A2A vs MCP: How the Two Agent Protocols Fit Together
If you're building a multi-agent system in 2026, you've probably run into both names in the same architecture diagram and wondered which one you actually need. The short answer to the A2A vs MCP question is that they're not competitors — they solve problems at different layers of the stack. MCP (Model Context Protocol) connects a single AI application to the tools, files, and data it needs to do its job. A2A (Agent2Agent) connects one autonomous agent to another so they can delegate work and collaborate without either side needing to know how the other is built internally. This piece walks through what each protocol actually does, where the boundary between them gets blurry in practice, and how to decide which one (or both) belongs in your architecture.
Why the confusion happens
The confusion is understandable. Both protocols showed up in the same twelve-month window, both use JSON-based message exchange over HTTP, and both are pitched as solving "the agent interoperability problem." Marketing decks tend to flatten them into a single "agent protocol" category, which is misleading — MCP predates A2A by several months and was built for a narrower purpose. MCP was released by Anthropic in November 2024 as, in the words of the official documentation, "an open-source standard for connecting AI applications to external systems." A2A launched in April 2025, originally announced by Google Cloud, explicitly aimed at a different problem: letting independently built agents "communicate, exchange information, and coordinate actions" as peers, according to the Linux Foundation's announcement of the project.
That's the core distinction to hold onto as you read the rest of this article: MCP is a vertical integration — it connects an agent downward to the tools and data it operates on. A2A is a horizontal integration — it connects an agent sideways to other agents it needs to collaborate with. A single production system frequently needs both wires running at once.
What MCP actually is
MCP follows a client-server architecture. According to the MCP architecture docs, the three participants are:
- MCP Host — the AI application itself (Claude Desktop, an IDE, a custom agent runtime) that coordinates one or more MCP clients.
- MCP Client — a component inside the host that maintains a dedicated 1:1 connection to a single MCP server.
- MCP Server — a lightweight program that exposes context and capabilities to whichever client connects to it.
Underneath that, MCP defines a data layer built on JSON-RPC 2.0 with three core primitives a server can expose to a client: tools (executable functions the model can invoke, like a database query or an API call), resources (read-only data such as file contents or records), and prompts (reusable interaction templates). There's also a set of primitives that flow the other direction — sampling, elicitation, and logging — that let a server ask the host's LLM to do work or ask the user a question. As of the current spec, MCP also has an experimental tasks primitive for durable, long-running operations, distinct from A2A's task concept described below.
The mental model: MCP is what lets a single agent reach down into the world — a filesystem, a CRM, a ticketing system, a search index — and pull in the context or trigger the action it needs to answer a user's request. It's not designed to model two independent, reasoning agents negotiating with each other; it's designed to model an application calling a well-defined capability.
What A2A actually is
A2A is also a client-server protocol at the transport level, but the "client" and "server" in this case are both full agents, not an application and a data source. The A2A documentation describes it as "an open standard that enables seamless communication and collaboration between AI agents," designed so agents "built by different developers, on different frameworks, and in different organizations" can work together.
The architecture centers on a few concepts that don't exist in MCP:
- Agent Card — a JSON metadata document, published at a well-known URL (
/.well-known/agent-card), that describes an agent's identity, skills, service endpoint, and required authentication. It's effectively a discovery mechanism: before you talk to a remote agent, you fetch its card to learn what it can do and how to reach it. - Client agent and remote agent — one agent initiates a request (the client role) and another agent fulfills it (the remote/server role). Either side can be a fully autonomous, stateful, LLM-driven system with its own memory and tool access.
- Tasks — the fundamental unit of work in A2A. A task has a unique ID and moves through a defined lifecycle (submitted, working, input-required, completed, failed, and so on), which matters because agent-to-agent work is often long-running and asynchronous rather than a single synchronous call.
- Message exchange — A2A defines
sendMessagefor standard request/response andsendMessageStreamfor streaming updates over Server-Sent Events, so a client agent can watch a remote agent's progress on a task that takes minutes rather than milliseconds.
