Evolution of AI Agent Registry Solutions: Centralized, Enterprise, and Distributed Approaches

TL;DR

This paper compares five AI agent registry solutions across centralized, enterprise, and distributed models, analyzing their security, scalability, and maintainability to guide future interoperable AI agent ecosystems.

Contribution

It provides a comprehensive analysis of diverse registry architectures and offers design recommendations for trustworthy, scalable, and interoperable AI agent discovery systems.

Findings

Distributed approaches enhance resilience and privacy.

Centralized registries offer simplicity and control.

Trade-offs exist between security, scalability, and maintainability.

Abstract

Autonomous AI agents now operate across cloud, enterprise, and decentralized domains, creating demand for registry infrastructures that enable trustworthy discovery, capability negotiation, and identity assurance. We analyze five prominent approaches: (1) MCP Registry (centralized publication of mcp.json descriptors), (2) A2A Agent Cards (decentralized self-describing JSON capability manifests), (3) AGNTCY Agent Directory Service (IPFS Kademlia DHT content routing extended for semantic taxonomy-based content discovery, OCI artifact storage, and Sigstore-backed integrity), (4) Microsoft Entra Agent ID (enterprise SaaS directory with policy and zero-trust integration), and (5) NANDA Index AgentFacts (cryptographically verifiable, privacy-preserving fact model with credentialed assertions). Using four evaluation dimensions: security, authentication, scalability, and maintainability, we surface architectural trade-offs between centralized control, enterprise governance, and distributed resilience. We conclude with design recommendations for an emerging Internet of AI Agents requiring verifiable identity, adaptive discovery flows, and interoperable capability semantics.