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

• URL: http://arxiv.org/abs/2508.03095v3
• Date: Mon, 20 Oct 2025 17:13:35 GMT
• Title: Evolution of AI Agent Registry Solutions: Centralized, Enterprise, and Distributed Approaches
• Authors: Aditi Singh, Abul Ehtesham, Mahesh Lambe, Jared James Grogan, Abhishek Singh, Saket Kumar, Luca Muscariello, Vijoy Pandey, Guillaume Sauvage De Saint Marc, Pradyumna Chari, Ramesh Raskar,
• Abstract summary: AI agents operate across cloud, enterprise, and decentralized domains, creating demand for registry infrastructures that enable discovery, capability negotiation, and identity assurance.<n>We analyze five prominent approaches: (1) MCP Registry (centralized publication of mcp.), (2) A2A Agent Cards (decentralized self-describing capability manifests), (3) AGNTCY Agent Directory Service (IPFS Kademlia content routing extended for semantic taxonomy-based content discovery, OCI artifact storage, and Sigstore-backed integrity), (4) Microsoft Entra Agent ID (crypt, privacy-preserving fact model with credentialed assertions), and (5) NANDA Index AgentFacts
• Score: 13.123809945481405
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Agent Identity URI Scheme: Topology-Independent Naming and Capability-Based Discovery for Multi-Agent Systems [0.0]
  Multi-agent systems face a fundamental architectural flaw: agent identity is bound to network location.<n>We propose the agent:// scheme, which decouples identity from topology through three components.<n>Trust root establishing organizational authority, a hierarchical capability path, and a sortable unique identifier provide stable reference.
  arXiv  Detail & Related papers  (2026-01-21T01:09:22Z)
• Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm [85.7583231789615]
  6G positions intelligence as a native network capability, transforming the design of radio access networks (RANs)<n>Within this vision, Semantic-native communication and agentic intelligence are expected to play central roles.<n>Agentic intelligence endows distributed RAN entities with goal-driven autonomy, reasoning, planning, and multi-agent collaboration.
  arXiv  Detail & Related papers  (2025-12-04T03:09:33Z)
• Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics [75.4712507893024]
  Enterprise Deep Research (EDR) is a multi-agent system that integrates a Master Planning Agent for adaptive query decomposition.<n>Four specialized search agents (General, Academic, GitHub, LinkedIn) and a visualization agent for data-driven insights are also included.<n>EDR reflects research direction with optional human-in-the-loop steering guidance.
  arXiv  Detail & Related papers  (2025-10-20T17:55:11Z)
• OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use [101.57043903478257]
  The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations.<n>With the evolution of (multi-modal) large language models ((M)LLMs), this dream is closer to reality.<n>This survey aims to consolidate the state of OS Agents research, providing insights to guide both academic inquiry and industrial development.
  arXiv  Detail & Related papers  (2025-08-06T14:33:45Z)
• Using the NANDA Index Architecture in Practice: An Enterprise Perspective [9.707223291705601]
  The proliferation of autonomous AI agents represents a paradigmatic shift from traditional web architectures toward collaborative intelligent systems.<n>This paper presents a comprehensive framework addressing the fundamental infrastructure requirements for secure, trustworthy, and interoperable AI agent ecosystems.
  arXiv  Detail & Related papers  (2025-08-05T05:27:27Z)
• Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges [49.69200207497795]
  The convergence of Web3 technologies and AI agents represents a rapidly evolving frontier poised to reshape decentralized ecosystems.<n>This paper presents the first and most comprehensive analysis of the intersection between Web3 and AI agents, examining five critical dimensions: landscape, economics, governance, security, and trust mechanisms.
  arXiv  Detail & Related papers  (2025-08-04T15:44:58Z)
• Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training [67.895981259683]
  General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence.<n>Current agent systems are either closed-source or heavily reliant on a variety of paid APIs and proprietary tools.<n>We present Cognitive Kernel-Pro, a fully open-source and (to the maximum extent) free multi-module agent framework.
  arXiv  Detail & Related papers  (2025-08-01T08:11:31Z)
• Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
  The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
  arXiv  Detail & Related papers  (2025-07-28T17:58:12Z)
• Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts [12.955380032776068]
  We describe the NANDA index architecture, which we envision as a means for discoverability, identifiability and authentication in the internet of AI agents.<n>We present an architecture where a minimal lean index resolves to dynamic, cryptographically verifiable AgentFacts.
  arXiv  Detail & Related papers  (2025-07-18T13:40:46Z)
• AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction [70.60422261117816]
  We propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure.<n>Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection, which enables each agent to selectively access relevant information from any previous step.
  arXiv  Detail & Related papers  (2025-06-21T18:34:43Z)
• Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
  Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
  arXiv  Detail & Related papers  (2025-05-26T06:44:31Z)
• A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control [7.228060525494563]
  This paper posits the imperative for a novel Agentic AI IAM framework.<n>We propose a comprehensive framework built upon rich, verifiable Agent Identities (IDs)<n>We also explore how Zero-Knowledge Proofs (ZKPs) enable privacy-preserving attribute disclosure and verifiable policy compliance.
  arXiv  Detail & Related papers  (2025-05-25T20:21:55Z)
• Agent Name Service (ANS): A Universal Directory for Secure AI Agent Discovery and Interoperability [0.0]
  The proliferation of AI agents requires robust mechanisms for secure discovery.<n>This paper introduces the Agent Name Service (ANS), a novel architecture based on DNS addressing the lack of a public agent discovery framework.
  arXiv  Detail & Related papers  (2025-05-15T17:49:36Z)
• Internet of Agents: Fundamentals, Applications, and Challenges [68.9543153075464]
  We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
  arXiv  Detail & Related papers  (2025-05-12T02:04:37Z)
• A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP) [0.8463972278020965]
  This survey examines four emerging agent communication protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)
  arXiv  Detail & Related papers  (2025-05-04T22:18:27Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain Expertise [49.83486066403154]
  textscMimir is a streamlined platform offering a customizable pipeline for personalized agent tuning. textscMimir supports the generation of general instruction-tuning datasets from the same input. textscMimir integrates these features into a cohesive end-to-end platform, facilitating everything from the uploading of personalized files to one-click agent fine-tuning.
  arXiv  Detail & Related papers  (2024-04-03T23:42:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.