A Survey of AI Agent Registry Solutions

2025-08-05

AI Summary

AI agents lack standardized registration mechanisms for cross-domain discovery, authentication, and capability sharing across cloud, enterprise, and decentralized environments. Method: We systematically survey three prominent agent registration paradigms—MCP, A2A, and NANDA—and establish a four-dimensional evaluation framework assessing security, scalability, authentication, and maintainability. This enables the first horizontal comparison of centralized, decentralized, and cryptographically verifiable metadata models. Contribution/Results: We propose a unified registration architecture integrating structured metadata (mcp.json), Agent Cards, and verifiable credentials (AgentFacts), incorporating GitHub-based identity verification, well-known URI discovery, and distributed directory services. We rigorously delineate the operational boundaries of each approach across deployment contexts, thereby establishing design principles and standardization pathways to advance interoperability in the AI agent internet.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent Communication
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As As autonomous AI agents scale across cloud, enterprise, and decentralized environments, the need for standardized registry systems to support discovery, identity, and capability sharing has become essential. This paper surveys three prominent registry approaches each defined by a unique metadata model: MCP's mcp.json, A2A's Agent Card, and NANDA's AgentFacts. MCP uses a centralized metaregistry with GitHub authenticated publishing and structured metadata for server discovery. A2A enables decentralized interaction via JSON-based Agent Cards, discoverable through well-known URIs, curated catalogs, or direct configuration. NANDA Index introduces AgentFacts, a cryptographically verifiable and privacy-preserving metadata model designed for dynamic discovery, credentialed capabilities, and cross-domain interoperability. These approaches are compared across four dimensions: security, scalability, authentication, and maintainability. The paper concludes with suggestions and recommendations to guide future design and adoption of registry systems for the Internet of AI Agents.

Problem

• Standardized registry systems for AI agent discovery and identity
• Comparison of metadata models for security and scalability
• Guidelines for future AI agent registry design and adoption

Innovation

• Centralized metaregistry with GitHub authentication
• Decentralized JSON-based Agent Cards discovery
• Cryptographically verifiable privacy-preserving metadata model