Books

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

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.

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.

Learn to build production-ready LLM applications with DSPy through hands-on tutorials, complete runnable examples, and real-world projects. Master DSPy's core abstractions and create AI systems that improve with data instead of endless prompt tweaking.

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 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.

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.

AI agents don’t fail because they forget everything. They fail because they remember badly. This book shows you how to engineer memory that stays accurate, efficient and useful over time. From SQLite and PostgreSQL to vector indexes and multi-agent systems, you’ll learn what it takes to build agents that can run for days without losing the plot.

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.

Master Codex CLI from your first prompt to production-ready AI workflows. Learn how to automate coding, streamline development, and use OpenAI's terminal-native coding agent with confidence.

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.

The hardest part of building AI is not the model. It is everything around it. The Art of Harness Engineering is a practical guide to turning AI prototypes into reliable products. It covers testing, guardrails, observability and governance, giving you the tools to build AI systems people can trust and organizations can run with confidence.

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.

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.

Network traffic can look like noise until you know what to look for. This practical guide shows you how to investigate unfamiliar protocols, analyze packet captures and build reliable workflows with Claude Code. Learn to reverse engineer traffic, test your assumptions and turn raw network data into findings you can trust.