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Overview

The Agent Directory Service (ADS) is a distributed directory service designed to store metadata for AI agent applications. This metadata, stored as directory records, enables the discovery of agent applications with specific skills for solving various problems. The implementation features distributed directories that interconnect through a content-routing protocol. This protocol maps agent skills to directory record identifiers and maintains a list of directory servers currently hosting those records. Directory records are identified by globally unique names that are routable within a DHT (Distributed Hash Table) to locate peer directory servers. Similarly, the skill taxonomy is routable in the DHT to map skillsets to records that announce those skills.

The Agent Directory leverages the OASF to describe AI agents and provides a set of APIs and tools to build, store, publish and discover AI agents across the network by their attributes and constraints. Directory also leverages the CSIT for continuous system integration and testing across different versions, environments, and features.

Each directory record must include skills from a defined taxonomy, as specified in the Taxonomy of AI Agent Skills from the OASF. While all record data is modeled using the OASF, content routing in the distributed network of directory servers is driven by a subset of record attributes — skills, domains, modules, and locators — which are announced as labels and used for network discovery.

The ADS specification is under active development as an IETF Internet Draft, with a Go reference implementation that exposes gRPC and protocol buffer interfaces. See Specifications and references for the relevant links.

Features

ADS enables several key capabilities for the agentic AI ecosystem:

  • Capability-Based Discovery: Agents publish structured metadata describing their functional characteristics as described by the OASF. The system organizes this information using hierarchical taxonomies, enabling efficient matching of capabilities to requirements.
  • Verifiable Claims: While agent capabilities are often subjectively evaluated, ADS provides cryptographic mechanisms for data integrity and provenance tracking. This allows users to make informed decisions about agent selection.
  • Semantic Linkage: Components can be securely linked to create various relationships like version histories for evolutionary development, collaborative partnerships where complementary skills solve complex problems, and dependency chains for composite agent workflows.
  • Distributed Architecture: Built on proven distributed systems principles, ADS uses content-addressing for global uniqueness and implements distributed hash tables (DHT) for scalable content discovery across decentralized networks.

Specifications and references

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