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A local presence model becomes more robust when complementary sensors update one time-bounded occupancy state instead of each sensor acting as final truth.

Tracking turns repeated sightings into one persistent target, so detector and classifier work can be reused or spaced out without treating every frame as new.

OpenTelemetry gives model, storage, database, and network work compatible span data so one slow local AI request can be attributed to its real bottleneck.

Distributed tracing preserves one request identity across services so synchronous, parallel, and queued spans can be reconstructed into one causal path.

Circuit breaking converts repeated tool failures into a bounded state transition, controlled fallback, and recovery probe instead of endless retries.

Capabilities bind authority to specific resources and rights, letting agent tasks receive only the permissions they need instead of ambient access.

Beam search keeps multiple transcript hypotheses alive, improving recovery from early mistakes while increasing decoder work and latency.

Diarization converts speech segments into speaker embeddings, clusters similar voices, and maps speaker labels back onto a private audio timeline.