An artificial (synthetic) intelligence can enjoy no more complex domain of sensory input than that provided by instantiation within an autonomous vehicular device. In order to embody an artificial intelligence (a synthetic agency) within a vehicular device (a host), the system designer must be prepared to integrate an array of mechanical components together with computational hardware, mission-specific behavioral heuristics, and autonomous control algorithms. Systems whose requirements specify adaptive, stimulus generalized, and purposive sensorimotor behavior may be a lesser challenge to design, if a degree of non-deterministic system behavior is allowed. However, the opposite is true for military systems that will require demonstration of not only deterministic concrete and even formal reasoning behavior but also retain positive control over an array of lethal and survival-motivated sensorimotor behaviors. Such "cognate" systems will have an implicit need for mechanisms that map raw sensation onto dissimilar cognitive sub-processing elements. Common to these systems will be the problem that the mechanism(s) they employ to map raw sensation onto cognitive sub-processes will be plagued by inherent representational, scope limited, and frame of reference offset errors. Further, all of these problems will be exacerbated in systems where specifications require evidence of agent learning as a deliverable. Obviously, these are not trivial problems and their solution will require methods derived from a variety of sources. In this essay, the author describes a focused array of principles taken from cybernetics and biogenic-psychology, a controversial view of intelligence, and a Turing algorithm history misplaced. Taken together, the author has found the principles useful in creating models of synthetic agency and host interface technology.