Agent Name Service (ANS) for Secure AI Agent Discovery
Designing the Agent Name Service (ANS): Architecture, Roles, and Trust Models
Agent Name Service (ANS) for Secure AI Agent Discovery free download
Designing the Agent Name Service (ANS): Architecture, Roles, and Trust Models
This course offers a comprehensive foundation in Agent Name Service (ANS) for Secure AI Agent Discovery, focusing on how autonomous agents securely identify, verify, and collaborate through the Agent Name Service (ANS) framework. We begin by establishing a clear understanding of Agentic AI and Multi-Agent Systems (MAS), framing how independent, task-oriented agents function within intelligent digital ecosystems. From there, learners explore the core architecture of ANS, diving into components such as agent resolvers, trust authorities, and federated registries. Special emphasis is placed on the Agent Registration Lifecycle, highlighting how agents are registered, renewed, and revoked in a secure, traceable manner using Public Key Infrastructure (PKI) and digital certificates.
The course then examines how agent discovery and interaction are governed through structured semantics, introducing the ANSName format—an intuitive, hierarchical naming system that embeds identity, capability, version, and compliance in each agent name. Key mechanisms such as version negotiation, signature verification, TTL enforcement, and endpoint validation ensure robust, real-time resolution and prevent impersonation or misuse. Students will also learn about governance challenges, including naming collisions and domain ownership, with comparisons to ICANN-style registries.
A full module is devoted to the Protocol Adapter Layer, explaining how ANS supports varied agent interactions (A2A, MCP, ACP) through capability cards, metadata schemas, role-based policies, and secure delegation frameworks. This is paired with deep dives into identity modeling and verification, including the use of Zero-Knowledge Proofs (ZKPs), JWTs, OAuth, mutual TLS, and sandbox enforcement to authenticate and isolate agents at runtime.
Advanced sessions explore security using the MAESTRO 7-Layer Threat Model, analyzing vulnerabilities like registry poisoning, DoS, and side-channel attacks, and presenting ANS-specific mitigation strategies. Finally, learners evaluate implementation options such as centralized vs. distributed registries, federated resolution, and hybrid caching models (Redis, Memcached) to scale ANS securely and efficiently.
