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Findings support the feasibility of bounded, database-grounded, and traceable agent orchestration for non-diagnostic skincare assistance, but do not establish clinical readiness, external generalization, formal privacy guarantees, or universal safety.
A controlled, safety-oriented evaluation using a multi-task one-dimensional convolutional neural network and a structurally non-IID partition of the Commercial Modular Aero-Propulsion System Simulation benchmark reveals a trade-off between robust update selection and collaborative representation learning.
A four-dimensional Integration Friction Index is introduced that separates one-time engineering cost from recurring organisational, legal, and maintenance cost and shows why scope and budget enforcement cannot be delegated to system prompts.
The findings show that network-operation agents need execution-time authorization boundaries alongside prompt-level instruction hygiene, and that network-operation agents need execution-time authorization boundaries alongside prompt-level instruction hygiene.
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