6.2 Scope Conditions
MAGE has scope conditions because it imposes costs: models have to be built and maintained; controls consume compute and attention; synchronization can fail; every new mechanism adds to what the environment must carry. Those costs are justified only where the resulting structure buys something back.
The right unit of analysis is usually an obligation or engineering surface, not an industry label. The same scientific system may have highly governable build and reproducibility machinery around a kernel whose scientific validity remains a matter of expert judgment. A startup may have little reason to model its whole architecture while still needing a hard security boundary around payments. MAGE therefore applies unevenly even within one repository.
6.2.1 Return, Governability, and Authority
Does the investment earn a return? Recurrence is the usual source of return. If the same architectural knowledge, review judgment, or failure class will be encountered hundreds of times, externalizing it once can avoid purchasing the same reasoning repeatedly. But recurrence is not the only justification. A rare obligation with catastrophic consequences may warrant extensive modeling and assurance because preventing a single failure can repay the investment. The economic question is therefore broader: does the expected reduction in future reasoning, risk, or recovery cost justify the construction and carrying cost of the asset?
Can the obligation be governed adequately? Some properties can be represented precisely and settled mechanically: a schema conforms, a dependency is permitted, an invariant holds. Others admit statistical evidence but not a definitive predicate. Still others depend substantially on aesthetic, scientific, organizational, or social judgment. Governability is the degree to which an important obligation can be made explicit, supplied with relevant evidence, and evaluated with adequate reliability and acceptable cost. Deterministic checking is its strongest region, not its definition.
One organizational condition determines whether lessons become shared assets: authority. Someone who discovers a structural problem must have a functioning path to the architecture, models, controls, or shared infrastructure that could address it. Authority need not reside in one person. It has to connect diagnosis to the environment. Without that path, the organization can understand a failure perfectly and still patch it locally forever.
These are theoretical scope conditions informed by the observations in Part V, not population estimates.
6.2.2 Typical Profiles
MAGE's return varies by engineering surface within a system. Read Table 6.2-1 as a set of typical profiles rather than verdicts on whole domains: a long-lived embedded system and a two-week embedded prototype share a domain label but have very different economics, and a tiny startup may still owe one payment obligation a deterministic control.
| Setting | Why investment may pay | Strongly governable surfaces | Likely residual judgment |
|---|---|---|---|
| Large brownfield / services | repeated change and reconstruction | architecture, API contracts, security boundaries, tests, deployment | requirements and tradeoffs |
| Long-lived embedded | recurrence + assurance | timing, memory, protocols, interfaces | hardware/system integration |
| Safety-critical | consequence + recurrence | traceability, invariants, coverage, evidence | certification and acceptance |
| Greenfield | selective; increases with longevity | high-risk boundaries, build/test infrastructure | product discovery |
| HPC / scientific | long-lived estate | builds, reproducibility, numerical tolerances, provenance | scientific validity |
| ML systems | long-lived surrounding software | pipelines, evaluation infrastructure, serving, access | data/model fitness |
| Small startup | usually selective | consequential security/data obligations | product search and architecture in flux |
| One-off / throwaway | usually low reuse | only obligations whose consequence independently justifies it | most of the work |
The pattern is economic rather than categorical. Long-lived systems offer repeated opportunities to amortize representation and assurance. High-consequence systems can justify the same investment even at lower recurrence. Mixed domains divide along individual engineering surfaces: scientific software may support strong governance around reproducibility and deployment while leaving scientific validity with domain experts; ML systems can govern pipelines and serving infrastructure much more strongly than they can govern the question, "Is this learned model good?"
At the opposite extreme, do not build elaborate governance around work whose relevant reasoning will not recur and whose consequences do not justify the investment. Use the cheapest adequate mechanism. A prototype may need only tests around one dangerous boundary. If it becomes a long-lived estate, the economics change before the label does.
6.2.3 Two Ways an Investment Fails
MAGE becomes a bad bargain in two broad ways. First, the asset never earns a return: the knowledge is not reused, the risk is too small, or the machinery costs more to maintain than the judgment it replaces. Second, the important property resists adequate governance: it cannot be represented, evidenced, or evaluated strongly enough to justify moving authority out of human judgment.
Most real systems contain both kinds of surfaces. The practical question is therefore not whether a project "uses MAGE," but which properties deserve durable representation or authority, how strong the evidence should be, and which decisions should remain human.
The next chapter turns those boundaries into predictions that can be tested.