Agentic Engineering

Software development is changing fast, and Claude Code is at the center of that shift. Learn how to work effectively with AI agents to write code, automate workflows, and build larger projects with confidence. From setup and prompt design to real-world engineering practices, this book provides a practical guide to modern software development in 2026.

Scrum made human teams 10× faster. Now it's doing the same for AI agents. Here's the architecture.

A leader at a company opens Slack one morning. The brief their virtual employee wrote looks normal: same headers, same prose voice, same cadence as every other brief that week. The last line ends mid-sentence. The API returned `stop_reason: "max_tokens"`. The system shipped it anyway. No exception. No log line. No retry. That bug doesn't look like the normal kind. This book is about why, and what to build instead.

An LLM is not an AI system.Systems Thinking for Agentic AI shows software engineers and architects how to design reliable AI applications with prompts, RAG, tools, memory, orchestration, guardrails, evaluation, observability, and runtime control.Move beyond chatbot demos and learn how to build production-ready agentic AI systems you can reason about, measure, debug, operate, and improve

Reverse engineering gets a powerful upgrade with Claude Code. Learn how to investigate, understand and reconstruct software you’re authorized to analyze, while keeping evidence, accuracy and reproducibility at the center. From legacy systems to modern codebases, this book turns AI into a practical partner for serious software analysis.

AI can write code fast, but getting reliable software still starts with clear thinking. This book shows how to turn ideas into precise specifications, then use AI assistants and autonomous agents to build, test and maintain real-world software. Follow a complete project from business problem to production and learn a practical approach to AI-assisted development.

A two-part study guide written against the Claude Certified Architect blueprints, Foundations (CCAR-F) and Professional (CCAR-P). Organised domain-by-domain and weighted to match each exam, it teaches the architectural judgement and anti-pattern recognition the exams reward, not a feature tour, across Claude Code, the Agent SDK, the Claude API, and MCP.

This book teaches harness engineering as a discipline. Not magic prompts. Not vendor tricks. Engineering practice applied to a new substrate.

Die Softwareentwicklung wandelt sich rasant, und Claude Code steht im Zentrum dieses Wandels. „Mastering Claude Code“ zeigt, wie Entwickler mit KI-Agenten Code schreiben, Arbeitsabläufe automatisieren und auch komplexe Projekte effizient umsetzen. Praxisnah vermittelt das Buch Einrichtung, Prompt-Design und bewährte Engineering-Praktiken für moderne Softwareentwicklung im Jahr 2026.

Build AI agents that do more than generate text. This book shows you how to use the Claude Agent SDK to create autonomous systems that use tools, manage context, coordinate multiple agents, and solve complex tasks. With practical examples in Python and TypeScript, you will learn how to build secure, scalable agents ready for production.

You watched a language model do half an hour of work on its own. This book is about the question that came next: how much of it can you trust?

A practical, code-first guide to building production-ready AI agents and multi-agent systems in C# with Microsoft Agent Framework, Microsoft.Extensions.AI, tools, context, orchestration, observability, workflows, and enterprise-ready patterns.

The CCAR-P exam tests judgment, not trivia. This scenario-based guide covers all seven domains, from solution design and RAG to governance and stakeholder communication, with 62 sketchnote figures, chapter quizzes, a distractor appendix that explains every wrong answer, and a full 63-question practice exam at the official weighting.

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.

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.