Crucially, A2A treats the remote agent as an opaque black box. The client agent doesn't get to see the remote agent's internal reasoning, prompts, or tool calls — it only sees task state and message exchanges. That's a deliberate design choice: it lets agents from different vendors, running different models and different internal architectures, collaborate without either party having to expose (or trust) the other's internals.
Governance and where each protocol stands today
MCP is maintained by Anthropic with an open specification and a large ecosystem of community and vendor-built servers; it has since been adopted by other major AI providers as a client-side standard as well.
A2A's governance changed significantly after launch. Google donated the protocol to the Linux Foundation on June 23, 2025, establishing the Agent2Agent Protocol Project under vendor-neutral governance with AWS, Cisco, Google, Microsoft, Salesforce, SAP, and ServiceNow as founding members, per the Linux Foundation's press release. By its one-year mark in April 2026, the project reported more than 150 supporting organizations, SDKs in five production languages, a v1.0 stable release with signed Agent Cards, and general availability inside Microsoft Copilot Studio, Azure AI Foundry, and Amazon Bedrock AgentCore, according to the Linux Foundation's anniversary announcement. That's a meaningfully different trust posture than a single-vendor project — worth knowing if you're weighing A2A for something that needs multi-stakeholder buy-in.
Side-by-side comparison
| Dimension | A2A (Agent2Agent) | MCP (Model Context Protocol) |
|---|---|---|
| Primary relationship | Agent to agent (peer to peer) | Application/agent to tool or data source |
| Direction of integration | Horizontal — across organizational or system boundaries | Vertical — down into an agent's own capability layer |
| Core unit of work | Task, with a stateful lifecycle | Tool call, resource read, or prompt fetch |
| Discovery mechanism | Agent Card at a well-known URL | Server capability negotiation during initialize |
| Transport | HTTP, JSON-RPC-based sendMessage / sendMessageStream, SSE for streaming |
JSON-RPC 2.0 over stdio (local) or Streamable HTTP (remote) |
| What's exposed to the caller | Opaque — only task state and messages, not internal reasoning | Explicit — typed tool schemas, resource contents, prompt templates |
| Originated by | Google, donated to Linux Foundation (June 2025) | Anthropic (November 2024) |
| Governance today | Linux Foundation, vendor-neutral, 150+ orgs, v1.0 stable (April 2026) | Anthropic-led open specification with broad multi-vendor client adoption |
| Typical use case | Delegating a sub-task to a specialist agent owned by another team or vendor | Giving one agent access to a database, API, filesystem, or search index |
Where the boundary blurs
The clean separation above holds in theory, but real systems have a gray zone that's worth naming explicitly, because it's where most architecture debates actually happen.
An agent can be exposed both ways at once. Nothing stops you from taking the same underlying agent — say, a research agent that searches the web and synthesizes findings — and exposing it as an MCP tool to one caller (a coding assistant that wants a quick, synchronous answer) while also exposing it as an A2A remote agent to another caller (an orchestrator that wants to delegate an open-ended, long-running research task and track its progress). The difference isn't the agent's capabilities; it's the interaction contract. MCP tool exposure implies a bounded, largely synchronous, single-shot interaction with a fixed schema. A2A exposure implies a stateful task with a lifecycle, potential multi-turn negotiation, and the caller treating the callee as an autonomous peer rather than a function.
"Wrapping an agent as a tool" is a real anti-pattern A2A was built to avoid. Before A2A existed, the common workaround for agent-to-agent delegation was to shove one agent behind an MCP tool interface — call it, get a single text blob back, done. That works for simple cases but collapses the moment the sub-agent needs to ask a clarifying question, stream partial progress, or run for ten minutes. A2A's task lifecycle and bidirectional messaging exist specifically to give agent-to-agent interaction room to be a conversation instead of a single function call. If you notice your MCP tool wrapping a full agent that occasionally needs to ask something back mid-task, that's usually a sign you actually want A2A's task and message model, not an MCP tool call.
MCP is creeping toward longer-running interactions too. The addition of an experimental "tasks" primitive to the MCP spec — for durable execution and deferred result retrieval — narrows one of the gaps that used to separate the two protocols cleanly. It doesn't make MCP a substitute for A2A's peer-to-peer model (an MCP server is still fundamentally a capability provider to a client, not an autonomous negotiating party), but it does mean "MCP can't handle async work" is no longer an accurate blanket statement.
