MCP and A2A appear early in the development of agent systems, often framed as a decision between two competing standards. That framing misses the point.

These open-source standards operate at different layers of an agent architecture and solve different coordination problems. MCP focuses on how an agent accesses tools and data, while A2A focuses on how multiple agents discover each other and coordinate work. Most production systems rely on both.

This guide explains where each protocol fits, how they differ in practice, and how to use them together without creating architectural friction.

TL;DR

• MCP and A2A solve different coordination problems at different layers. MCP handles how an agent accesses tools and data (vertical integration). A2A handles how multiple agents discover each other and coordinate work (horizontal integration). Most production systems use both.
• MCP eliminates the N×M integration problem through a single client that connects to any MCP server. A2A standardizes multi-agent collaboration through Agent Cards that advertise capabilities while preserving opacity to protect internal logic.
• Start with MCP alone for single-agent tool access. Add A2A when your system requires multiple specialized agents that delegate work, track long-running tasks, or coordinate across vendor boundaries.
• Airbyte's Agent Engine sits beneath both protocols, which provides agents with governed, continuously updated data through MCP while remaining neutral to how those agents coordinate through A2A. This eliminates the need to reimplement data access logic or hardcode brittle integrations.
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What Is A2A (Agent to Agent)?

Agent-to-Agent (A2A) Protocol is an open-source standard that allows autonomous AI agents to discover each other and coordinate work across different platforms and vendors. This standard lets agents discover what other agents can do and work together securely on long-running tasks.

Before A2A, coordinating multiple specialized agents meant building custom integration code for each pairing. A2A standardizes this through Agent Cards, which are JSON metadata documents where agents advertise their capabilities.

The protocol preserves agent opacity by design, and allows collaboration without exposing internal memory, proprietary logic, or tool implementations. This creates security boundaries essential for multi-tenant systems and external agent partnerships.

What Is MCP (Model Context Protocol)?

Model Context Protocol (MCP) is Anthropic's open standard for connecting large language models to external data sources and tools. It solves what's called the "N×M integration problem": Before MCP, every AI application required custom connectors for each data source, which created exponential integration overhead as systems grew. With MCP, you implement one client, and any MCP server becomes immediately accessible.

The protocol defines three core primitives:

• Resources (data sources your model can read)
• Tools (executable functions requiring user approval)
• Prompts (pre-configured templates)

MCP runs on JSON-RPC 2.0 with support for multiple transports. You can use stdio for local servers running alongside your application, or Streamable HTTP with Server-Sent Events for remote servers.

How A2A Works

A2A uses a client-server protocol over JSON-RPC 2.0 and HTTP(S). Agents act as either clients (initiating requests) or servers (responding to tasks). The workflow follows four steps:

1. Discovery: The client requests /.well-known/agent.json to retrieve the Agent Card, which describes capabilities, endpoints, and authentication requirements.
2. Authentication: If required, the client authenticates using schemes declared in the Agent Card.
3. Task execution: The client sends a task request with work specifications and input artifacts. The remote agent processes the task internally, exposing only results, not logic.
4. Completion: A2A returns final status and output artifacts. Progress updates use polling or Server-Sent Events streaming, while long-running operations support webhook-based push notifications.

How MCP Works

MCP operates through three layers: the Host (your AI application), the Client (connection manager), and Servers (programs exposing tools, resources, and prompts via JSON-RPC 2.0). The workflow follows three steps:

1. Initialization: The client connects to configured servers and discovers capabilities through tools/list, resources/list, and prompts/list requests, storing them for runtime use.
2. Execution: For resources, the client fetches content and attaches it to LLM requests as context. For tools, the LLM determines which tool to use, the host sends the invocation via JSON-RPC, and results return for the model to incorporate.
3. Synchronization: Servers push real-time notifications about capability changes, eliminating the need for polling.

MCP vs A2A: Side-by-Side Comparison

Here's how these protocols compare across the dimensions that matter for production deployments:

‍How Do MCP and A2A Work Together?

Most production systems use both protocols in a layered architecture. MCP handles vertical integration (connecting agents to tools and data), while A2A manages horizontal coordination (orchestrating work between agents).

Example: A healthcare research system uses an orchestrator to coordinate research, analysis, and compliance agents via A2A. Each agent connects to its own tools through MCP. When the orchestrator requests a literature review, the research agent queries PubMed through its MCP connection and returns results via A2A.

This separation keeps boundaries clean, so updating an agent's MCP tool connections doesn't affect its A2A communication with other agents. Start with MCP alone for simple tool access, and add A2A when you need multiple specialized agents working together.

How Should You Think About A2A vs MCP in Production?

A2A and MCP are not competing choices. They describe two different coordination layers in an agent system. MCP defines how an individual agent accesses tools and data. A2A defines how agents discover each other, delegate work, and coordinate over time. You can ship with MCP when you have one agent. As soon as your system relies on multiple agents working together, you need both layers.

Once you look at agent architectures this way, the missing piece becomes obvious: something has to make MCP reliable in production. This is where Airbyte’s Agent Engine helps. It sits beneath both protocols, providing agents with governed, continuously updated data through MCP, while staying neutral to how those agents coordinate through A2A. 

Agents collaborate by exchange of outcomes, which eliminates the need to reimplement data access logic or hardcode brittle integrations.

Talk to us to see how Airbyte Embedded supports MCP-based data access and A2A-driven agent coordination in real production systems.

Frequently Asked Questions

Can I use MCP and A2A together in the same system?

Yes. MCP handles tool and data access inside each agent, while A2A manages coordination between agents. In practice, most multi-agent systems use MCP for vertical integration and A2A for horizontal orchestration.

What are the enterprise security differences between A2A and MCP?

MCP includes defined security mechanisms such as TLS/mTLS, OAuth 2.0, API keys, and per-client consent to reduce confused-deputy risks. A2A leaves authentication to implementers and relies on agent opacity for IP protection, which means teams must add their own validation, trust boundaries, and abuse protections.

Do I need Google Cloud to use A2A?

No. A2A is platform-agnostic and runs anywhere you can deploy containers or Kubernetes. Google Cloud provides managed tooling and hosting options, but it is not required by the protocol.

How long does it take to implement MCP versus A2A?

MCP usually reaches a first working setup in under an hour, with production readiness in a few weeks. A2A takes longer, typically several hours to prototype and over a month to harden for production.

What if my agents need both coordination and tool access?

That is the default production pattern. Use A2A for agent-to-agent coordination and MCP inside each agent for tool and data access. This keeps collaboration logic separate from data plumbing and easier to evolve over time.