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ADL vs A2A, MCP, and Other Agent Standards

The Agent Definition Language (ADL) describes who an AI agent is — its identity, capabilities, permissions, security, lifecycle, and compliance posture — in a single auditable, machine-readable document (an "agent passport"). Most other agent standards solve an adjacent problem: how agents communicate (A2A) or how they connect to tools and data (MCP). ADL is complementary to both, and can generate their artifacts.

Short answer

  • ADL vs A2A — A2A defines how agents talk to each other; ADL defines what an agent is and what it's allowed to do. ADL can generate A2A Agent Cards. They work together.
  • ADL vs MCP — MCP defines how a model connects to tools and context; ADL defines the agent's identity, permission boundaries, and governance around those tools. ADL can generate MCP server configurations. They work together.
  • ADL is not a replacement for A2A or MCP — it's the governance and identity layer above them.

How the standards divide the problem

LayerQuestion it answersStandards
Identity & governanceWho is this agent, what may it do, who authorized it?ADL
CommunicationHow do agents advertise capabilities and call each other?A2A (Agent Cards)
Tools & contextHow does the model reach tools and data?MCP

Feature comparison

ConcernA2A Agent CardsAgent SpecAGNTCYADL
Agent identityPartialDIDs + VCsDIDs + attestation
Permissions modelPartialPartialDeny-by-default
Governance & compliancePartialNIST, SOC 2, EU AI Act
Lifecycle managementPartialPartialStatus + sunset dates
Agent relationshipsPartialFlowsPartialPortfolio profile

A2A Agent Cards (Google / Linux Foundation) · Agent Spec (Oracle) · AGNTCY (Cisco / Outshift)

Detailed comparisons

  • ADL vs A2A — agent identity and governance vs agent-to-agent communication.
  • ADL vs MCP — agent identity and governance vs the model-to-tool protocol.