Summary

Commodity intelligence changes the software-engineering problem because implementation capacity can grow faster than the engineering judgment required to direct and evaluate it. The relevant unit is therefore not the agent alone, but the larger engineered system through which autonomous work is represented, constrained, observed, evaluated, and accepted.

Two problems follow. The reasoning-horizon problem concerns what the reasoner can know and reconstruct while performing a task. Important system knowledge left implicit in implementation must repeatedly be recovered through finite and fallible reasoning. The probabilistic problem concerns consequential judgments left to probabilistic realization. Even a highly reliable decision can eventually fail when it is repeated often enough.

MAGE addresses these problems through two complementary moves. Modeling externalizes selected engineering knowledge and intent into representations suited to the questions engineers and agents must answer. Alignment enforces selected obligations through mechanisms that can constrain actions, produce and evaluate evidence, and control consequences.

Neither move eliminates engineering judgment. Models preserve selected distinctions rather than representing everything, and not every obligation can or should be mechanically enforced. The engineering task is to decide what should be explicit, what should bind realization, what should remain open, and what evidence is sufficient for the consequence at stake.

Parts II and III develop those two problems separately. Part II asks what should be modeled. Part III asks how selected engineering obligations come to be enforced.