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Governed AI-Agent Coordination for Dementia Care: Architecture, Safety Contracts, and Evidence-Derived Workflow Verification
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arXiv:2609.25956v2 Announce Type: replace Abstract: Dementia care at home increasingly involves connected sensors, medication devices, electronic records, and assistive technologies. Professionals and family carers must still interpret these observations, reconstruct context, coordinate responses, and verify resolution. Sustaining safe care at home while ensuring timely clinical escalation requires continuity between these tasks. Agentic engineering creates a systems opportunity by combining model interpretation with persistent external memory, planning, authorised tool use, and outcome feedback. This paper presents Governed Closed-loop Agent Coordination (GCAC), an architecture for bounded agent participation in community dementia-care workflows. Evidence on coordination failures and policy obligations is translated into traceable system requirements. GCAC separates observation, four governed memory classes, planning, deterministic policy enforcement, execution, and outcome monitoring through a typed event-memory-decision-action-outcome contract. A worked medication-device example explains how these roles maintain context, allocate responsibility, and distinguish an unrecorded dose from a clinically established omission. A reference harness evaluates 18 evidence-derived traces spanning care gaps, service failure, consent change, stale state, duplicate events, untrusted text, and acute-change routing. GCAC satisfies all 18 contract oracles with zero policy-violating tool calls; event-threshold and stateless-planner controls satisfy 2/18 and 1/18, respectively. Six ablations identify the contribution of memory, policy, and versioning. The results establish architectural conformance. Effects on hospital use, quality of life, staff workload, unpaid care, and net costs require prospective evaluation. GCAC provides a testable basis for accountable coordination that preserves human authority over consequential care decisions.
Source:https://arxiv.org/abs/2609.25956PreviousCan Agents Adapt Their Institutions And Sacrifice Themselves During Collapse? Executable Self-Governance in a GovSim CommonsNextTowards Strategy-Level RSI for Skill-Augmented Agents: Learning When to Reuse Skills from Execution Feedback
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