Blueprint: local edge assistant

Objective

Serve common requests locally for low latency and data locality, while escalating only approved low-confidence cases to a cloud fallback. Adapt through signed specialist updates rather than in-place learning.

Topology

Edge packages: intent router, command specialist, local retrieval model, safety classifier, optional speech components.

Cloud packages: broad fallback model, centralized evaluator, registry, candidate factory, and release controller.

Request flow

FUNCTION edge_assistant(request)
    context <- CLASSIFY_DATA_AND_RISK(request)
    local_plan <- LOCAL_ROUTER_PLAN(request, context, device_budget)
    local_result <- RUN_LOCAL(local_plan)

    IF local_result.accepted
        RETURN local_result
    END IF

    IF NOT context.cloud_transfer_allowed
        RETURN SAFE_LOCAL_ABSTENTION
    END IF

    minimized <- MINIMIZE_FOR_ESCALATION(request, context)
    cloud_result <- RUN_APPROVED_CLOUD_FALLBACK(minimized)
    RETURN MERGE_WITH_LOCAL_CONTEXT(cloud_result, context)
END FUNCTION

Breeding loop

Aggregate privacy-preserving failure summaries, not raw personal conversations. Curate approved examples centrally, train candidate adapters or distilled specialists, evaluate on device classes, then sign and roll out progressively.

Contracts

Local specialists declare memory, latency, offline operation, language, hardware, data classes, and abstention behavior. The cloud fallback declares what minimized data it accepts and which jurisdictions it serves.

Key metrics

Local completion rate, escalation rate, p95 local latency, cloud bytes, battery or energy cost, calibration, offline success, update failure, rollback time, and quality by device tier.

Safety boundaries

• no background outbound network beyond the update and approved fallback service;
• no raw conversation upload for training by default;
• signed package updates with rollback cache;
• user-visible control for cloud escalation and memory;
• local models cannot modify update policy or install packages;
• device resource ceilings enforced by the host application.

Implementation stages

Start with static local-versus-cloud rules. Add a learned router only after enough labeled route evidence exists. Add local adapters only after package signing, rollback, and resource profiling work across target devices.