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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2025 IEEE 7th International Conference on Cognitive Machine Intelligence, CogMI 2025 |
| Place of Publication | usa |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 507-515 |
| Number of pages | 9 |
| ISBN (Electronic) | 9798331592059 |
| DOIs | |
| State | Published - Jan 1 2025 |
| Event | 7th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2025 - Pittsburgh, United States Duration: Nov 11 2025 → Nov 14 2025 |
Conference
| Conference | 7th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2025 |
|---|---|
| Country/Territory | United States |
| Period | 11/11/25 → 11/14/25 |
Keywords
- Agentic web
- Healthcare AI
- decentralized identity
- registry
- trust
- verifiable credentials
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