When OpenClaw Meets Hospital: Toward an Agentic Operating System for Dynamic Clinical Workflows

TL;DR

It is argued that the design in OpenClaw, an open-source autonomous agent framework that structures agent capabilities as a curated library of discrete skills, and extends it with the infrastructure-level constraints required for safe clinical deployment, forms the basis of an Agentic Operating System for Hospital.

Abstract

Large language model (LLM) agents extend conventional generative models by integrating reasoning, tool invocation, and persistent memory. Recent studies suggest that such agents may significantly improve clinical workflows by automating documentation, coordinating care processes, and assisting medical decision making. However, despite rapid progress, deploying autonomous agents in healthcare environments remains difficult due to reliability limitations, security risks, and insufficient long-term memory mechanisms. This work proposes an architecture that adapts LLM agents for hospital environments. The design introduces four core components: a restricted execution environment inspired by Linux multi-user systems, a document-centric interaction paradigm connecting patient and clinician agents, a page-indexed memory architecture designed for long-term clinical context management, and a curated medical skills library enabling ad-hoc composition of clinical task sequences. Rather than granting agents unrestricted system access, the architecture constrains actions through predefined skill interfaces and resource isolation. We argue that such a system forms the basis of an Agentic Operating System for Hospital, a computing layer capable of coordinating clinical workflows while maintaining safety, transparency, and auditability. This work grounds the design in OpenClaw, an open-source autonomous agent framework that structures agent capabilities as a curated library of discrete skills, and extends it with the infrastructure-level constraints required for safe clinical deployment.

Figures (3)

• Figure 1: Architecture of the Agentic Operating System for Hospital. The system comprises three layers. (Agent Layer) Each participant role---patient, clinician, triage, specialist---maps to a dedicated agent process executing within an OS-enforced isolated namespace; agents interact with the environment solely through two operations: invoking pre-audited medical skills and reading or writing shared clinical documents. (Interface Layer) The Medical Skills Library exposes a curated set of statically typed workflow modules; the Document Store maintains per-role document collections linked by an append-only mutation event stream dispatched by the Event Broker. (OS Enforcement Layer) Linux user isolation, file permission bits, seccomp filters, AppArmor policies, and the auditd/inotify subsystem collectively enforce all access boundaries at the kernel level, independent of model behaviour. Critically, no agent communicates with another through a direct channel; all inter-agent coordination is realised exclusively through document writes and event notifications.
• Figure 2: Page-indexed memory architecture and progressive disclosure retrieval. (Left) The memory lifecycle consists of three stages: Capture (observations are written as structured pages), Store (pages are consolidated into a rooted document hierarchy with a manifest file at each internal node), and Recall (the agent navigates the hierarchy through successive manifest reads). (Right) The document tree is organised by patient, care episode, and document group; each internal node carries a manifest (badge M) listing its children with natural-language scope descriptions, while leaf nodes contain primary clinical content. Given a query, the agent issues an LLM-Select call at each level to identify relevant subtrees (highlighted in green) and prune irrelevant branches without reading them (shown in grey, labelled "pruned"). This design requires no vector embeddings; retrieval accuracy depends solely on the agent's language-reasoning capability applied to manifest text, and the sequence of manifests consulted constitutes an inherently interpretable access trace.
• Figure 3: Clinical application scenarios enabled by the Agentic Operating System for Hospital. The nine-panel grid illustrates representative use cases realised by the proposed architecture. The central panel summarises the four core mechanisms shared across all scenarios: least-privilege execution, document-mutation coordination, manifest-guided memory retrieval, and ad-hoc skill composition. The surrounding eight panels cover scheduled care (Continuous Monitoring, Medication Management), reactive workflows (Triage, Emergency Escalation), longitudinal analysis (Proactive Risk Identification, Population Health), multi-agent coordination (Cross-Specialty Coordination), and infrastructure (Safety, Audit & Governance). Each scenario is realised entirely through the composition of medical skills, document writes, event subscriptions, and manifest navigation---without any bespoke architectural extension. The diversity of use cases demonstrates that a single unified infrastructure layer is sufficient to address both routine and exceptional clinical needs across the full spectrum of hospital workflows.