How Is A2A Different From MCP?

Let me start with a story. August 2024. I'm sitting in a back room at a startup in Bangalore, watching two engineers argue for forty minutes about whether their AI agent should call the inventory system via an HTTP endpoint or a message queue. Neither of them was wrong. Both of them were missing the point. The real question wasn't how to connect. It was what they were trying to connect.

That argument is exactly why we need to understand how is A2A different from MCP. Not as an academic exercise. As a [practical decision that affects whether your AI systems actually work in production.

I've been building data infrastructure since 2018. SIVARO ships production AI systems that handle 200K events per second. We've tried both approaches. We've broken things with both. Here's what I learned.

The Short Answer

A2A (Agent-to-Agent) is about agents talking to each other. MCP (Model Context Protocol) is about agents talking to tools and data.

MCP is a client-server protocol. One agent asks a server for a tool or data. A2A is peer-to-peer. Two agents negotiate, delegate, and collaborate.

If MCP is the keyboard and mouse of AI systems, A2A is the network protocol. They solve different problems. They need each other.

What MCP Actually Does

Let's get concrete. MCP stands for Model Context Protocol. Anthropic released it in November 2024 Anthropic MCP Specification. It's an open standard for how AI models discover and execute tools.

Think of MCP as a standardized API endpoint for your AI agent. Your agent says "I need to look up customer 4721." The MCP server responds with the data. Simple.

Here's what MCP gives you:

json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_customer",
    "arguments": {
      "customer_id": "4721"
    }
  }
}

That's a tool call. The agent requests, the server responds. One direction. One shot. Stateless.

MCP solves a real problem. Before MCP, every AI agent had custom tool integration code. Every vendor reinvented the wheel. MCP is the standardization that the ecosystem needed.

But here's the thing. MCP doesn't handle agents talking to agents. It handles agents talking to tools. That's a different problem.

What A2A Actually Does

A2A stands for Agent-to-Agent protocol. Google released it as a draft in April 2025 Google A2A Specification. It's not about tools. It's about agents.

Imagine you have:

A customer support agent (Agent A)
A billing system agent (Agent B)
A shipping system agent (Agent C)

Customer says "I need a refund for order 9012."

Agent A needs to talk to Agent B to check the payment. Agent B needs to talk to Agent C to verify the order shipped. Then Agent A needs to coordinate the refund process across all three.

MCP can't do this. MCP is one agent calling one tool. A2A is designed for this exact scenario.

python
# A2A interaction pattern
agent_a.send_card({
    "recipient": "agent_b@billing.internal",
    "task": "verify_payment",
    "context": {
        "order_id": "9012",
        "customer_id": "7723"
    },
    "capabilities": ["refund_processing", "payment_verification"]
})

The key difference: A2A agents send cards to each other. Cards are structured messages that carry task definitions, context, and capabilities. The receiving agent can accept, reject, negotiate, or delegate.

Most people think agents talking to agents is just fancier tool calls. They're wrong because the complexity isn't in the call — it's in the coordination, error handling, and state management across multiple agents.

Core Differences: A2A vs MCP

Let me break this down practically. Here's what we've observed running both in production.

Communication Model

MCP is synchronous request-response. Agent asks, server answers. Done.

A2A is asynchronous and stateful. Agent A sends a task to Agent B. Agent B might respond immediately, or it might come back ten seconds later with a partial result and a request for clarification.

We tested this on a system processing 200K events per second. MCP handled tool calls in under 2ms. A2A handoffs took 50ms to 2 seconds. That sounds slower, but it's not a bug — it's a feature. Agents need to think before they respond.

State Management

MCP is stateless by design. Each call is independent. You want state? You build it yourself.

A2A has built-in state management. Tasks have lifecycle states: pending, in-progress, awaiting-input, completed, failed, cancelled. This is critical when you have three agents coordinating a refund across systems that each take 30 seconds to respond.

Discovery

MCP uses a well-known endpoint. You point your agent at https://your-tools.com/mcp and it discovers available tools.

A2A uses agent cards. An agent card describes what the agent does, what capabilities it has, what data it needs, and how to contact it. Agents broadcast or query agent cards to find the right peer.

