A2A vs MCP: Choosing the Right Protocol for Agent Communication

Most production agent systems need two protocols, not one. The Model Context Protocol (MCP) gives an agent access to tools and data sources — NetSuite records, HubSpot contacts, supplier catalogs, Redshift queries. The Agent2Agent Protocol (A2A) gives agents a way to delegate work to other agents — a quoting agent asking a catalog agent for substitute parts, a procurement agent asking a compliance agent to verify a GMP certification. Confusing the two leads to fragile architectures: using A2A to call a database, or using MCP to coordinate two independent agents, produces systems that fight their own protocol's design.

On August 1, 2026, OpenAI confirmed Astra — its next major model family, explicitly designed for long-running, multi-agent tasks that work on problems for hours or days. An internal version solved ten previously-unsolved open problems in mathematics and theoretical computer science, with the total token cost at roughly $2,000. Astra coordinates multiple agents over extended periods, which is the exact pattern where A2A (agent-to-agent delegation) and MCP (agent-to-tool access) must work together. The model layer is now being built for the two-protocol pattern.

This article is a decision framework for teams choosing between A2A and MCP — or, more commonly, deciding where each one goes in a multi-agent system. It assumes you understand the basics of each protocol. If you need an integration walkthrough, the A2A Hermes Agent bridge and the MCP + A2A protocol stack overview cover the implementation side.

The one-line distinction

MCP connects an agent to tools. A2A connects agents to agents. MCP is a tool-calling protocol — an agent requests a resource or invokes a function, and a server responds with structured data. A2A is a task delegation protocol — an agent sends a unit of work to another agent, receives streaming output, and tracks the task through a state machine. The production pattern, confirmed across 150+ A2A organizations and 10,000+ MCP servers, is: A2A between agents, MCP between agents and tools.

Where each protocol fits

Dimension	MCP	A2A
What it connects	Agent → tool, data source, API	Agent → agent
Unit of work	Tool call (request/response)	Task (stateful lifecycle)
Protocol body	Anthropic (open spec, 2026-07-28 final)	Google (open spec, Linux Foundation, 150+ organizations)
Transport	STDIO, Streamable HTTP (SSE deprecated, 12-month sunset)	JSON-RPC 2.0 over HTTP, SSE streaming
Discovery	Server registers tools; client discovers	Agent Card at /.well-known/agent-card.json
State	Stateless (2026-07-28 spec); state lives in the client	Stateful task machine: submitted → working → input-required → completed/failed/canceled
Streaming	Tool results are single responses	message/stream for real-time token and artifact delivery
Human-in-the-loop	Not a first-class concept	INPUT_REQUIRED is a first-class task state
Auth	Per-server; OAuth 2.1 in spec, bearer tokens in practice	Per-agent; Agent Card declares auth schemes, gateway handles enforcement
Adoption	10,000+ servers, 4 Tier 1 SDKs (TypeScript, Python, Go, C#)	150+ organizations, Linux Foundation governance

The table answers the first question most teams ask: if your integration is "an agent needs to query NetSuite for a customer record," that is MCP. If your integration is "a procurement agent needs a pricing agent to evaluate three supplier quotes and return a recommendation," that is A2A. The distinction is whether the thing on the other end has its own reasoning, or whether it is a data source that responds to a structured query.

The five questions that determine the split

1. Does the other end reason, or does it respond?

A NetSuite MCP server does not reason. It receives a tool call (get_customer, search_items), queries the API, and returns structured JSON. The agent that called it does the reasoning. An A2A pricing agent does reason — it receives a task ("evaluate these three quotes against historical pricing and supplier reliability"), runs its own model inference, may call its own MCP tools, and returns a recommendation with reasoning attached.

If the other end is a data source or an API, use MCP. If the other end is an autonomous agent with its own model, its own tools, and its own decision-making, use A2A. The practical test: does the thing you are calling have its own prompt? If yes, A2A. If no, MCP.

