Mastering AI Agent Architectures: Analysis Foundations

Before you choose an agent architecture, learn to measure one.

Most arguments about multi-agent AI systems are arguments about taste. This book replaces them with measurement. You will rebuild one workload three ways, classify agent systems by control, state and flow, and build the Gauntlet — three fixed workloads (research, transactions, monitoring) scored on eight metrics, including the failure mode that hurts most in practice: plausible wrongness, answers that look right and are not.

You will learn to:

Write quality-attribute scenarios for agent systems and test designs against themModel an agent fleet as a queue and predict latency before you deployPrice a design in tokens, calls, latency and peopleRun a mini-ATAM review and write a decision card as conditions, not opinionsFreeze a baseline behind a lock so every later comparison is fairEach chapter moves from Practice (a lab with runnable code) to Insight to Theory, and closes with a scorecard, a decision card, exercises and a self-test. Every figure and table is generated from the code at tag gauntlet-b1; no API key is needed.

Book 1 of 7 in Mastering AI Agent Architectures — The Styles Series. Start here: every later book is measured against this baseline.