Books

Building an impressive AI agent demo is easy. Running one in production—reliably, at scale, with governance—is where most enterprises get stuck. Agentic Orchestration shows how BPMN-based process orchestration governs AI agents at scale, drawing on real deployments across banking, telecom, and healthcare. Reasoning without orchestration is expensive chaos. This book shows the way out.

Die Nutzung von KI zum Programmieren ist einfach. Sie zu nutzen, ohne die Kontrolle zu verlieren, schon weniger. Spec-Driven Development ist die Methode, um deine Idee in eine Spezifikation zu verwandeln, die die KI präzise ausführt — ohne Endlosschleifen, ohne kaputten Code, ohne wieder bei null anzufangen.

16 章手写一个 CLI Agent。从 Hello World 到 Skill 插件系统，只用 Python 标准库 + OpenAI SDK。每章附真实项目踩坑笔记——来自 git 提交历史的 bug、设计失误和凌晨调试的教训。读完你会理解 Agent 的底层运作方式，而非只是会用某个框架。

Go beyond prompts and learn how to build AI systems that hold up in production. This book shows how to make context the foundation of reliable LLM applications, covering practical patterns, real trade-offs and proven engineering techniques with clear examples you can put to work right away.

The future of AI depends on how we measure it. This book cuts through the hype to show what benchmarks really tell us, where they fall short and why better evaluation leads to better models. Practical, clear and grounded in real-world experience, it is an essential guide for anyone building, studying or deploying modern AI.

Build serious AI systems on NVIDIA DGX Spark with a practical guide that goes far beyond setup. Learn to deploy LLMs, create RAG pipelines, orchestrate AI agents, and optimize performance for real production workloads. Whether you are experimenting or scaling enterprise AI, this book shows you how to get there.

A £4.1M PRA compliance failure. A fictional bank called Avon & Wessex. Sixteen chapters and 23 systems showing exactly how to build production-grade, auditable AI for credit, market, operational and liquidity risk, model governance, and regulatory reporting — the practitioner's guide UK banking has been missing.

Evaluation Engineering for AI Systems is a practical guide to building reliable AI evaluations for production. Learn how to measure performance, compare models, detect regressions and create evaluation pipelines using real-world examples, modern Python and proven industry practices.

What if TransE, ComplEx, RotatE and the rest of the knowledge graph “model zoo” were different views of one geometric operator? Learn the mathematics, code and practical design principles behind structured memory for trustworthy AI.

Discover how to unlock the full potential of Claude Opus 5 with a practical guide built on Anthropic's official documentation and proven best practices. From writing your first effective prompt to designing sophisticated agentic workflows and production-ready systems this book gives you the techniques, patterns and insights needed to achieve more accurate, reliable and consistent results.

Top-k is not relevance, retrieved text is not evidence and an LLM judging another LLM is not verification. Beyond “Chunk and Pray” shows how to build RAG that answers through a verified knowledge graph, preserves exact numbers, cites its sources and abstains when it cannot prove the answer.

Build AI agents that can see screens, reason about tasks and control desktop applications and web browsers. Starting with a simple prototype, you'll learn how to create reliable production-ready systems that do real work on a computer.

Move beyond agent demos and learn to engineer reliable AI systems with Gemini, ADK, and Antigravity. Build the WidgetWare SDR while mastering context, tools, skills, structured workflows, evidence-backed research, multi-agent coordination, human approval, evaluation, deployment, and controlled loops. Includes all future Early Access update

Unlock the full potential of Moonshot AI's groundbreaking Kimi K3 with practical prompting techniques, real-world examples and production-ready strategies. Whether you are building AI applications, conducting research or deploying autonomous agents, this guide helps you get better results from one of the world's most capable open models.

Ein Coding-Agent liefert in Sekunden makellosen Code, mittendrin eine Funktion, die es nie gab: Halluzinations-Lasagne. Claude Shannons verrauschter Kanal von 1948 erklärt, warum plausible Ausgaben nicht verlässlich sind, und wie ein geschärfter Sender, ein beherrschter Kanal und ein prüfender Empfänger daraus verlässliche Software machen.