Functional Taxonomy of Agent Infrastructure

• Functional Taxonomy of Agent Infrastructure is a structured framework that categorizes the core mechanisms, protocols, and architectural patterns of multi-agent systems.
• It supports decentralized discovery by leveraging hierarchical taxonomies, cryptographic integrity, and federated registry models.
• The framework enables scalable, secure, and interoperable agent ecosystems through schema-driven extensibility and efficient indexing.

A functional taxonomy of agent infrastructure provides a structured framework for organizing, classifying, and comparing the core mechanisms, protocols, and architectural patterns underlying contemporary multi-agent systems. The taxonomy encapsulates the spectrum of enablers, integration approaches, and operational primitives that support agent discovery, capability expression, registry, orchestration, security, and interoperability in heterogeneous environments. This taxonomy draws on recent advances and formalizations, notably the AGNTCY Agent Directory Service and its comparative position among emerging registry and agent coordination initiatives (Muscariello et al., 23 Sep 2025).

1. Hierarchical Taxonomies for Agent Capabilities and Infrastructure

Modern agent infrastructure is underpinned by explicit, multi-dimensional, and hierarchically structured taxonomies. In AGNTCY ADS, the capability taxonomy consists of skills (core, hierarchically organized using fully-qualified identifiers, FQIDs, e.g., nlp.summarization.abstractive), domains (flatter, orthogonal categorizations, e.g., healthcare, finance.retail), and features (modality or operational extensions, e.g., streaming-output, eval.safety.v1-pass). The skill taxonomy forms a directed, rooted tree with bounded depth (typically ≤4), with each skill node uniquely addressable by FQID and semantic version. Domains and features are indexed independently, enabling multi-axial query and filtering.

Extensibility is achieved through federated publication of subtrees under unique namespaces, supporting forward compatibility and independent evolution. Each agent record declares atomic sets of skill, domain, and feature references, providing a fine-grained, composable means for capability declaration and discovery.

Taxonomy Axis	Structure	Example Identifier	Extensibility Mechanism
Skill	Hierarchy	nlp.summarization.abstractive	Federated namespace subtrees
Domain	Flat/tree	healthcare	Independent publication
Feature	Flat	streaming-output	Registry-driven

2. Layered Architectural Model for Agent Infrastructure

The AGNTCY ADS and similar systems implement a layered infrastructure to achieve scalability, composability, and robustness:

1. Schema Layer: Based on OASF, formalizes agent records with versioning and extension points, supporting emerging agent modalities such as prompt bundles, MCP descriptors, and evaluation graphs.
2. Indexing Layer: Maintains capability-centric indices mapping taxonomy nodes to sets of content identifiers (CIDs), realized as cryptographically versioned, immutable OCI artifacts. These indices are advertised and retrieved via a Kademlia-based DHT.
3. Storage Layer: Stores the agent and index artifacts in OCI/ORAS-compliant registries, using SHA-256 digests for addressability.
4. Distribution/Replication Layer: Federated registry model, supporting synchronization and cache/mirror-based scaling, without needing global artifact location knowledge.
5. Security Layer: All records and indices cryptographically signed via Sigstore; content-addressed invariance enforces tamper resistance.

Decoupling between capability indices and artifact locations is central: indices (posting lists per skill/domain/feature) map to CIDs; CIDs resolve to endpoint locations. Queries compute intersections in posting lists:

$$C = P_s \cap \left( \bigcap_i P_{d_i} \right) \cap \left( \bigcap_j P_{f_j} \right)$$

where $P_k$ are posting lists for skill/domain/feature $k$.

3. Distributed Discovery and Security Mechanisms

Discovery, replication, and integrity are driven by DHT-mediated mapping and strong cryptographic guarantees:

• Index Resolution: DHT key is the hash of the taxonomy FQID; value is the current index artifact CID(s).
• CID Location: DHT lookup yields storage endpoints (registry URLs, mirrors, caches) for given artifact CIDs.
• Artifact Retrieval: Retrieval involves digest validation and, optionally, signature verification (Sigstore with transparency logs).
• Delta Distribution: Posting lists (index artifacts) and their deltas are sorted, immutable, and distributed as OCI artifacts for durability and efficient update propagation.

High read/write availability (small index objects), staleness compensation via fallback, and independent registry operation all contribute to the scalability and resilience of agent infrastructure.

4. Schema-Driven Extensibility and Cross-Ecosystem Discovery

OASF-based schema-driven extensibility allows infrastructure to evolve:

• Additive, type-safe extensions—for representation of more complex agent modalities (prompt graphs, evaluation metrics).
• Backward/forward compatibility—unrecognized schema extensions are safely ignored.
• Multi-modal agent representation—LLM agents, MCP servers, and A2A-enabled components share discovery substrate, facilitating cross-ecosystem capability queries.

Schema/format updates do not necessitate global recompilation or re-indexing, enabling pragmatic federation.

5. Comparative Landscape and Interoperability

A clear distinction exists between AGNTCY ADS and other registry/registry-like initiatives:

System	Skill Semantics	Taxonomy Support	Extensibility	Notes
MCP	Structured (flat/ad-hoc)	Limited	Moderate	Runtime context, not taxonomy
A2A	Protocol-level	Minimal	Limited	Agent comms, not cap. sets
NANDA	Hierarchical, DNS-like	Strong	High	Naming/resolution emphasis
AGNTCY	Hierarchical, structured	Multi-dimensional	High	Content-addressed, federated

The distinguishing properties of AGNTCY ADS include hierarchical, multi-dimensional capability taxonomy, explicit content-addressed integrity, and a robust separation of indices from storage. ADS infrastructure complements, rather than replaces, naming systems (NANDA/Entra), protocol registries (MCP/A2A), and artifact standards (OCI/ORAS).

6. Formalization and Operational Workflow

The operational workflow of a capability query comprises:

1. Retrieve posting lists for each required skill, domain, and feature from the DHT-indexed indices.
2. Compute set intersection of CIDs ($C$) matching all criteria.
3. For each resultant CID, resolve storage endpoints for artifact retrieval.

This strict decoupling enables scalable, low-latency, and failure-resilient capability discovery and reference.

7. Impact and Future Directions

Agent infrastructure functional taxonomy, exemplified by AGNTCY ADS, establishes a foundation for resilient, cryptographically auditable, and interoperable agent discovery and capability negotiation. The outlined model scales to federated, privacy-preserving, and heterogeneous multi-agent systems without central lock-in, and supports rapid composability for new agent modalities. The explicit use of hierarchical taxonomy, content-addressed storage, and schema-driven extensibility ensures adaptability and future-proofing as agent ecosystems and standards evolve (Muscariello et al., 23 Sep 2025).