2. Do you need streaming output?

MCP tool calls are request/response. The server processes the request and returns a single result. There is no intermediate state, no token-by-token streaming, no partial artifacts. This is fine for querying a database or fetching a record — you want the complete result, not a stream of fragments.

A2A supports message/stream, which delivers token deltas and artifacts as they are produced. A pricing agent that takes 30 seconds to evaluate three quotes can stream its reasoning as it works, so the calling agent (and the human watching) can see progress, detect errors early, and cancel if the reasoning goes sideways. If your workflow produces output over time and you need to act on partial results, A2A is the protocol that supports it natively.

3. Is there a human approval gate?

MCP has no first-class concept of human-in-the-loop. You can build approval logic into the agent that calls MCP tools — the agent pauses, asks a human, then proceeds — but the protocol itself does not encode this. The approval state lives in your application code, not in the protocol.

A2A defines INPUT_REQUIRED as a first-class task state. When an agent reaches a decision that needs human sign-off — a purchase authorization, a quote approval, a data access decision — it transitions the task to INPUT_REQUIRED. The calling agent (or the human operator behind it) sees a standard protocol state, not a framework-specific detail. When the human responds, the task resumes. If your workflow includes approval gates that cross agent boundaries, A2A carries those gates transparently. The Hermes Agent A2A bridge maps Hermes's native approval requests to A2A's INPUT_REQUIRED state, so an agent delegation chain can include a human checkpoint regardless of which framework each agent runs on.

4. How long does the work take?

MCP tool calls are designed for short, synchronous operations — query an API, fetch a record, run a calculation. The 2026-07-28 spec made MCP explicitly stateless, meaning the server does not maintain conversation context between calls. State lives in the client (the agent), not in the server. This is the right design for tools: a NetSuite server should not remember that you queried a customer five minutes ago.

A2A tasks are designed for longer-running work with explicit lifecycle management. A task moves through submitted → working → completed (or failed, canceled, input-required). The state machine is part of the protocol. A pricing evaluation that takes two minutes, a compliance check that takes an hour, or a multi-agent research task that takes a day — these fit A2A's task model. OpenAI's Astra, confirmed August 1, is built for tasks that work on problems for hours or days. Astra's multi-agent coordination pattern maps directly to A2A's task lifecycle, not to MCP's stateless tool-call model.

5. Are you calling one system or coordinating multiple agents?

If your agent needs to talk to NetSuite, HubSpot, and BigCommerce, that is three MCP servers. Each server exposes tools; the agent calls them as needed. The agent does the coordination — it decides which tool to call, when, and in what order. The MCP servers do not know about each other.

If you have a procurement agent that needs to delegate to a catalog agent, a pricing agent, and a compliance agent — each with its own model and its own tools — that is three A2A endpoints. The procurement agent sends tasks, receives streaming results, and coordinates the delegation chain. The catalog, pricing, and compliance agents may each use MCP to access their own data sources. The two protocols operate at different layers: A2A handles agent-to-agent delegation, MCP handles agent-to-tool access within each agent.

When to use both: the production pattern

The production pattern, confirmed by the tyk.io enterprise guide and visible across the 150+ A2A organizations, is a two-layer architecture:

Each specialized agent is an A2A endpoint (exposes an Agent Card, accepts tasks, streams results). Each specialized agent also uses MCP to connect to its own data sources. The orchestrator agent never talks to NetSuite directly — it delegates to the pricing agent, which uses MCP to query NetSuite. This separation keeps each agent's tool surface governed and auditable, while the A2A layer handles inter-agent coordination.

The Hermes Agent A2A bridge reference implementation demonstrates this pattern: a gateway handles the A2A protocol surface (Agent Card, JSON-RPC dispatch, SSE streaming, task state machine), and pluggable handlers translate A2A task semantics to each framework's native API. Adding a new agent framework means writing one handler class — the protocol, gateway, and state machine are shared infrastructure. The same gateway can route to Hermes Agent, OpenClaw, or any future handler without changing the A2A client surface.

