Semantic Agent Cards

• Semantic Agent Cards are standardized constructs that summarize an agent’s identity, capabilities, and operational metadata to enable robust interoperability.
• They leverage structured schemas (JSON, YAML, JSON-LD) to encode skills, authentication methods, and protocols for secure, dynamic multi-agent orchestration.
• The inclusion of semantic embeddings and digital signatures ensures precise agent discovery, capability matching, and trustworthy federation.

Semantic Agent Cards are standardized, machine-readable constructs that encode an artificial agent’s identity, capabilities, invocation semantics, and operational requirements for robust interoperability, discovery, and collaboration in multi-agent ecosystems. Semantic Agent Cards enable agents to advertise and negotiate their skills, facilitate secure and scalable peer-to-peer orchestration, and ground agent selection, recommendation, and communication in context-sensitive semantic representations.

1. Conceptual Foundations and Evolution

Semantic Agent Cards have emerged as a response to the need for interoperable, dynamic, and autonomous agent ecosystems, particularly within LLM-powered and multi-agent systems. In early agent architectures, agent discovery, capability negotiation, and orchestration required ad-hoc interfaces, which led to fragility and poor scalability (Ehtesham et al., 4 May 2025). The Agent Card paradigm abstracts agent self-description into explicit, structured, and versionable digital artifacts that encapsulate not only technical skills but also security, authentication, and protocol affordances.

The term "Semantic Agent Card" surfaced prominently in contemporary agent ecosystem protocols such as A2A (Agent-to-Agent Protocol) (Ehtesham et al., 4 May 2025), and in semantically grounded agent selection frameworks like AgentRec (Park et al., 23 Jan 2025). The concept extends naturally to cross-cutting domains, including AI governance (AI Cards (Golpayegani et al., 26 Jun 2024)) and semantic web-based agent registries.

2. Technical Structure and Schema

Agent Cards are typically implemented as JSON, YAML, or JSON-LD documents exposed at well-known endpoints (e.g., /.well-known/agent.json), allowing for automated retrieval and processing (Ehtesham et al., 4 May 2025). The schema is semantically structured to encode all salient agent metadata necessary for dynamic interoperability:

Field	Purpose	Data Type
name	Human-readable agent name	String
version	Artifact or protocol version	String
description	Free-text description of the agent’s domain/purpose	String
skills	Array of skills, each with name/purpose/input/output schemas	Array/Object
authentication	Supported auth methods (OAuth, JWT, DID, etc)	Array/String
protocols	Supported transport/protocols (HTTP, SSE, JSON-RPC, etc)	Array/String
usage	Instructions for skill invocation and parameterization	String/Object
metadata	Trust, provenance, contact, documentation URLs, signatures	Object

Skills are described via input and output schemas, typically following JSON Schema for syntactic/semantic validation. In the Agent Network Protocol (ANP), agent descriptions are further enriched with semantic web vocabularies and W3C DIDs, affording global, decentralized discovery and attribution (Ehtesham et al., 4 May 2025).

Formally, an Agent Card $C$ can be modeled as:

$$C = \{\text{name}, \text{version}, \text{description}, \{\text{skills}_i\}_{i=1}^n, \text{authentication}, \text{protocols}, \ldots\}$$

with $$\text{skills}_i = \{\text{name}, \text{purpose}, \text{input\_schema}, \text{output\_schema}\}$$.

3. Functional Role in Agent Discovery, Selection, and Orchestration

Agent Cards are the basis for three critical agent lifecycle functions:

Discovery

Agents are programmatically discoverable via their exposed Agent Cards, which act as both advertisement and contract. Consumers (human or agentic) enumerate available agents, introspect exposed skills, and retrieve invocation requirements without prior integration.

Selection and Capability Negotiation

Automation frameworks parse Agent Cards to match user/agent requirements with available agent functionality (“capability matching”), checking that authentication, protocol, and data format constraints can be satisfied for secure task execution. Capability negotiation then proceeds by aligning invocation schemas and access policies.

Orchestration and Invocation

Workflows or composite agents use Agent Cards to construct valid task invocations, embedding required schemas, credentials, and negotiation tokens. The returned artifacts—again, schema-bound—enable downstream processing, chaining, or auditing.

In the Agent-to-Agent Protocol (A2A), the absence of a valid Agent Card renders an agent “effectively invisible,” precluding its participation in the ecosystem (Ehtesham et al., 4 May 2025).

