for real project types, labeled honestly about what has and has not been tested.

Go from AI beginner to confident Claude Code user in just seven days. Learn to build software, automate tedious tasks and tackle complex projects with less effort. Whether you're a developer or simply curious about what AI can do, this practical guide shows you how to turn Claude Code into a powerful tool for getting more done.

HNSW powers almost every vector database, and few of the engineers who rely on it have seen inside it. Vector Search Inside Out builds it from first principles in 92 step-by-step figures, then tunes it on 1.18 million real vectors. You'll know exactly what M, efConstruction and ef do, and how to set them for your own data.

Move from observing AI agents to operating them through runtime evidence, evaluation, economics, reliability, and governed action.This practitioner-focused book develops an architecture for production AI systems where traces, evaluations, safety signals, cost, business outcomes, and operational authority become part of one evidence model.

Curious about agentic AI but not sure where to start? This beginner-friendly guide takes you from the basics to building your own AI agents, even if you have no coding experience. Learn how agents work, choose the right tools and turn your ideas into practical, reliable solutions for real-world problems.

The battle-tested prompt framework and architectural system rules to turn any AI coding assistant into a principal full-stack engineer.