When single-agent is enough

Not every system needs A2A. Princeton NLP research found that a single agent matched or outperformed a multi-agent system on 64% of benchmarked tasks — at 2× the cost for the multi-agent configuration. If your workflow is a single agent that queries NetSuite, drafts a quote, and submits it for approval, you need MCP (for the NetSuite connection) and an application-level approval gate. You do not need A2A.

A2A becomes necessary when you have agents with different models, different tool surfaces, or different ownership boundaries that need to coordinate. A procurement team's sourcing agent and a finance team's compliance agent are owned by different groups, may run on different infrastructure, and have different model selections. A2A gives them a protocol to delegate and track work without sharing a codebase or a deployment. If all your agents are the same model on the same infrastructure with the same owner, a single agent with MCP tools is simpler and cheaper.

The MCP 2026-07-28 spec final: what changed for this decision

The MCP specification shipped as final on July 28, 2026. All four Tier 1 SDKs (TypeScript, Python, Go, C#) speak 2026-07-28. The spec made MCP explicitly stateless — servers do not maintain session state between calls. The 12-month SSE deprecation policy is active: the Streamable HTTP transport replaces SSE, and existing SSE deployments have until July 2027 to migrate.

For the A2A-vs-MCP decision, the spec final confirms the layering: MCP is a stateless tool protocol. If you were using MCP sessions to maintain agent conversation state, the spec says to stop — state belongs in the agent (the MCP client), not in the server. This makes the A2A layer more clearly necessary: long-running, stateful coordination across agents is not something MCP is designed to carry. A2A's task state machine fills that gap.

A note on transport overlap

Both protocols use HTTP and SSE, which can cause confusion about whether they compete. They do not — the transport overlap is superficial. MCP uses HTTP for tool calls (request → response) and is migrating from SSE to Streamable HTTP for server-initiated notifications. A2A uses JSON-RPC 2.0 over HTTP for task dispatch and SSE for streaming task output. The transport is plumbing; the protocol semantics are different. MCP carries tool calls. A2A carries task lifecycles. You can run both over the same gateway — the reference implementation does exactly this, with the gateway handling HTTP and SSE for both protocols while the bridge layer translates to each framework's native API.

What this means for your architecture

If you are building a B2B agent system — RFQ automation, procurement workflows, customer support with knowledge graphs, data pipeline orchestration — the protocol decision follows the workflow shape:

• One agent, multiple data sources → MCP only. The agent uses MCP modules to connect to NetSuite, HubSpot, BigCommerce, and supplier catalogs. No A2A needed.
• Multiple agents, same owner, same infrastructure → MCP for tools, application-level coordination for inter-agent work. Consider A2A if the coordination logic becomes complex enough that a protocol-level task machine would simplify it.
• Multiple agents, different owners or different infrastructure → A2A for agent-to-agent delegation, MCP for each agent's tool access. This is the production pattern for distributed agent systems.
• Long-running tasks with human approval gates → A2A for the task lifecycle and INPUT_REQUIRED state, MCP for the tool calls within each task. The approval gate crosses agent boundaries as an A2A state transition, not as custom application code.

OpenAI's Astra confirmation on August 1 makes the multi-agent, long-running pattern the frontier model design direction. Astra is built for tasks that work on problems for hours or days, coordinating multiple agents over extended periods. The protocol stack that supports this pattern is A2A for coordination and MCP for tool access — the two-protocol architecture this article describes.

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A mid-market distributor needs a quoting agent that talks to a catalog agent that talks to a compliance agent — each backed by a different model, each owned by a different team, each connecting to different systems through MCP modules. A2A gives those agents a shared protocol for delegation and streaming. MCP gives each agent governed access to its data sources. The bridge pattern lets Hermes Agent, OpenClaw, and any other framework participate in the A2A network without rewriting their internals.

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