Harness Engineering

Build MCP servers that are ready for real production. This hands on guide shows you how to design, secure, deploy and operate stateless Model Context Protocol servers that scale with confidence. Packed with practical examples and proven patterns, it gives you the skills to build reliable AI infrastructure from the ground up.

vLLM and SGLang, side by side. Learn how LLM serving works, measure both engines fairly, tune them with evidence, and take a model from an empty GPU to a deployment with a cost per million tokens you can defend.

Your AI coding agent starts every session knowing nothing about your project, so it guesses - and the guess is sometimes expensive. The AGENTS.md Cookbook shows you how to write short, specific rules files, how to test them with a simple before-and-after method, and includes twelve ready-to-adapt recipes for real project types, labeled honestly about what has and has not been tested.

Building AI agents is one thing. Making them reliable in production is another. This book explores why agents fail and how to design systems that hold up under real-world conditions. From architecture and engineering patterns to evaluation and operations, it gives technical leaders the tools to build agents they can trust.

Build agents that write code, fix tests, and commit changes while you sleep — safely, cheaply, and entirely on your own hardware (622 manuscript pages).

Meta Muse changes the way you work with AI. This practical guide shows you how to write better prompts, build reliable workflows, automate everyday tasks and avoid common mistakes. Whether you're new to agentic AI or already putting it to work, you'll learn how to get more done with less friction and more confidence.

Build Smarter Chatbots and Voice AssistantsExplore Conversational AI from fundamentals to real-world applications. Learn NLP, intent and context management, intelligent chatbots, voice assistants, speech technologies, multimodal interfaces, APIs, deployment, business integration, analytics, ethics, LLMs, and Generative AI.

Go beyond basic prompting and learn how to get reliable, high-quality results from Claude Opus 5.5. This practical guide shows you how to structure instructions, manage context, break down complex work and verify outputs across coding, research, analysis, writing and automation.

GPT-6 Astra changes the way prompting works. This practical guide explains what makes it different, why old techniques can fail and how to write prompts that deliver reliable results. Learn how to build, refine and troubleshoot prompts while finding the right balance between control and autonomy.

Build AI agents that actually hold up in production. This practical guide covers agent architecture, tool use, multi-agent systems, security, reliability, observability and operations, with working code throughout. Learn how to build agents that are dependable, cost-aware and ready for the real world.

AI agents are moving from experiments into real production systems. This book shows experienced engineers how to design, deploy and operate reliable agent systems that can scale. From architecture and infrastructure to security, reliability and governance, it provides practical patterns and production-ready examples for building autonomous systems that work in the real world.

Coding agents reconstruct your domain from schemas and code, and nothing on disk disagrees with them. This book gives them something that does: an ORM 2 conceptual schema that verbalizes into checkable English. 18 chapters, 61 diagrams, 38 working models, and a promise graph for multi-agent work.

Build AI that keeps working when the cloud doesn't. Local-First AI Engineering shows you how to run capable, private AI systems on infrastructure you control. From hardware and inference to RAG, agents, security and production ops, you'll learn how to build local AI that is fast, reliable and truly yours.

Move beyond AI demos and build agents that can actually run IT operations. This practical guide takes you from MCP fundamentals to production-ready monitoring, incident response, remediation, Kubernetes operations, security and multi-agent systems, with complete runnable code and a strong focus on safe autonomy.

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.