Agentic Engineering

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.

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.

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.

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.

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။

Your AI prototype works. Now ship it. Most AI frameworks are built for exploration. kdeps is built for production. Define your agent in YAML, declare its dependencies, and deploy it anywhere — Docker, Kubernetes, a standalone binary, an edge device — without rewriting a line when you switch LLM providers. AI Appliances is the hands-on guide to building autonomous AI agents and multi-agent systems with kdeps: deterministic pipelines, real error handling, real deployment, and no vendor lock-in. Write YAML. Run anywhere. Own everything.

AI coding agents can move fast, but speed without clear intent creates expensive mistakes. This practical guide shows how Behavior-Driven Development turns specifications into a reliable contract between you and autonomous coding systems, helping agents build the right thing, catch problems earlier and produce software you can trust.

Specification Engineering turns complex ideas into clear, workable specifications. This practical guide takes you from core principles to advanced methods for defining, validating, managing and evolving requirements across products, software, systems and services. Built for real-world use, it gives you the tools to create specifications that people can understand and act on.

They say AI is coming for junior developers first. This is a junior's honest case for the opposite: how driving agents well, on real legacy code, makes you level up faster instead of getting left behind. The failures are left in.

Domine a arquitetura de sistemas de IA autônomos. Um guia completo cobrindo motores de raciocínio, memória de longo prazo, orquestração multiagentes, otimização de custos e o Model Context Protocol (MCP). O livro definitivo para arquitetos e desenvolvedores.

面向智能体重度用户的实战可靠性手册：不教造智能体、不教提示词技巧、不做产品测评； 每天用智能体 2 小时以上、已配多个工具、需要一套可预算 / 可监控 / 可验收的运营方法的实践者；

ChatGPT can save you time, sharpen your ideas and change the way you work, but only if you know how to use it well. This practical guide takes you from your first prompt to advanced techniques, with real-world examples for writing, research, coding, business and creativity. Learn what works, what doesn’t and how to get better results every day.

Vectors, embeddings, retrieval, agents, and evaluation are all built from first principles inside the chapter that needs them. No mathematics. No machine learning background. No prior AI experience and no framework knowledge is required. We build with plain Python and small, single-purpose libraries.  

What if your AI didn’t just answer questions—but solved problems?Imagine an AI that doesn’t just chat—it plans, reasons, and takes action. An AI that can fetch data, run diagnostics, collaborate with other agents, and even handle your most tedious tasks—all while you focus on what matters.Google’s Agent Development Kit (ADK) makes this possible,

Build smarter AI applications with Redis at the core. This practical guide shows you how to use vector search, RAG, semantic caching, agent memory and real-time inference to create fast, scalable systems. With runnable Python examples throughout, you’ll learn how to take AI projects from prototype to production.