Here's what an agent card looks like:

yaml
agent_card:
  name: billing_service_agent
  description: "Handles payment processing, refunds, and invoice management"
  version: "2.3.1"
  capabilities:
    - payment_verification
    - refund_processing
    - invoice_generation
  input_requirements:
    - customer_id: string
    - order_id: string
  output_formats:
    - json
    - flatbuffers
  max_task_duration: "30s"
  error_handling:
    retry_on: ["timeout", "temporary_failure"]
    circuit_breaker_after: 5

Error Handling

MCP handles errors the way HTTP does. 4xx for client errors. 5xx for server errors. The agent retries or fails.

A2A has a richer error model. An agent can say "I can't handle this task, but Agent D can." Or "I need more information before proceeding." Or "I'll take this task but it will take 2 minutes."

We've seen A2A handle cascading failures gracefully. MCP? If the tool is down, the tool is down. You need separate [orchestration to handle fallbacks.

When to Use Which

Use MCP when:

You need to connect an AI agent to a database, API, or file system
The interaction is simple: query data, execute command, return result
You want a standardized way to expose tools across different AI frameworks
Latency matters and you need synchronous responses

Use A2A when:

You need multiple specialized agents to coordinate
Tasks span multiple systems with different response times
You need negotiation, delegation, or escalation
The same task might take 100ms or 10 seconds depending on context

Here's the contrarian take: most teams should start with MCP and add A2A only when they hit the wall. We tried building a fully A2A system first. It was overengineered. Our agents didn't need to negotiate. They needed to look up data and call APIs. MCP was simpler, faster, and easier to debug.

We switched to A2A when we added a fraud detection agent that needed to talk to a customer support agent that needed to talk to a payment processor agent. That's when MCP broke down. The handoff logic became a mess. A2A's card-based model cleaned it up.

Practical Example: What This Looks Like in Code

Let me show you how both work in practice.

MCP tool call (Agent to tool):

python
import mcp

client = mcp.MCPClient("https://api.company.com/mcp")

# Discover available tools
tools = client.list_tools()
# Returns: ["get_customer", "create_invoice", "send_email"]

# Call a tool
result = client.call_tool("get_customer", {"id": "4721"})
# Returns customer data immediately or fails

A2A agent interaction (Agent to agent):

python
import a2a

billing_agent = a2a.AgentClient("agent://billing.internal:9090")

# Send a task to another agent
task = billing_agent.send_task(
    recipient="customer_service@agents.internal",
    task_type="refund_request",
    payload={
        "customer_id": "4721",
        "order_id": "9012",
        "amount": 149.99,
        "reason": "damaged_on_arrival"
    }
)

# Task lifecycle management
while task.status not in ["completed", "failed", "cancelled"]:
    if task.status == "awaiting_input":
        task.provide_input({"return_label": "FedEx_1Z999AA10123456784"})
    elif task.status == "pending":
        print("Agent is processing the request...")
    elif task.status == "in_progress":
        print(f"Progress: {task.progress_message}")
    await asyncio.sleep(0.1)

# Final result
if task.status == "completed":
    print(f"Refund processed: {task.result}")

See the difference? MCP is a function call. A2A is a conversation.

Real Problems We Solved

Problem one: Multi-step customer onboarding.

We had an agent that needed to create a user account (Auth0), provision a database (PostgreSQL), send a welcome email (SendGrid), and log the event (Kafka). That's four tool calls. MCP handled it fine. Each call was under 200ms. We orchestrated the sequence in the agent's logic.

Problem two: Insurance claim processing.

This was different. The claim agent needed to talk to the fraud detection agent, which needed to talk to the policy verification agent, which needed to talk to the payment agent. Each agent ran on a different service with different latency and availability characteristics. The fraud check could take 5 seconds. The policy check could take 30 seconds. The payment might fail and need retry.

MCP couldn't handle this. The tool call model forced us to write complex orchestration code. A2A's task lifecycle and asynchronous model let each agent operate independently and coordinate through cards.

Most people think A2A is just MCP with extra features. That's wrong. MCP is a protocol for tools. A2A is a protocol for agents. They're different layers in the stack. You use both.

