# Using the NANDA Index Architecture in Practice: An Enterprise Perspective

arXiv: https://arxiv.org/abs/2508.03101 · HTML: https://mlsanity.com/paper/2508.03101/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts](https://mlsanity.com/paper/2507.14263/) (2025)
- [Upgrade or Switch: Do We Need a Next-Gen Trusted Architecture for the Internet of AI Agents?](https://mlsanity.com/paper/2506.12003/) (2025)

## Built on by (most cited first, 3 of 3)

- [Toward a Safe Internet of Agents](https://mlsanity.com/paper/2512.00520/) (2025)
- [The AGNTCY Agent Directory Service: Architecture and Implementation](https://mlsanity.com/paper/2509.18787/) (2025)
- [AONA: A Comprehensive Architecture and Workflow Design for Global Agentic Collaboration](https://mlsanity.com/paper/2606.20573/) (2026)

## Abstract (arXiv)

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.