A2A vs MCP: How are they Different? - Analytics Vidhya

Agent-to-Agent (A2A) and Model Context Protocol (MCP) are two prominent AI protocols that have attracted significant interest recently. While it might seem like a choice between "A2A vs MCP," they actually tackle different aspects of AI systems. This article explores what A2A and MCP are, their distinct roles in AI systems, and how they enhance each other to facilitate seamless integration within enterprise AI workflows.

Table of Contents

• What is A2A (Agent-to-Agent)?
• What is MCP (Model Context Protocol)?
• A2A vs MCP
• Key Differences
• How They Work Independently
• Integration (Better Together)
• Misconceptions
• Complementary Strengths
• Conclusion
• Frequently Asked Questions

What is A2A (Agent-to-Agent)?

Agent2Agent (A2A) is an open protocol developed by Google that standardizes the communication and collaboration among AI agents. A2A enables different AI agents, created by various vendors or running on different platforms, to establish a common language for cooperation. Through A2A, agents can share objectives, exchange context, and trigger actions securely and systematically. The protocol is specifically designed to support multi-agent workflows that operate across various clouds, applications, or services. A2A leverages familiar web standards like HTTP, which facilitates its integration into existing IT infrastructures.

For an in-depth look at how the A2A protocol functions, please refer to this article: How A2A works?

What is MCP (Model Context Protocol)?

The Model Context Protocol (MCP) was introduced by Anthropic, the parent company of Claude, to facilitate the connection of AI agents (or LLMs) to external tools. While A2A focuses on agent-to-agent communication, MCP emphasizes agent-to-resource integration. It provides a standardized method for AI models to access external data sources, knowledge bases, and services beyond their own parameters. This is why MCP is often likened to a "USB-C port" for AI applications. Before MCP, developers had to create custom integrations for each new tool or data source, resulting in a complex network of connectors. MCP streamlines this with a single open protocol, enabling any compliant data/service connector to work with any MCP-compatible agent.

To understand how MCP operates, please refer to this article: How MCP works?

For a video explanation of the MCP protocol, please watch:

A2A vs MCP ----------

The following table outlines the distinct roles of A2A and MCP:

**Aspect**	**A2A (Agent-to-Agent)**	**MCP (Model Context Protocol)**
**Purpose**	Facilitates connections and coordination among multiple agents (agent ↔ agent)	Enables connections between agents and external tools/data (agent ↔ resource)
**Key Functionality**	Facilitates task delegation among agents; allows for context and goal sharing	Enables tool and data integration; provides real-time context to agents
**Created by**	Google (open specification with contributions from partners)	Anthropic (open specification with adoption by multiple vendors)
**Ecosystem Support**	Microsoft (Azure AI Foundry, Copilot Studio), Google, Atlassian, Salesforce, ServiceNow, etc.	Microsoft (Copilot Studio), Google, OpenAI, Anthropic (Claude), Atlassian, etc.
**Focuses On**	Inter-agent communication: security, trust, and interoperability in agent collaboration.	Agent extensibility: uniform access to data sources and tools, maintaining current context for the agent.
**Analogy**	Protocol for dialogue and teamwork among AI agents.	Universal connector for linking an AI to any data/tool it requires.

Key Differences

A2A and MCP operate in different areas of AI architecture. Here are the three main distinctions:

• Scope of Interaction: A2A links agents with each other, whereas MCP connects agents to external tools and data. Google positions A2A as a standard for agent collaboration, while Anthropic's MCP focuses on integrating agents with external services.
• Primary Function: A2A manages communication, task delegation, and state sharing between agents. MCP enhances individual agents by connecting them to external resources through a unified, tool-based interface.
• Design Principles: A2A is based on HTTP/JSON standards and supports agent discovery and secure delegation. MCP employs JSON-RPC and focuses on tool registration, data access, and real-time context provision. A2A treats agents as peers, while MCP views tools as callable services.

