构建可验证可信的智能体协作网络,解决跨协议安全连接难题。
Using the NANDA Index Architecture in Practice: An Enterprise Perspective
- 基于去中心化架构实现智能体全局发现与能力认证。
- 支持MCP、A2A、NLWeb等多协议互通,保障跨平台协作安全。
- 适用于企业级智能体治理,兼顾安全与自主性,适合大规模部署。
自主AI智能体的兴起标志着从传统网页架构向协同智能系统范式的转变,亟需在异构协议环境中实现智能体发现、身份认证、能力验证与安全协作的复杂机制。本文提出NANDA(去中心化架构中的网络化智能体)框架,提供全球范围的智能体发现、通过AgentFacts实现密码学可验证的能力证明,并支持Anthropic的模态上下文协议(MCP)、Google的智能体对智能体(A2A)、Microsoft的NLWeb及标准HTTPS通信的跨协议互操作性。NANDA遵循零信任智能体访问(ZTAA)原则,扩展传统零信任网络访问(ZTNA)以应对自主智能体面临的能力伪造、冒名攻击和敏感数据泄露等安全挑战。框架定义了智能体可见性与控制(AVC)机制,支持企业治理的同时保持运营自主性与合规性。该方案将孤立的智能体转化为可验证、可信的智能服务生态系统,为企事业与消费级场景下的大规模自主智能体部署奠定基础。本工作填补了当前智能体能力与安全可扩展协作基础设施之间的关键鸿沟,奠定了下一代自主智能系统的技术基石。
原文摘要 · Abstract (English)
The proliferation of autonomous AI agents represents a paradigmatic shift from traditional web architectures toward collaborative intelligent systems requiring sophisticated mechanisms for discovery, authentication, capability verification, and secure collaboration across heterogeneous protocol environments. This paper presents a comprehensive framework addressing the fundamental infrastructure requirements for secure, trustworthy, and interoperable AI agent ecosystems. We introduce the NANDA (Networked AI Agents in a Decentralized Architecture) framework, providing global agent discovery, cryptographically verifiable capability attestation through AgentFacts, and cross-protocol interoperability across Anthropic's Modal Context Protocol (MCP), Google's Agent-to-Agent (A2A), Microsoft's NLWeb, and standard HTTPS communications. NANDA implements Zero Trust Agentic Access (ZTAA) principles, extending traditional Zero Trust Network Access (ZTNA) to address autonomous agent security challenges including capability spoofing, impersonation attacks, and sensitive data leakage. The framework defines Agent Visibility and Control (AVC) mechanisms enabling enterprise governance while maintaining operational autonomy and regulatory compliance. Our approach transforms isolated AI agents into an interconnected ecosystem of verifiable, trustworthy intelligent services, establishing foundational infrastructure for large-scale autonomous agent deployment across enterprise and consumer environments. This work addresses the critical gap between current AI agent capabilities and infrastructure requirements for secure, scalable, multi-agent collaboration, positioning the foundation for next-generation autonomous intelligent systems.
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