{"ID":2893100,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.14263","arxiv_id":"2507.14263","title":"Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts","abstract":"The Internet is poised to host billions to trillions of autonomous AI agents that negotiate, delegate, and migrate in milliseconds and workloads that will strain DNS-centred identity and discovery. In this paper, we describe the NANDA index architecture, which we envision as a means for discoverability, identifiability and authentication in the internet of AI agents. We present an architecture where a minimal lean index resolves to dynamic, cryptographically verifiable AgentFacts that supports multi-endpoint routing, load balancing, privacy-preserving access, and credentialed capability assertions. Our architecture design delivers five concrete guarantees: (1) A quilt-like index proposal that supports both NANDA-native agents as well as third party agents being discoverable via the index, (2) rapid global resolution for newly spawned AI agents, (3) sub-second revocation and key rotation, (4) schema-validated capability assertions, and (5) privacy-preserving discovery across organisational boundaries via verifiable, least-disclosure queries. We formalize the AgentFacts schema, specify a CRDT-based update protocol, and prototype adaptive resolvers. The result is a lightweight, horizontally scalable foundation that unlocks secure, trust-aware collaboration for the next generation of the Internet of AI agents, without abandoning existing web infrastructure.","short_abstract":"The Internet is poised to host billions to trillions of autonomous AI agents that negotiate, delegate, and migrate in milliseconds and workloads that will strain DNS-centred identity and discovery. In this paper, we describe the NANDA index architecture, which we envision as a means for discoverability, identifiability...","url_abs":"https://arxiv.org/abs/2507.14263","url_pdf":"https://arxiv.org/pdf/2507.14263v1","authors":"[\"Ramesh Raskar\",\"Pradyumna Chari\",\"John Zinky\",\"Mahesh Lambe\",\"Jared James Grogan\",\"Sichao Wang\",\"Rajesh Ranjan\",\"Rekha Singhal\",\"Shailja Gupta\",\"Robert Lincourt\",\"Raghu Bala\",\"Aditi Joshi\",\"Abhishek Singh\",\"Ayush Chopra\",\"Dimitris Stripelis\",\"Bhuwan B\",\"Sumit Kumar\",\"Maria Gorskikh\"]","published":"2025-07-18T13:40:46Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AI\",\"cs.CR\",\"cs.MA\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}