面向痴呆症护理的受治理AI智能体协调：架构、安全契约与证据驱动的工作流验证

Computer Science > Multiagent Systems arXiv:2609.25956 (cs) [Submitted on 22 Sep 2026] Title:Governed AI-Agent Coordination for Dementia Care: Architecture, Safety Contracts, and Evidence-Derived Workflow Verification Authors:Francesca Medda, Hui Gong View a PDF of the paper titled Governed AI-Agent Coordination for Dementia Care: Architecture, Safety Contracts, and Evidence-Derived Workflow Verification, by Francesca Medda and Hui Gong View PDF HTML (experimental) Abstract:Dementia care increasingly involves connected sensors, medication devices, electronic records, and assistive technologies. Interoperability can transport observations but cannot maintain an accountable care state, reconcile evidence, determine who may act, or verify resolution. The shift from large language models to agentic engineering creates a systems opportunity: an external runtime can maintain memory across episodes, plan over goals and constraints, invoke tools, observe outcomes, and enforce governance. This paper presents Governed Closed-loop Agent Coordination (GCAC), an architecture for bounded agent participation in community dementia-care workflows. Evidence on care-coordination failures and policy obligations is translated into traceable system requirements. GCAC separates observation, governed memory, planning, deterministic policy enforcement, execution, and outcome monitoring through a typed event-memory-decision-action-outcome contract. A reference harness evaluates 18 evidence-derived traces covering missing records, medication conflict, caregiver reports, service failure, consent change, stale state, duplicate events, untrusted text, and suspected acute neurological change. GCAC satisfies all 18 contract oracles with zero policy-violating tool calls and correctly preserves obligations, rejects stale state, creates human hand-offs, and records workflow closure. Event-threshold and stateless-planner controls satisfy 2/18 and 1/18 oracles, respectively. Component ablations localise failures to the removed memory, policy, or versioning function. The results establish architectural conformance rather than clinical effectiveness and show how agentic systems can automate reconciliation, routing, documentation, and follow-up while preserving human authority over consequential care decisions. Comments: Subjects: Multiagent Systems (cs.MA) Cite as: arXiv:2609.25956 [cs.MA] (or arXiv:2609.25956v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2609.25956 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hui Gong [view email] [v1] Tue, 22 Sep 2026 10:09:01 UTC (31 KB) Full-text links: Access Paper: View a PDF of the paper titled Governed AI-Agent Coordination for Dementia Care: Architecture, Safety Contracts, and Evidence-Derived Workflow Verification, by Francesca Medda and Hui Gong View PDF HTML (experimental) TeX Source view license Ancillary-file links: Ancillary files (details): ablation_results_by_trace.csv ablation_summary.csv baseline_results_by_trace.csv baseline_summary.csv gcac_harness.py trace_manifest.json Current browse context: cs.MA new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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