Google and industry partners have announced the Agentic Resource Discovery (ARD) Specification, an open standard designed to enable AI agents to publish, discover, and verify external tools, APIs, and services across organizational boundaries.
谷歌与行业合作伙伴宣布了代理资源发现（ARD）规范，这是一项开放标准，旨在让AI代理能够跨组织边界发布、发现和验证外部工具、API和服务。
The specification addresses a growing gap in AI agent infrastructure, where capabilities are widely distributed but lack a common discovery and trust mechanism.
该规范填补了AI代理基础设施中日益扩大的空白——能力广泛分布，但缺乏统一的发现和信任机制。
While protocols such as the Model Context Protocol (MCP) define how an AI agent invokes a tool, ARD addresses an earlier stage in the lifecycle: how agents discover those tools in the first place.
尽管模型上下文协议（MCP）等协议定义了AI代理如何调用工具，但ARD处理的是生命周期的更早阶段：代理最初如何发现这些工具。
Rather than replacing existing standards, ARD is designed as a complementary discovery layer that works across frameworks and providers.
ARD并非取代现有标准，而是设计为跨框架和提供商的补充性发现层。
Srinivas Krishnan, Distinguished Engineer at Google Cloud, highlights the motivation for ARD:
谷歌云杰出工程师Srinivas Krishnan强调了ARD的动机：
The problem is simple to state and hard to solve, especially in the enterprise, where the answer can't just be ‘find something that works.’ It has to be governed, with security and identity built in rather than bolted on.
问题表述简单但解决困难，尤其是在企业中，答案不能只是‘找到可行的东西’。它必须受到治理，安全性和身份要内建而非外挂。
The specification introduces two core constructs: catalogs and registries.
该规范引入了两个核心结构：目录和注册表。
Organizations publish a machine-readable ai-catalog.json file within their domain that describes available capabilities such as tools, APIs, skills, and agent endpoints.
组织在其域内发布一个机器可读的 ai-catalog.json 文件，描述可用的能力，如工具、API、技能和代理端点。
Registries aggregate these catalogs and enable agents to search based on task intent instead of relying on hardcoded integrations or static endpoint lists.
注册表汇总这些目录，使代理能够基于任务意图进行搜索，而不是依赖硬编码的集成或静态端点列表。
This allows agents to locate relevant resources across organizational boundaries while remaining compatible with execution standards such as MCP and OpenAPI.
这使得代理能够跨组织边界定位相关资源，同时保持与MCP和OpenAPI等执行标准的兼容性。
Trust and verification are central to the design.
信任和验证是设计的核心。
ARD includes domain-based ownership and verification mechanisms so agents can validate the authenticity of discovered resources before establishing connections.
ARD包含基于域的所有权和验证机制，使代理在建立连接前能够验证所发现资源的真实性。
This is intended to reduce risk in environments where autonomous agents may trigger actions across third-party services and enterprise systems.
这旨在降低自主代理可能跨第三方服务和企业系统触发操作的环境中的风险。
From Reddit community discussions, one perspective highlights the value of standardization:
来自Reddit社区的讨论中，一种观点强调了标准化的价值：
A uniform baseline protocol makes it easier to build alternatives without needing to interpret many different proprietary documentation formats.
统一的基线协议使得构建替代方案更加容易，无需解读许多不同的专有文档格式。
However, discussions also point out that the effectiveness of such systems will depend on the quality of exposed tools and the access or pricing models associated with them.
然而，讨论也指出，此类系统的有效性将取决于所暴露工具的质量以及与之相关的访问或定价模式。
The specification was developed with contributions from Microsoft, GitHub, Hugging Face, Cisco, Databricks, GoDaddy, NVIDIA, Salesforce, ServiceNow, and Snowflake.
该规范由微软、GitHub、Hugging Face、思科、Databricks、GoDaddy、英伟达、Salesforce、ServiceNow和Snowflake等公司共同贡献开发。
Early implementations have already emerged, including GitHub’s Agent Finder in Copilot and Hugging Face’s Discover Tool, both leveraging ARD for runtime capability discovery.
早期实现已经出现，包括GitHub Copilot中的Agent Finder和Hugging Face的Discover Tool，两者都利用ARD进行运行时能力发现。
Jennifer Marsman, Principal Engineer in AI at Microsoft, notes:
微软人工智能首席工程师Jennifer Marsman指出：
The goal isn’t a single global catalog of every resource. There will be many discovery services, each defined by what it indexes, whom it serves, and how it ranks… ARD helps AI clients discover capabilities, but it doesn’t replace authentication, authorization, governance, or organizational trust decisions.
目标并非单一全球资源目录。将会有许多发现服务，每个服务由其索引内容、服务对象和排名方式定义……ARD帮助AI客户端发现能力，但它不取代身份验证、授权、治理或组织信任决策。
The ARD specification is currently available with reference implementations and documentation, allowing organizations to experiment with publishing capability catalogs and exploring the federation model defined in the specification.
ARD规范目前提供参考实现和文档，使组织能够尝试发布能力目录并探索规范中定义的联邦模型。
It includes schemas, trust mechanisms, and guidelines for interoperability across discovery services.
它包括模式、信任机制以及跨发现服务互操作的指南。
The broader ecosystem is expected to evolve through community contributions, including implementation feedback and extensions to the schema and governance model.
更广泛的生态系统预计将通过社区贡献（包括实现反馈以及对模式和治理模型的扩展）不断发展。