How Do Google A2A and Anthropic MCP Differ?

TL;DR

Google A2A enables AI agents to discover one another, communicate securely, exchange information, and coordinate actions across enterprise platforms and applications. It complements Anthropic’s MCP: A2A focuses on collaboration between client and remote agents, while MCP supplies agents with useful tools and context. Together, the protocols support evolving agentic AI applications and large-scale multi-agent systems.

Transcript

hello guys so in this video we are going to discuss about this new protocol which is called as agentto aagent protocol which has been launched by Google we also say it as A2A protocol and uh if you remember like uh recently uh anthropic has also come up with this model context protocol itself uh so obviously Google will not be behind you know they'... Read More

Key Insights

• A2A is an open protocol from Google that allows AI agents to communicate with one another, securely exchange information, and coordinate actions across different enterprise platforms or applications.
• A2A complements Anthropic’s Model Context Protocol because the protocols address different parts of an agentic system. A2A supports communication between agents, while MCP provides agents with helpful tools and context.
• Agent discovery is the process through which a client agent identifies available remote agents that can perform required parts of a task, such as booking flights or finding hotels.
• A client agent is the agent that receives or coordinates a user’s request, while remote agents represent other services or companies and provide specialized information or capabilities needed to complete the request.
• Multi-agent travel planning works by dividing a broad request into intermediate tasks. The client agent can contact airline agents for flight prices and hotel agents for accommodation options, then choose affordable results.
• Enterprise interoperability is a central purpose of A2A because businesses need a standardized way to manage and combine agents operating across diverse platforms, cloud environments, applications, and providers.
• A2A was launched with support and contributions from more than 50 technology partners, including companies named in the discussion such as Atlassian, Box, LangChain, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UKG, and Workday.
• Collaborative AI agents can handle recurring or complex tasks by communicating with other agents. Examples discussed include travel aggregators coordinating bookings and job portals connecting with company agents responsible for candidate hiring requirements.

Questions & Answers

Q: What is Google’s Agent2Agent protocol?

Google’s Agent2Agent protocol, commonly called A2A, is an open protocol designed to let AI agents communicate with one another. It supports secure information exchange and coordinated actions across enterprise platforms and applications. Its broader purpose is to make agents built by different providers interoperable, allowing users and businesses to combine and manage them across varied platforms and cloud environments.

Q: How does the A2A protocol work?

A2A works by allowing a client agent to discover and communicate with remote agents that offer relevant capabilities. The client agent begins with a user request, identifies the intermediate tasks required, finds suitable specialized agents, gathers their information, and coordinates their responses. The travel example shows this process through separate airline and hotel agents providing options to a booking agent.

Q: What is the difference between A2A and MCP?

A2A focuses on communication and coordination between AI agents, including discovering other agents, exchanging information, and collaborating across platforms or applications. Anthropic’s Model Context Protocol focuses on providing helpful tools and context to agents. Google describes A2A as a protocol that complements MCP, so the two protocols address related but distinct needs within agentic AI systems.

Q: What is agent discovery in A2A?

Agent discovery is the process that enables a client agent to identify remote agents available for a particular task. In the travel-planning example, discovery tells the booking agent which agents can supply flight or hotel information. The client can then communicate with those agents, compare the information they return, and use appropriate options to fulfill the user’s broader request.

Q: What are client agents and remote agents in A2A?

A client agent is the coordinating agent that handles a user’s request and determines which outside capabilities are required. Remote agents are other agents that represent specialized services or companies. In the example, a booking platform serves as the client agent, while agents associated with airlines and hotels act as remote agents that provide prices and other relevant information.

Q: How can A2A support automated travel planning?

A2A can support travel planning by letting a booking agent divide a broad request into tasks such as finding flights and hotels. For a seven-day Europe trip at minimum cost, the client agent can discover airline and hotel agents, request price information, compare their responses, and select affordable options while coordinating the overall travel plan for the user.

Q: Why is A2A useful for enterprise AI systems?

A2A gives businesses a standardized method for connecting and managing agents across different platforms, applications, providers, and cloud environments. It also enables agents to exchange information securely and coordinate actions. This interoperability supports large-scale multi-agent systems in which specialized agents from different organizations can collaborate rather than remaining isolated within a single company’s technology or platform.

Q: What practical applications of A2A are discussed?

The discussion presents travel booking and recruitment as practical applications. A travel aggregator’s agent can communicate with airline and hotel agents to compare options and plan a trip. A job portal agent can similarly connect with agents from companies such as IBM, HCL, or Sapient, receive hiring requirements, and help provide candidates relevant to those requirements.

Summary & Key Takeaways

• Google’s Agent2Agent protocol, also called A2A, is an open protocol for communication and coordination among AI agents. Supported by contributions from more than 50 technology partners, it is intended to help agents securely exchange information and coordinate actions across enterprise platforms, applications, cloud environments, and providers.

• A travel-planning example illustrates A2A collaboration. A client agent receives a request to plan a seven-day Europe trip at minimum cost, discovers remote agents representing airlines and hotels, requests relevant information, compares available prices, and selects affordable flight and accommodation options while coordinating the broader task.

• A2A and MCP address related but distinct needs in agentic systems. A2A connects agents so they can collaborate on complex tasks, while Anthropic’s Model Context Protocol provides helpful tools and context to agents. Google describes A2A as complementary to MCP, making both protocols relevant as agentic AI applications evolve.