Authentication and trust boundaries differ in practice, even if both support OAuth. MCP's transport layer recommends OAuth for remote HTTP servers, and A2A's Agent Cards can declare required security schemes with signed cards as of v1.0. But the trust model is different in spirit: an MCP server is usually something your own application chose to install and configure, closer to a dependency. An A2A remote agent is more often a third party's live service you're calling at runtime, closer to a vendor integration. That distinction should inform how much you validate, sandbox, and rate-limit each connection.
A decision framework
Use this ordering when you're deciding what to reach for:
- Does the thing you're connecting to have its own reasoning loop, memory, and autonomy — or is it a fixed capability with a defined input/output contract? A weather API, a SQL database, a file store: MCP. A specialist agent built by another team that plans, retries, and makes judgment calls: A2A.
- Is the interaction bounded and synchronous, or open-ended and stateful? A single lookup or action: MCP tool call. A task that might take minutes, need mid-flight clarification, or stream incremental progress: A2A task.
- Do you need to see inside the call, or is opacity actually a feature? If you need a typed schema and full visibility into what's being invoked, MCP's explicit tool/resource/prompt primitives fit better. If you're deliberately calling across a trust or organizational boundary and don't want (or aren't allowed) to see internal reasoning, A2A's opaque task model is the better fit.
- Is this integration internal to one application, or crossing an ownership boundary? MCP shines when one team owns both the host and the servers it configures. A2A shines when the client agent and remote agent are built and operated by different teams or companies entirely.
If you're new to MCP itself and want the fundamentals before applying this framework, our getting started with MCP guide covers building your first server and client from scratch. And if you're also weighing MCP against retrieval-augmented generation for a different part of your stack, how MCP compares to RAG covers that orthogonal decision — RAG and MCP solve a "what goes in the context window" problem, which is a separate axis entirely from the A2A vs MCP question this article addresses.
Example architectures
Single-agent assistant with tool access (MCP only). A customer-support copilot that needs to look up order status, read a knowledge base, and file a refund. There's one reasoning agent, and everything it touches is a bounded capability. Three MCP servers — orders, knowledge base, refunds — cover the whole system. A2A adds nothing here because there's no second autonomous agent to talk to.
Multi-agent pipeline within one team (A2A internally, MCP at the edges). A content pipeline where a research agent, a drafting agent, and an editing agent each have their own specialization and their own tool access. The three agents talk to each other over A2A, passing tasks down the pipeline and streaming status back up to an orchestrator. Each individual agent, in turn, uses MCP to reach its own tools — the research agent calls a web-search MCP server, the drafting agent calls a style-guide resource server, and so on. This is the pattern most real systems converge on: A2A for the agent mesh, MCP hanging off each node in that mesh.
Cross-organization delegation (A2A as the integration layer). A travel-booking agent built by one company needs to delegate part of an itinerary to a specialist airline agent built by another company, which in turn might delegate to a hotel agent from a third company. None of these organizations wants to expose their internal tool stack, prompts, or model choice to the others — they just want to hand off a task and get a result back, with visibility into task state along the way. This is the scenario A2A was explicitly designed for, and it's why enterprise platforms like Azure AI Foundry and Amazon Bedrock AgentCore added first-class A2A support: it's the interoperability layer for agents that don't share an owner.
The bottom line
A2A vs MCP isn't a fork in the road where you pick one and abandon the other — it's closer to picking the right protocol for each edge in your system's graph. MCP is the protocol for an agent reaching into its own toolbox. A2A is the protocol for one agent reaching out to a peer it doesn't control. Most non-trivial multi-agent systems built in 2026 use both simultaneously: A2A stitching together a mesh of specialist agents across teams or vendors, and MCP wiring each individual agent in that mesh to the tools and data it actually needs to get its piece of the job done. If you're staring at an architecture diagram trying to decide which protocol goes on a given arrow, ask whether that arrow points at a capability or at another mind — the answer tells you which protocol you need.