Internet-Draft | agent-dir | March 2025 |
Muscariello & Polic | Expires 28 September 2025 | [Page] |
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 note is to be removed before publishing as an RFC.¶
The latest revision of this draft can be found at https://spec.dir.agncty.org. Status information for this document may be found at https://datatracker.ietf.org/doc/draft-agntcy-ads/.¶
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The Agent Directory Service (ADS) provides a mechanism for discovering AI agent applications based on their capabilities. It uses a distributed directory architecture to map agent skills to directory record identifiers and maintains a list of directory servers 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. The skill taxonomy is also routable in the DHT to map skillsets to records that announce those skills.¶
Each directory record includes skills from a defined taxonomy, as specified in the [Taxonomy of AI Agent Skills] from [OASF]. While all record data is modeled using [OASF], only skills are leveraged for content routing in the distributed network of directory servers.¶
This document describes the core concepts and architecture of the Agent Directory Service.¶
In distributed systems, a reliable and collision-resistant naming scheme is crucial. The agent directory uses cryptographic hashes [RFC7838] to generate globally unique identifiers for data records.¶
ADS leverages OCI as object storage, and therefore identifiers are made available as described in [OCI digest].¶
ADS implements capability-based record discovery through a hierarchical skill taxonomy. This architecture enables:¶
Multi-agent systems publish their capabilities by encoding them as skill taxonomies. Each record contains metadata describing the agent's functional abilities. Skills are structured in a hierarchical format for efficient matching.¶
The system performs a two-phase discovery operation:¶
Matches queried capabilities against the skill taxonomy to determine records by their identifier¶
Identifies the server nodes storing relevant records.¶
## Distributed Resolution¶
Local nodes execute targeted retrievals based on:¶
Skill matching results: Evaluates capability requirements.¶
Server location information: Determines optimal data sources.¶
ADS uses libp2p [Kad-DHT] [DHT] for server and content discovery.¶
+----------------+ | DHT Node | | Content Index | +----------------+ ^ | +----------------+-----------------+ | | | +-------v------+ +------v-------+ +-----v--------+ | Server Node A | | Server Node B| | Server Node C| | Content X | | Content Y | | Content Z | +-------+------+ +------+-------+ +------+-------+ | | | | Content Exchange | +---------------+------------------+ | Content Replication Flow: 1. Servers register content with DHT 2. DHT maintains content-to-server mappings 3. Servers query DHT to locate content 4. DHT returns list of servers hosting content 5. Servers download content from peers¶
ADS differs from block storage systems like [IPFS] in its approach to distributed object storage.¶
ADS leverages the OCI distribution specification for content storage and retrieval:¶
Records are stored and transferred using OCI artifacts.¶
Any OCI distribution-compliant server can participate in the network.¶
Servers retrieve records directly from each other using standard OCI protocols.¶
While ADS uses zot as its reference OCI server implementation, the system works with any server that implements the OCI distribution specification.¶
This document has no IANA actions.¶