4. Security, Trust, and Lifecycle Management

Agent Cards incorporate explicit mechanisms for identity verification, access control, and integrity attestation:

• Digital Signatures: Cards are signed to bind capabilities and metadata to the agent origin, thwarting spoofing and unauthorized capability injection.
• Immutable, Versioned Manifests: Cards are version-controlled and protected from rollback attacks or unauthorized mutation, often validated via hashes or versioned diffs.
• Authentication Policy: Each card encodes the expected credential type(s), scope, and delegation approach (e.g., OAuth2, API keys, decentralized identifiers).
• CI/CD Integration: Build pipelines for card publication are hardened to resist supply-chain compromise.

Table: Security Mechanisms in Agent Cards (A2A context) | Threat | Mitigation | |-------------------------------|-----------------------------------------| | Manifest spoofing | Digital signature verification | | Capability injection/tampering| Immutable versioned manifests, CI/CD | | Unauthorized invocation | Declared auth schemes, scope restriction|

A plausible implication is that Agent Card signatures and tight schema validation are essential for trustworthy autonomous agent marketplaces.

5. Semantic Representation and Adaptive Selection

Beyond static capability encoding, an emerging dimension of Semantic Agent Cards is the use of semantic embeddings and context-associated corpora to represent agent competencies and domains in a shared latent space (Park et al., 23 Jan 2025). In AgentRec, each agent is associated with a “Semantic Agent Card” composed of a corpus of language prompts describing its operational profile. These prompts are mapped, via a Sentence-BERT encoder, to embedding clusters that serve as the agent’s semantic fingerprint. Agent selection is performed by embedding user queries into the same space and measuring similarity—a process robust to semantic drift, new agent induction, and context-sensitive intent.

The score for agent $q$ is given by a p-mean aggregation of cosine similarities:

$$S_q = \ln\left( \frac{1}{n} \sum_{i=1}^n (\cos\theta_i)^p \right)^{1/p}$$

where $n$ is the agent prompt corpus size, $\theta_i$ the similarity for each prompt, and high $p$ (e.g., $p=200$) amplifies discriminative selection. Alignment to human judgment is further refined through reinforcement learning from human feedback (RLHF). This approach enables agents to be added or updated simply by enriching or altering their prompt corpora, providing a flexible and explainable method for agent representation (Park et al., 23 Jan 2025).

6. Interoperability Across Ecosystems and Protocols

While Agent Cards originated in A2A, their semantic and functional utility has been generalized across several agent interoperability protocols (Ehtesham et al., 4 May 2025):

Protocol	Agent Card (A2A)	Equivalent Construct	Notable Features
MCP	N/A	Tool/function descriptors	No agent-level manifest
ACP	Agent Detail	JSON/YAML manifest	Similar schema/function, at agent scope
ANP	Agent Description (ADP)	JSON-LD with DIDs/vocabularies	Adds decentralized identity, semantic web tech

The evolution from A2A to ANP reflects increasing semantic richness, decentralized identifiers, and global discovery/attribution, further supporting complex agent societies and marketplaces.

7. Applications, Implications, and Outlook

Semantic Agent Cards underpin scalable agent interoperability, secure orchestration, and dynamic adaptation in multi-agent systems:

• Plug-and-Play Composition: Agents discovered via their cards can be combined in arbitrarily complex workflows, supporting rapidly evolving requirements.
• Semantic Alignment: Embedding-based Agent Cards enable nuanced, context-aware agent selection, outperforming rigid class-based dispatch and enabling robust task matching even in the presence of ambiguous or emerging domains.
• Governance and Compliance: The conceptual architecture aligns closely with frameworks such as AI Cards (Golpayegani et al., 26 Jun 2024), extending semantic carding to audit, provenance, and risk representation for responsible AI deployment.
• Autonomous Federations: The combination of explicit, semantically grounded self-description and cryptographically enforced authentication is foundational for secure, scalable autonomous marketplaces and federation.

This suggests that the Semantic Agent Card paradigm—coupling explicit machine-readable manifests with latent semantic representations and rigorous security—will be a central element in the standardization and automation of future agent-based AI infrastructure.

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References:

• "AgentRec: Agent Recommendation Using Sentence Embeddings Aligned to Human Feedback" (Park et al., 23 Jan 2025)
• "A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)" (Ehtesham et al., 4 May 2025)
• "AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act" (Golpayegani et al., 26 Jun 2024)
• "Semantic-Driven AI Agent Communications: Challenges and Solutions" (Yu et al., 1 Oct 2025)