The interoperability layer of agentic AI is in the middle of a rapid, somewhat chaotic standardization process that will determine whether the multi-agent systems being built today are durable or disposable. Two protocols have emerged as the leading candidates for different parts of the interoperability problem. The Model Context Protocol (MCP), originally developed by Anthropic and donated to the Linux Foundation in December 2025, addresses the connection between an agent and the tools and data sources it uses. The Agent2Agent protocol (A2A), originally developed at Google and donated to the Linux Foundation in June 2025, addresses the communication between agents in a multi-agent system. Together they sketch an interoperability stack that has the potential to do for agentic AI what HTTP and TCP/IP did for the web: create a common substrate on which diverse systems can interact without requiring bespoke integration for every pair.
MCP: The Host-Client-Server Model
MCP defines a three-component architecture: a host (the application that runs the agent and manages connections), a client (a protocol implementation within the host that maintains a connection to a specific server), and a server (a lightweight process that exposes tools, resources, and prompts through the MCP interface). The transport between client and server uses JSON-RPC 2.0, a lightweight remote procedure call protocol that is simple enough to implement in any language and already widely supported in developer tooling. An agent that speaks MCP can connect to any MCP server — a file system, a database, a web browser, a code execution environment, a SaaS API — without requiring bespoke integration code for each. The MCP server ecosystem has grown rapidly: as of early 2026, hundreds of MCP servers are publicly available, covering everything from GitHub and Slack to weather APIs and specialized enterprise data systems.
The MCP protocol exposes three types of capabilities: tools (functions the agent can call), resources (data the agent can read), and prompts (reusable prompt templates the agent can invoke). This taxonomy maps cleanly onto the agentic use case taxonomy: tools correspond to the action capabilities an agent needs (send an email, create a record, execute a query); resources correspond to the data access capabilities it needs (read a document, query a knowledge base, retrieve a configuration); and prompts correspond to the behavioral specifications it needs (follow this procedure, apply this template, respond in this style). The three-category design is not accidental — it reflects a careful analysis of what agents actually need from their environment and creates clean boundaries between capability types that have different security implications.
The security model of MCP has evolved significantly since its initial release. The original version had minimal security specifications; the version donated to the Linux Foundation includes OAuth 2.0 support for authentication and authorization, explicit scope definitions for tool and resource access, and a consent model that requires explicit user authorization for sensitive operations. These security improvements are necessary but not sufficient for enterprise deployment — organizations building production MCP integrations will need to add additional controls at the host layer, particularly around tool call auditing and the enforcement of the decision authority matrix for consequential tool invocations.
A2A: Agent-to-Agent Task Lifecycle
While MCP addresses the agent's connection to its tools and data, A2A addresses the agent's relationship with other agents. In a multi-agent system, agents need to delegate tasks to each other, track the progress of delegated tasks, exchange intermediate results, and handle the completion or failure of a delegated workflow. A2A defines a task lifecycle protocol that standardizes these interactions: a requesting agent sends a task with a structured description and a callback address; the responding agent accepts, processes, and returns results through a defined sequence of status transitions; and the requesting agent tracks the task's progress through a polling or event-streaming interface.
The A2A protocol's task lifecycle has six states: submitted, working, input-required (the receiving agent needs additional information from the requestor), completed, failed, and cancelled. The input-required state is particularly important for enterprise deployments: it models the case where a sub-agent encounters a decision point that requires input from the orchestrating agent or from a human, allowing the task to pause and wait without losing its state. This structured interrupt mechanism is the A2A equivalent of the human-in-the-loop checkpoint that the orchestration stack must support — and its standardization means that multi-agent systems built with A2A get interrupt handling for free, rather than requiring each implementation to build it from scratch.
AGNTCY and the Agent Network Infrastructure
Beyond MCP and A2A, a third initiative is attempting to address the network infrastructure layer that a large-scale, distributed multi-agent ecosystem will require. AGNTCY — an open-source initiative led by Cisco with participation from LangChain, CrewAI, and others — is building an "internet of agents": a directory and routing infrastructure that allows agents to discover other agents, understand their capabilities, and establish secure communication channels with them. Where MCP handles the connection between an agent and its tools and A2A handles the task communication between agents, AGNTCY handles the discovery and routing problem: how does an orchestrating agent find the right sub-agent for a given task among the potentially thousands of agents available in an enterprise's or a network's agent registry?
The AGNTCY project's core components include an agent registry (a directory of available agents with structured capability descriptions), an agent connect API (a standard interface for initiating A2A connections with registered agents), and an open agent schema (a JSON Schema format for describing agent capabilities in a machine-readable way that enables automated agent selection). The combination of AGNTCY's registry, MCP's tool connectivity, and A2A's task lifecycle protocol sketches a complete interoperability stack for multi-agent systems — though as of early 2026, significant gaps remain in the security specifications and the operational tooling for managing large agent networks.
"MCP is the USB standard for agent tools. A2A is the TCP/IP for agent communication. AGNTCY is the DNS. Together they are building the plumbing for an internet of agents that nobody has fully imagined yet."
Comparing MCP and A2A
MCP and A2A are complementary rather than competing protocols, but they do have some functional overlap in the area of agent-to-agent communication that is worth understanding. An MCP server can, in principle, expose another agent as a tool — effectively using MCP to mediate agent-to-agent communication. This is simpler to implement than A2A but loses A2A's structured task lifecycle: MCP tool calls are synchronous request-response interactions, and while the MCP protocol supports streaming, it does not natively model the long-running, interruptible, multi-state task lifecycle that A2A defines. For simple agent-to-agent interactions — an orchestrating agent calling a sub-agent to perform a quick classification or extraction — MCP's tool model may be entirely adequate. For complex, long-running delegated workflows with human-in-the-loop requirements, A2A's task lifecycle model is more appropriate.
The practical implication for enterprise architects is that both protocols are likely to be present in a mature multi-agent deployment, serving different parts of the interoperability problem. MCP provides the rich ecosystem of tool integrations that give agents their capabilities; A2A provides the structured communication protocol that allows those agents to collaborate on complex tasks; and the choice of which to use for any specific agent-to-agent interaction should be made based on the complexity and duration of the interaction rather than as a global architectural decision.