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Agent Name Service (ANS) for Secure AI Agent Discovery
Rating: 5.0 out of 5(6 ratings)
25 students

Agent Name Service (ANS) for Secure AI Agent Discovery

Designing the Agent Name Service (ANS): Architecture, Roles, and Trust Models
Created byEdcorner Learning
Last updated 6/2025
English

What you'll learn

  • Understand the foundational principles of Agentic AI and how Multi-Agent Systems (MAS) operate in decentralized ecosystems.
  • Analyze the architecture of the Agent Name Service (ANS), including its components, roles, and operational flow.
  • Learn the agent registration lifecycle, covering secure onboarding, certificate-based renewal, and revocation protocols.
  • Design and interpret ANSNames with embedded semantics like versioning, capability tags, and compliance markers.
  • Implement secure resolution mechanisms including TTL enforcement, signature verification, and fallback protocols.
  • Explore public key infrastructure (PKI) and its integration into agent identity and trust management.
  • Understand how the Protocol Adapter Layer enables cross-environment agent communication via A2A, MCP, and ACP interfaces.
  • Apply Zero-Knowledge Proofs (ZKP), OAuth, JWTs, and mTLS to validate agent capabilities and isolate execution environments.
  • Use the MAESTRO 7-layer threat modeling framework to identify and mitigate risks like registry poisoning, impersonation, and denial-of-service.
  • Compare and deploy centralized, federated, and distributed registry models, enhanced with caching layers such as Redis and Memcached for scalable resolution.

Course content

7 sections • 25 lectures • 1h 16m total length

  • Understanding Agentic AI and Multi-Agent Systems (MAS)2:38

    Explore agentic AI as autonomous agents that perceive, reason, and act. Understand multi-agent systems with decentralized intelligence and emergent behavior, and how the agent name service (ans) enables secure discovery.

  • Traditional DNS vs. Agent Name Service (ANS)3:09

    Contrast traditional DNS with Agent Name Service (ANS) to show how ANS maps identities, including agents' roles, policies, and versions, using cryptographic proofs and signed records for secure, policy-aware discovery.

Requirements

  • Basic understanding of Artificial Intelligence

Description

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.

Who this course is for:

  • AI Developers and Engineers designing autonomous agents or agentic platforms seeking secure identity, registration, and discovery protocols.
  • Cloud Architects and DevOps Professionals interested in integrating agent registries, federated resolution, and runtime verification in distributed systems.
  • Cybersecurity Analysts and Architects exploring new paradigms of identity verification, PKI, and threat modeling in AI-driven environments.
  • Protocol Designers and Standards Contributors working on decentralized identity, semantic naming, or multi-agent interoperability layers.
  • Technical Product Managers building agentic systems who need to understand the architectural components and governance models of ANS.
  • Researchers in Multi-Agent Systems (MAS) looking to operationalize theory into practice with real-world tooling, registries, and security layers.
  • System Integrators and Middleware Engineers involved in adapting legacy services or orchestrating heterogeneous AI agents through standardized interfaces.
  • Web3, Blockchain, and Decentralized Infrastructure Builders seeking bridges between agent naming services and trustless environments.
  • Students and Academics in Computer Science or AI Engineering who want a hands-on understanding of emerging trends in agent discovery and resolution.
  • Open-source Contributors and Technologists interested in building, testing, or extending GitHub-based ANS prototypes and cross-domain agent registries.

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