Название: Harness Engineering: Optimization, Coordination, and Adaptive Agentic Systems (Early Release)
Автор: Nicole Koenigstein
Издательство: O’Reilly Media, Inc.
Год: 2026-08-31
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

For engineers who already build and deploy AI agents, the next challenge is engineering the system around the model, not just making the model more capable. Harness Engineering shows you how to treat your harness as the operational intelligence and reliability layer of your agent system, governing what the model perceives, executes, retains, and improves over time.

AI researcher and practitioner Nicole Koenigstein provides a practical methodology for turning scattered implementation details into a unified, inspectable architecture. You'll learn how to optimize operational signals to improve system resilience without altering the underlying model. Ultimately, this book offers a strategic framework for building adaptive agent systems that know what should be explicitly engineered, what to internalize, and what to let the harness learn.

This book is for readers who want to go beyond model quality and focus on engineering the reliability infrastructure around AI agents. I assume that you already have a basic understanding of LLM-powered agents and the common patterns used to build them. You should be familiar with calling a language model from code and with concepts such as tool calling, stateful workflows, memory, orchestration, and basic multi-agent patterns. I also assume that you are comfortable reading and modifying Python code and understand the practical concerns involved in moving an agent from a prototype toward production. You should also have a working understanding of LLMs, including how they are called, prompted, and integrated into applications.

Since the focus of this book remains on engineering the surrounding system rather than training the underlying model from scratch, you don’t need an advanced mathematical background. Where math becomes relevant, for instance in Reinforcement Learning or optimization, I will introduce the concepts needed to understand how they apply to the harness, together with practical implementations.