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.

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

Learn how to build, run, and optimize llama.cpp from the ground up. This book covers everything from compiling the code and working with GGUF models to deploying fast, production-ready local LLM inference.

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.

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.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

Learn how modern LLM inference engines work by building one from scratch in Rust. From transformers and tokenization to KV caching, quantization, batching, and GPU optimization, this book combines theory, hands-on code, and performance engineering to help you create fast, production-ready AI systems.

Your C# skills are worth more today than they were a year ago — if you know how to put a language model in the loop. This book shows you how, with the Microsoft Agent Framework: real tools, RAG, multi-agent orchestration, plus the hosting, observability, and safety that separate a demo from a system you ship. Nineteen chapters. 120 runnable projects. No Python detours. Just C# and .NET 10.

Stop prompting your AI. Start engineering the loop. The best engineers no longer babysit agents — they design systems that discover work, verify it, and run while they sleep. Loop Engineering is the complete guide: goals, verification, memory, scheduling, and orchestration with Claude Code — plus four end-to-end builds. Wake up to finished work, not chat transcripts.

Most AI systems can talk, but few can actually do. This book shows you how to build AI agents that reliably use tools, call APIs and automate real workflows. Using DSPy, Pydantic AI, the Claude Agent SDK, the OpenAI Agents SDK and Google ADK, you'll learn practical patterns for building reliable agents that work in production.

Learn how to build intelligent, production-ready AI agents with the OpenAI Agents SDK. Through practical Python examples and step by step guidance, you'll master everything from the fundamentals to advanced multi-agent workflows, tools, and deployment.

Learn how to build reliable AI agents with PydanticAI, from simple chatbots to production-ready multi-agent systems. With practical examples, clear explanations, and hands-on projects, this book helps you write AI applications that are structured, testable, and easy to maintain.

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.

Local Intelligence shows you how to run large language models entirely on your Mac with Apple Silicon. Learn to use tools like Ollama, MLX, and llama.cpp, understand quantization, and build real local AI applications with open-source code.

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 65 sketchnote figures, chapter quizzes, a distractor appendix that explains every wrong answer, and a full 63-question practice exam at the official weighting.