G.6 A Practical Adoption Procedure

The preceding argument can be turned into a procedure. It is deliberately organized around one engineering surface at a time rather than around an enterprise-wide maturity level.

1. Choose a recurring unit of work. Start where GenAI can already perform useful work and where the organization can observe the result. Identify the artifact or system being changed, the outcome expected, and the boundary of the work unit.
2. Write down the consequential obligations. Ask what must remain true if this work is delegated. Include functional requirements, architectural boundaries, security and privacy rules, operational limits, provenance requirements, and qualitative obligations that matter to acceptance. Do not require every obligation to be mechanically decidable.
3. Identify what the reasoner currently has to reconstruct. Record the system facts, organizational knowledge, prior decisions, and relationships repeatedly rediscovered from source, documents, or people. Externalize the consequential recurring parts into representations appropriate to the questions being asked.
4. Separate evidence from production. Determine how the organization can establish important properties without relying solely on the producing agent's account of its own work. Reuse existing tests, analyses, measurements, simulations, and review procedures where they are adequate; add evidence where important claims remain unsupported.
5. Assign enforcement to selected obligations. Decide which properties should prevent an action, reject an artifact, require additional evidence, or escalate the work. Use the weakest mechanism that provides adequate assurance for the consequence at stake. Preserve expert judgment where no adequate evaluator exists rather than disguising a heuristic as a proof.
6. Delegate within the resulting boundary. Give the agent the work unit, relevant models, permitted actions, and acceptance path. Record enough provenance to reconstruct what happened and why. Keep escalation available for situations the environment does not adequately cover.
7. Study interventions and failures. When a person corrects the work, ask whether the correction is local or reveals missing organizational structure. Repeated reconstruction suggests a missing model; repeated violation suggests missing enforcement; repeated disagreement with a checker suggests an inadequate evaluator; repeated escalation may indicate either an appropriate judgment boundary or an opportunity for better support.
8. Convert recurring lessons into engineering capital. Update the representation, evidence, mechanism, procedure, or skill that would let later work inherit the lesson. Check that the new asset has a consumer and that its expected value justifies its carrying cost.
9. Expand delegation where the evidence supports it. Increase the scope or consequence of delegated work only where the surrounding environment provides an adequate basis for doing so. Evaluate individual engineering surfaces rather than declaring the product or organization autonomous.
10. Revisit responsibility and resilience as the boundary moves. Determine who remains answerable for consequential decisions, what competence they need to exercise that responsibility, and which capabilities the organization must retain if its preferred machine intelligence is unavailable or inappropriate.

This procedure can begin with one team and one workflow. The first useful result may be modest: a dependency rule that stops being rediscovered, a test that becomes an admission gate, a provenance record that makes an automated change inspectable, or a recurring review criterion that becomes a structured rubric. The relevant measure of progress is not how many tasks have been handed to an agent. It is whether the organization can delegate useful work while retaining a sound account of what the work must satisfy, how those obligations are evaluated, and where authority over the result resides.

As capability improves, that account becomes more rather than less important. Some work that currently requires direct expert judgment will acquire better evaluators; artificial experts may become trustworthy for judgments that organizations currently reserve for people; and cheap realization will make still larger spaces of alternatives practical to explore. Other obligations will remain contextual, contested, or expensive to evaluate. Adoption therefore has no fixed endpoint at which the organization has "automated engineering." It is an ongoing allocation of reasoning and authority among people, artificial reasoners, and engineered mechanisms, revised as their relative capabilities and costs change.

The practical objective is correspondingly narrower than maximum autonomy. An organization should delegate work when doing so creates value and when the surrounding engineering provides adequate knowledge, evidence, enforcement, and accountability for the consequences involved. MAGE supplies structures for making those conditions explicit and for improving them through use. The result is an organization that can take advantage of increasingly cheap intelligence without requiring either that people inspect everything it produces or that the organization simply trust what the intelligence decides.