How They Work Independently

A2A Alone: Consider a company with specialized AI agents in areas such as finance, marketing, and scheduling. A master agent can delegate tasks like budgeting or timeline planning to other agents using A2A. Each agent then returns results through the shared protocol. Without MCP, however, each agent depends solely on its internal knowledge or pre-established connections.

MCP Alone: Imagine a support chatbot linked to live systems such as product databases, shipping APIs, and knowledge bases via MCP. This setup enables the agent to be dynamically aware and responsive in real time. Even without A2A, MCP equips it to be a tool-rich, responsive assistant. Nonetheless, it lacks the ability to coordinate with multiple agents to tackle complex or multi-step issues.

Independently, both protocols offer clear benefits. A2A facilitates modular teamwork, while MCP allows agents to gain external functionality.

Integration (Better Together)

In modern GenAI systems, A2A and MCP frequently collaborate to enable intelligent orchestration:

• Layered Cooperation: Consider MCP as the foundational layer for tool and data access, and A2A as the coordination layer that delegates tasks among agents. In a supply chain scenario, agents can retrieve inventory data, manage procurement, and oversee delivery using MCP, while A2A enables them to distribute tasks and share outcomes.
• Unified Development Experience: Microsoft's Copilot Studio exemplifies this integration. Developers can register MCP tools and connect agent workflows via A2A within a single interface. A2A manages the workflow, and MCP handles the functionality.

Misconceptions

Despite their origins from different organizations, A2A and MCP should not be seen as competing standards:

• Different Problems: A2A addresses communication, whereas MCP focuses on execution. They operate on different protocol layers.
• Complementary Functions: A2A facilitates task-sharing among agents, while MCP enables each agent to utilize tools.
• Industry-Wide Alignment: Microsoft integrates A2A in Copilot and registers MCP tools. Anthropic has open-sourced MCP and supports A2A adoption.
• No Hierarchy of Importance: Both solve essential challenges. A2A without MCP results in uninformed agents, while MCP without A2A leads to isolated agents.

The creators of both standards, Google and Anthropic, actively promote the integration of both protocols within enterprise AI workflows. Using both enables the construction of adaptable and scalable agentic systems.

Complementary Strengths

The two protocols each excel in handling specific aspects of a workflow. When used together, they complement each other effectively:

• Interoperability Extensibility: A2A connects agents across systems, while MCP makes each agent extensible. Together, they form modular, flexible ecosystems.
• Specialization Cooperation: Agents can specialize yet collaborate. MCP provides the tools, while A2A allows them to share the workload.
• Real-Time Adaptation: MCP delivers current context, while A2A reroutes tasks if conditions change. This makes systems resilient and responsive.
• Governance Observability: MCP manages tool access, while A2A governs interactions. Together, they provide traceability, compliance, and control.

Together, they bring intelligence and interoperability to generative AI systems.

Conclusion

A2A and MCP are not isolated; they are synergistic standards. Each addresses a unique challenge, but when combined, they empower agents to communicate (A2A) and act with real-world context (MCP).

Microsoft CEO Satya Nadella aptly stated:

“Open protocols like A2A and MCP are crucial for enabling the agentic web… [so] customers can develop agentic systems that are interoperable by design.”

The future of GenAI isn't about choosing one protocol over the other; it's about integrating them into our workflows. Together, they lay the groundwork for next-generation intelligent systems that are interoperable and tool-aware.

Frequently Asked Questions

Q1. What’s the difference between A2A and MCP in AI systems?A. A2A facilitates connections among multiple AI agents for communication and task delegation, while MCP connects an agent to tools and data sources for real-world functionality.

Q2. Can A2A and MCP be used together in the same system?A. Yes, they are designed to complement each other. A2A handles coordination between agents, and MCP provides tool and data access.

Q3. Who created A2A and MCP, and are they open standards?A. A2A was developed by Google, MCP by Anthropic, and both are open protocols adopted by companies like Microsoft and OpenAI.

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