The critical technical step for Mister M was not simply calling a language model. It was defining everything the application must decide before and after generation.
The Sensei Engine work added compact context retrieval from local Path memory, intent and response-mode classification, safety prechecks, structured prompt assembly, response validation, and persistence rules. The first orchestrator deliberately used no fake reply. When generation was unavailable, the product preserved the user's message and stated the boundary instead of substituting a motivational template.
The next phase connected that architecture to Apple Foundation Models. A client adapter checks system availability, a serializer turns the structured packet into bounded input, and the engine validates the candidate. The conversation stores an assistant message only after the response has acceptable on-device provenance and passes display and persistence policy.
The application remembers, selects context, applies policy, and owns persistence. The model supplies candidate language. Automated tests cover this pipeline extensively, while live response quality on supported hardware remains a manual validation question.
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