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将新的物理设备集成到 AI 代理生态系统中，通常需要大量且繁琐的特定平台工程，包括驱动选择、依赖管理、接口设计和定制部署流水线。本文介绍了一种名为 Octopus 的全新硬件接入框架，它用动态编码代理运行时取代了静态集成。

Octopus 利用“提示词即基础设施”（Infrastructure-as-Prompts）的概念，仅需一条引导命令（配合 Shell 访问权限和模型 API 密钥），即可驱动代理完成五阶段流水线：枚举操作系统可见的硬件、推断设备身份与功能、自动生成带类型的模型上下文协议（MCP）工具及其底层硬件交互代码、将其激活为实时端点，并通过持久化守护进程自动修复特定类别的故障。实验表明，该框架在跨架构、跨操作系统的多台主机上实现了零手写集成代码的即时功能接口生成，为 AI 硬件控制展示了一条极具前景的自动化路径。

Octopus Protocol: One-Shot Hardware Discovery and Control for AI Agents via Infrastructure-as-Prompts

Octopus Protocol: One-Shot Hardware Discovery and Control for AI Agents via Infrastructure-as-Prompts

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  • arXiv ID: arXiv:2605.09055 [cs.RO]
  • Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
  • Authors: Quilee Simeon, Justin M. Wei, Yile Fan
  • Submitted: May 9, 2026; Last Revised: September 4, 2026 (v2)
  • DOI: 10.48550/arXiv.2605.09055
  • arXiv ID: arXiv:2605.09055 [cs.RO]
  • Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
  • Authors: Quilee Simeon, Justin M. Wei, Yile Fan
  • Submitted: May 9, 2026; Last Revised: September 4, 2026 (v2)
  • DOI: 10.48550/arXiv.2605.09055

Summary

Summary

将新的物理设备集成到 AI 代理生态系统中，通常需要密集的、特定于平台的工程设计——包括驱动程序选择、依赖项管理、接口设计和自定义部署流水线。

Integrating new physical devices into AI agent ecosystems typically requires intensive, platform-specific engineering—including driver selection, dependency management, interface design, and custom deployment pipelines.

Octopus 是一个新颖的硬件接入框架，它用动态编码代理运行时取代了静态集成。通过利用“提示词即基础设施”（Infrastructure-as-Prompts），单条引导命令（在具备 Shell 访问权限和模型 API 密钥的情况下）可引导代理通过稳健的流水线，实现以下功能： 1. 枚举操作系统可见的硬件。 2. 推断设备身份和功能。 3. 自动生成带类型的模型上下文协议（MCP）工具及其底层硬件交互代码。 4. 将设置激活为实时端点。 5. 通过能够自主修复定义明确的故障类别的持久化守护进程来维护部署。

Octopus is a novel hardware onboarding framework that replaces static integrations with a dynamic coding agent runtime. By leveraging "Infrastructure-as-Prompts," a single bootstrap command (given shell access and a model API key) guides the agent through a robust pipeline that: 1. Enumerates operating-system-visible hardware. 2. Infers device identities and capabilities. 3. Automatically generates typed Model Context Protocol (MCP) tools along with their underlying hardware-facing code. 4. Activates the setup as a live endpoint. 5. Maintains the deployment via a persistent daemon capable of autonomously repairing defined classes of failures.

Key Results

  • 跨平台兼容性： 在横跨两种处理器架构、三个操作系统系列和两个设备访问路径的四台不同主机上进行了测试。相同的散文规格说明即可即时生成功能性接口，无需逐台主机进行编辑或编写手写集成代码。
  • 可靠性： 在参考主机上的连续五次运行均在第一次尝试时端到端成功完成。
  • 观察到的局限性： 论文记录了在连续运行十一小时的无人值守持续修复循环中观察到的操作能力和特定故障模式。

Key Results

  • Cross-Platform Compatibility: Tested across four distinct hosts spanning two processor architectures, three operating system families, and two device-access paths. Identical prose specifications produced functional interfaces instantly without requiring per-host edits or hand-written integration code.
  • Reliability: Five consecutive runs on the reference host completed end-to-end on the first attempt.
  • Observed Limitations: The paper documents both operational capabilities and specific failure modes observed during unattended, continuous repair loops over an eleven-hour period.

Abstract

Abstract

将以前未集成的设备纳入 AI 代理的控制之下，仍然需要进行设备特定的工程设计：驱动程序选择、依赖项解析、接口设计和部署，并且每个设备和每个平台都需要重复这些过程。我们提出了 Octopus，这是一种硬件接入框架，其中编码代理（而不是预先打包的集成）作为运行时来生成所需的infrastructure。给定 shell 访问权限和模型 API 密钥后，单条引导命令即可驱动代理完成五阶段流水线：枚举操作系统可见的硬件、推断设备身份和功能、生成带类型的模型上下文协议工具及其背后的硬件交互代码，并将结果激活为实时端点。随后，持久化守护进程会维护该结果，在其生成的部署中修复定义明确的故障类别。在横跨两种处理器架构、三个操作系统系列和两个设备访问路径的四台主机上，相同的散文规格说明无需任何逐机编辑和手写集成代码即可生成工作接口。在参考主机上的五次连续运行在第一次尝试时即端到端完成。我们报告了由此产生的各项能力，以及在连续运行十一小时的无人值守修复循环中所观察到的一种故障模式。

Bringing a previously unintegrated device under the control of an AI agent still requires device-specific engineering: driver selection, dependency resolution, interface design, and deployment, repeated per device and per platform. We present Octopus, a hardware onboarding framework in which a coding agent, rather than a shipped integration, is the runtime that produces the required infrastructure. Given shell access and a model API key, a single bootstrap command drives the agent through a five-stage pipeline that enumerates operating-system-visible hardware, infers device identity and capabilities, generates typed Model Context Protocol tools and the hardware-facing code behind them, and activates the result as a live endpoint. A persistent daemon then maintains the result, repairing defined classes of failure in the deployment it produced. Across four hosts spanning two processor architectures, three operating system families, and two device-access paths, identical prose specifications produced working interfaces with no per-host edits and no hand-written integration code. Five consecutive runs on the reference host completed end to end on first attempt. We report both the resulting capability and a failure mode of unattended repair loops observed over eleven hours of continuous operation.