Cover Image for Online NYC chapter For Agentic Web
Cover Image for Online NYC chapter For Agentic Web
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Online NYC chapter For Agentic Web

Hosted by Ramesh Raskar, Pratheep Ramanujam & Kunal Shah
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About Event

​Who is going to build the foundational infrastructure for the Open Agentic Web - a system where trillions of AI agents can collaborate, communicate, and transact across organizational boundaries without bottlenecks or security vulnerabilities. NANDA addresses the core challenge: how can billions or even trillions of AI agents discover each other, verify capabilities, and coordinate tasks without creating bottlenecks or security vulnerabilities. The project develops both the technical infrastructure (index - interop links between all heterogenous agent registries, protocols, SDKs) and the governance frameworks needed for a responsible, Open Agentic Web.

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NANDA, is architecting the building blocks for the Internet of AI Agents in a three-phase roadmap.

​​​​​​​​​About NANDA: Research

​​​​​​​​​How can trillions of  AI agents collaborate across organizational silos, communicate seamlessly, navigate autonomously on the internet, socialize, learn, earn and transact on our behalf.

​​​The NANDA efforts include research, code base, standards and a venture hub. Roadmap includes trust layers, knowledge pricing, packet switched intelligence, population AI and PrivateML.

​​Research and Development Roadmap

​​​​​​​Phase 1: Foundations of Agentic Web Build the core infrastructure enabling agents to find each other, communicate seamlessly across different platforms and protocols, and integrate easily into existing systems through standardized onboarding tools and interoperability frameworks.

​​​​​​​Phase 2: Agentic Commerce Establish economic mechanisms where agents can price and trade knowledge, leverage distributed edge computing resources, and participate in marketplace systems with proper payment protocols and incentive structures for services and capabilities.

​​​​​​​Phase 3: Society of Agents Create collective intelligence networks where large populations of agents can learn collaboratively while maintaining privacy, coordinate across organizational data boundaries, and distribute AI processing across networks to achieve emergent group behaviors.

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