AOHP让AI代理在安卓系统中像原生应用一样高效安全运行
AOHP: An Open-Source OS-Level Agent Harness for Personalized, Efficient and Secure Interaction

- 将AI代理作为系统级核心角色,重构界面与运行环境
- 任务完成率提升21.12%,令牌消耗降低51.55%
- 适合研究智能操作系统与个性化代理的开发者
AI代理正推动新型软件范式,能自主调用工具、提取信息、管理内存并跨应用完成任务。但现有终端操作系统以应用为中心,对代理支持不足,导致部署效率低且存在安全隐患。尽管代理原生操作系统概念兴起,研究社区仍缺乏开源测试平台。我们提出基于AOSP的AOHP(Android Open Harness Project),核心理念是将代理视为第一类系统主体,实现自适应界面与代理友好运行环境。在保留成熟安卓生态基础上,引入三项机制:个性化服务组合、高效代理接口、安全信息流。初步实验显示,在涵盖关键能力的复杂任务中,AOHP任务完成率提升21.12%,令牌消耗降低51.55%,安全策略合规性显著增强。
原文摘要 · Abstract (English)
AI agents are driving a new software paradigm, with the ability to autonomously call tools, extract information, manage memory, and complete tasks that span applications and data sources. Most existing end-user operating systems, however, are designed for application-centric workflows and offer little native support for AI agents. This mismatch limits the wider adoption of agents and leads to execution overhead and safety risks when running agents on conventional systems. While the concept of agent-native operating systems is emerging, the research community lacks an open testbed to explore the architectural primitives desired for agent-mediated interaction. We present AOHP (Android Open Harness Project), an OS-level agent harness built on the Android Open Source Project (AOSP). The core design principle of AOHP is to treat agents as first-class OS actors, enabling adaptive user interfaces and agent-friendly runtime environments. AOHP preserves the mature Android software and hardware ecosystem while introducing three agent-oriented system mechanisms: personalized service composition, efficient agent interfaces, and secure information flow. Based on preliminary experiments on challenging tasks covering key capabilities of OS agents, AOHP shows clear advantages in task completion (+21.12% completion rate), execution cost (-51.55% token cost), and security-policy compliance.
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