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A2A vs MCP: The Race for the Protocol of AI Agents

Taylor Ye

• 4 min read
A2A vs MCP visual: A2A shows agent-to-agent chat; MCP shows an agent using tools.

As artificial intelligence progresses beyond monolithic models and isolated capabilities, a new paradigm is emerging: modular, agent-based systems. These systems are designed to decompose tasks into subtasks, communicate across components, invoke tools, and access memory or APIs in a controlled and intelligent way. Two of the most promising frameworks advancing this frontier are Google DeepMind’s A2A (Agent-to-Agent Protocol) and Anthropic’s MCP (Model Context Protocol). Though they aim to solve overlapping challenges in multi-agent orchestration and modular AI architecture, they take fundamentally different approaches.

In this post, we will explore what A2A and MCP are, how they work, their architectural philosophies, and what their competition reveals about the future of general-purpose, agentic AI systems.

A Comparative Examination of A2A and MCP

Google A2A: The Agent-to-Agent Protocol

Google’s A2A protocol was introduced as part of DeepMind's efforts to develop collaborative AI agents capable of performing complex reasoning through communication and delegation. It is a communication and task-coordination standard that allows agents to:

  • Autonomously delegate subtasks to other agents
  • Maintain their own execution environments and memory
  • Pass structured requests and responses in a format optimized for traceability and recursion
  • Operate asynchronously, completing subtasks independently and in parallel

Architectural Concepts

The A2A protocol represents a decentralized approach where each agent has autonomy. Communication is handled through structured messaging that includes:

  • A task schema (containing objective, parameters, context)
  • Dependencies and outputs
  • Success/failure signals

Agents can chain together or recursively call new agents, building a kind of task tree or DAG (Directed Acyclic Graph) for solving problems collaboratively. These agents can be instantiated dynamically, promoting composability and reuse.

Use Case Example

Consider an AI assistant asked to plan a multi-leg international trip. Using A2A:

  • One agent handles flight bookings
  • Another handles visa requirements
  • A third agent books accommodations
  • Each agent can spawn subtasks (e.g., flight agent asks a currency converter agent to calculate foreign exchange rates)

By decoupling roles and responsibilities, A2A allows AI systems to mirror human organizational structures.

Anthropic MCP: The Model Context Protocol

While A2A focuses on agent communication, Anthropic’s MCP (Model Context Protocol) is designed around context management and model invocation. It structures how models (such as Claude) interact with external tools, data sources, and memory systems. You can find more details in our previous article.

MCP is tightly integrated with Claude's architecture, particularly in the Claude Desktop interface. The main idea is to treat the AI model as a reasoning engine that responds to well-formed contextual prompts, where everything from user identity to memory history is part of the payload.

Architectural Concepts

MCP is built around:

  • Host/Client Architecture: The "host" (e.g., Claude Desktop) controls the interface and the session state; clients invoke specific tools, retrieve files, or perform system-level operations.
  • Context Blocks: Every model query includes structured data such as:
    • Identity (user ID, agent name)
    • Intent (purpose of query)
    • Tool state and capabilities
    • History and memory (chat logs, prior results)
  • Transparent Operation Chains: The steps taken to complete a query are logged for interpretability.

For instance, if the user asks, "Find the latest Q1 financial report and summarize it," MCP:

  1. Queries the local file system via a tool connector
  2. Passes the result back through the host
  3. Constructs an updated context
  4. Sends the new context back to the Claude model to generate a response

Each transition is modular, logged, and separable.

Benefits

  • High interpretability: MCP records context at every step
  • Clear tool invocation pipelines: Models are not black boxes
  • Separation of duties: Tool execution is not mixed with reasoning
  • Alignment and safety: Traceable actions help prevent model hallucination or tool misuse

Head-to-Head: A2A vs MCP

Let’s compare the two protocols directly:

Table comparing A2A and MCP by features like architecture, tools, and use cases.

The following diagram illustrates how modular AI agents operate within distinct system architectures and collaborate through standardized communication layers. Each agent comprises internal components such as large language models, orchestration frameworks, or development kits. The A2A protocol facilitates communication and coordination between agents across different systems, while the MCP protocol provides a structured interface for agents to interact with external applications and APIs within their local environment.

The distinction between A2A and MCP is clear: A2A focuses on agent-to-agent interaction, whereas MCP is designed for agent-to-application integration.

Diagram of agents using A2A protocol and MCP to connect with APIs and apps.

Source: Google

Strategic Vision and Future Outlook

Despite their differences, both A2A and MCP are trying to solve the same big problem: how to build AI systems that are modular, explainable, and capable of performing complex tasks by breaking them into composable, manageable components.

Google is betting on a fully decentralized AI future where autonomous agents interact like servers in a cloud-native mesh. The focus is on delegation, recursion, and independence, which is excellent for large-scale simulations, dynamic environments, and long-term planning. Think of A2A as a foundation for AI operating systems where agents are services in a network.

Anthropic takes a controlled modularity approach. Instead of emphasizing autonomy, it prioritizes alignment, traceability, and safe execution. MCP is perfect for enterprise-grade deployments where regulatory concerns, user privacy, and system reliability are paramount.It’s like building a secure OS kernel that knows everything going in and out, ideal for assistant models, personal productivity tools, and AI copilots.

Final Thoughts

Rather than viewing A2A and MCP as direct rivals, it is more productive to consider them as complementary components within a broader AI ecosystem. In fact, a sophisticated AI system could effectively integrate both frameworks. MCP could be used internally to manage context, tool access, and safety constraints for each individual agent, ensuring secure and coherent reasoning. 

At the same time, A2A could operate externally to facilitate coordination across multiple agents, manage distributed workflows, and execute decentralized plans. This layered approach is particularly relevant as the field advances toward multi-agent orchestration frameworks, such as OpenAI’s AutoGPT or LangChain Agents. Looking ahead, the infrastructure supporting general-purpose AI will likely require both safe, context-aware reasoning provided by MCP and the flexible, modular orchestration enabled by A2A.

Each has its strengths. A2A shines in general intelligence research, complex multi-agent planning, and simulating human-like collaboration. MCP excels in structured applications, assistant tooling, and AI safety.

For developers and researchers building the next generation of intelligent systems, understanding these protocols is not optional, it’s essential.And in a world heading toward increasingly autonomous AI ecosystems, these protocols may be the most important infrastructure decisions we make.