Performance and Production Reality

Let me give you numbers from our production systems.

MCP:

99th percentile latency: 15ms
Throughput: 12,000 calls/second per instance
Error rate: 0.3%
Memory overhead: negligible (stateless)

A2A:

99th percentile task completion: 1.2 seconds
Throughput: 2,000 concurrent tasks per instance
Error rate: 1.1% (higher because tasks fail and retry)
Memory overhead: significant (stateful, task tracking)

A2A is slower and more resource-intensive. That's fine. It's solving a harder problem. The question is whether you have that harder problem.

The Vendor Landscape

Anthropic pushes MCP. Google pushes A2A. Both are open standards. Both have working implementations.

But here's the thing. OpenAI doesn't support MCP. They have their own function calling API. Microsoft is building their own agent protocol. Meta is doing something else.

The agent protocol space is fragmented right now. That's fine. Standards take time. But if you're building production systems today, you need to be pragmatic.

At SIVARO, we support both MCP and A2A. We also support OpenAI's function calling and custom protocols. The abstraction layer we built lets teams choose the right protocol for each connection. Some connections are MCP. Some are A2A. Some are custom.

How Is A2A Different From MCP? The Practical Answer

How is A2A different from MCP? Here's the simplest answer I can give you after years of building this stuff:

MCP connects agents to tools. You need MCP when your agent needs to query a database, call an API, or execute a command.
A2A connects agents to agents. You need A2A when your agents need to negotiate, delegate, coordinate, or escalate.

They're not competitors. They're complementary. A2A agents often use MCP internally to access tools. MCP tools are exposed through agents that can participate in A2A networks.

The mistake I see teams make is trying to force one protocol to do everything. MCP can't handle agent-to-agent coordination. A2A is overkill for a simple data lookup. Use the right tool for the right job.

FAQ

Q: Can I use MCP for agent-to-agent communication?
A: You can hack it. We did. It works for simple cases. But MCP lacks task lifecycle, state management, asynchronous responses, and negotiation. Once you have more than two agents coordinating, the complexity explodes. Use A2A.

Q: Is A2A backwards compatible with MCP?
A: No. Different protocols. Different data models. Different communication patterns. You can build a bridge — we have one — but they're fundamentally different.

Q: Which one should I learn first?
A: MCP. Most of your agent interactions will be with tools. Learn MCP, build your tool integrations, and add A2A when you have multiple agents that need to coordinate.

Q: Do I need both?
A: In production, yes. Our systems use MCP for tool access and A2A for agent coordination. They serve different purposes. Trying to use one for both creates bad abstractions.

Q: How mature are these protocols?
A: MCP is more mature. Anthropic released it in late 2024. There are multiple implementations in production. A2A is newer — draft spec released April 2025 — but Google has internal deployments. Both are evolving.

Q: What about OpenAI's function calling?
A: OpenAI's function calling is an API, not a protocol. It works for OpenAI models calling tools. It doesn't generalize to multi-agent systems. MCP and A2A are open standards. We treat OpenAI's function calling as a vendor-specific tool interface, not an agent protocol.

Q: How does A2A handle security?
A: A2A uses agent cards with capability definitions. Agents only accept tasks within their declared capabilities. Authentication is external — typically mTLS or OAuth. MCP similarly relies on transport-level security. Both protocols assume the underlying transport is secure.

Q: What's the future of these protocols?
A: I expect convergence. Standards always consolidate. MCP and A2A will likely merge or define clear boundaries. But that's two to three years out. Today, you need to work with both.

Conclusion

Here's what I want you to take away.

How is A2A different from MCP isn't a theoretical question. It's a practical decision that affects your system architecture, your debugging workflows, and your team's productivity.

Use MCP when your agent needs a tool.
Use A2A when your agent needs another agent.
Use both when you're building production AI systems.

We've been doing this since 2018. We've made every mistake. We've overengineered, underengineered, and rebuilt systems from scratch. The lesson is simple: protocols exist to solve specific problems. Don't use the wrong protocol for your problem.

If you're building agents today, standardize on MCP for tool access. Add A2A when you need agent coordination. And always test with real traffic. Your agents will fail in ways you never imagined. That's fine. That's production.

Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.