Front Matter

The MAGE Method at a Glance

MAGE is one method with two theses and a conversion that grows an environment around them. To help you understand the full argument, Figure 0.1-1 shows the whole of it on a single page and how the parts relate to each other — the vocabulary this book builds, laid out in the order the method runs.

The MAGE method at a glance A single vertical flow. A coding agent — a reasoning engine paired with a printer — generates code through an agent fleet. The fleet branches into the book's two theses: the Modeling Thesis, which produces models (the Model Zoo), and the Alignment Thesis, which produces mechanisms (constraints, sensors, validators, gates). The two converge into the Governed Engineering Environment. A separate incoming arrow from the left feeds that environment from the start: the known constraints you already have are converted into governance up front, bootstrapping the environment before the loop runs. Then governance conversion grows that environment — turning each recurring failure into a mechanism — and the result is trustworthy software. Coding agent reasoning engine + printer generates code The agent fleet Modeling Thesis Models the Model Zoo Alignment Thesis Mechanisms constraints · sensors validators · gates bootstrap Known constraints The Governed Engineering Environment Governance conversion grows the environment Trustworthy software
Figure 0.1-1. The whole method on one page: a coding agent — a reasoning engine paired with a printer — generates code through a fleet, and the two theses fill a governed environment. That environment is bootstrapped up front from known constraints, then grown by governance conversion into trustworthy software.
© James C. Davis, 2026–present