Book

Description

Teams of language-model agents are now easy to assemble. Knowing which team to build, and whether it works, is not. This book is a practitioner's guide to both questions. It begins with the building blocks and the common design patterns for agent teams (chains, routers, votes, orchestrators, refinement loops, debates and tool-using agents), with what each costs, when it pays off and how it fails. It then shows how to turn a written requirement into an architecture, follows a complete system from design to running code, and sets out the reliability engineering that makes agent systems dependable. For evaluation, the book introduces one lens: the accuracy of any system that generates candidate answers and returns one is exactly coverage, whether a correct answer is generated at all, times selection, whether it is returned. Coverage belongs to the generator and selection to the verifier, so two numbers show which part of a system to improve, before any engineering is spent. The book covers language-model judges, sound experimental design, measurement hazards, and the accounting of cost and latency. A detailed case study puts all of this to work: an automatically designed multi-agent framework compared with a single call to the same model, three corrections to automated search including the Sarkar influence gate, and a measurement of what each correction needs from a real system. The book closes with a decision guide and the practices for deploying and operating agent teams. For engineers, researchers and technical leads who build or evaluate systems of language-model agents.