A deterministic safety pipeline for therapeutic AI in elderly assisted living

Abstract

Abstract Over 54 million Americans are aged 65+, with depression affecting 25–49% and anxiety exceeding 30% of assisted living residents. AI systems employing agentic orchestration exhibit 0.5–2% failure rates—unacceptable where a single missed crisis can be fatal. We designed and bench-evaluated Lilo Engine, a 5-layer deterministic therapeutic pipeline replacing a prior multi-agent orchestrator. Safety is enforced through structural invariants: a Guardian layer with 4-gate OR crisis detection runs unconditionally on every input; a Reflector layer validates every output. Evaluated across 3,720 test scenarios, the system achieved 100% crisis recall (500/500 comprehensive scenarios), <5% false positive rate, and 28.7 ms detection latency—well within crisis response benchmarks. Intent classification reached 96.4% accuracy; generation quality 98.4%. The architecture reduced execution paths from 7+ to exactly 2, producing deterministic, HIPAA-auditable traces. Clinical validation with elderly populations is the essential next step.

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