What Makes Google's A2A Protocol REALLY POWERFUL (in 12 Minutes)

Key Concepts

• A2A (Agent to Agent Protocol): A protocol developed by Google that allows agents (LLMs + tools) to communicate with each other.
• MCP (Model Context Protocol): A protocol that allows LLMs to interact with tools. It's considered the "USBC for AIs."
• Agent: In the context of A2A, an agent is a combination of a Large Language Model (LLM) and tools.
• Agent Card: Metadata associated with an agent, containing information like name, description, URL, version, capabilities, and skills.
• Multi-Agent: An agent that can connect and interact with other agents to accomplish a task.
• JSON RPC: The schema used for communication between A2A client and server instances.
• Skills: Capabilities of an agent that allow multi-agents to decide which agents to use and when.

A2A Protocol Explained

Introduction to A2A and its Relation to MCP

The video introduces the A2A (Agent to Agent) protocol from Google, explaining its purpose and how it relates to the previously discussed MCP (Model Context Protocol). The speaker emphasizes the importance of understanding MCP before diving into A2A, providing a link to a previous video explaining MCP.

What is an Agent in A2A?

An agent, in the context of A2A, is defined as a combination of an LLM (Large Language Model) and tools. The agent receives a task, uses the LLM to reason about it, and then utilizes tools to execute the task. The A2A protocol facilitates communication between agents.

Example Scenario: Booking a Flight

The video uses the example of booking a flight to illustrate how A2A works. In a hypothetical scenario, a user interacts with an A2A client, which sends a request to an A2A server (e.g., an airline agent). The airline agent uses its own LLM to parse the request and its tools (search flights, book flights) to fulfill the request. The agent then sends back a response to the user.

Multi-Agent Interactions

A key feature of A2A is the ability for agents to connect with other agents. For example, a travel agent agent could connect to airline, hotel, and car rental agents to book an entire trip. This is achieved using the same A2A protocol for communication between agents.

Google A2A Repo and Demo

The Google A2A repository on GitHub contains sample code and a demo application. The demo acts as a multi-agent, connecting to multiple agents simultaneously. The speaker has built upon this demo, creating a hotel agent and a flight agent.

Demo UI and CLI Client

The video showcases a demo UI connected to the hotel and flight agents. The speaker attempts to book a trip to Maui, demonstrating the interaction between the multi-agent and the individual agents. The speaker also demonstrates the A2A CLI client, which allows direct interaction with individual agents through an interactive terminal. The CLI client returns an "agent card" containing information about the agent.

Server-Side Implementation

The speaker provides a brief overview of the server-side implementation of an A2A agent, using the hotel agent as an example. The server instantiates an A2A server with a card containing the agent's metadata (name, description, URL, version, capabilities, skills). The server uses Google's Genkit and the Gemini 20 Flash model to process incoming requests, but can be configured to use other models.

Pros and Cons of A2A

Advantages of A2A

• JSON RPC: The use of JSON RPC as the communication schema is considered a good choice, aligning with MCP.
• Agent Marketplace: The concept of an agent marketplace, where multi-agents can discover and connect with other agents, is seen as a valuable feature.
• Agent Card: The agent card provides essential information about an agent, enabling informed decision-making.
• Model Selection: A2A allows each agent to select the most appropriate model for its specific task.
• Authentication: Authentication is built into the protocol from the start.
• No Standard IO: A2A does not support the standard IO protocol, which is considered a good decision as it enables the agent marketplace concept.
• Documentation: The documentation is considered excellent at this stage of the project.

Disadvantages of A2A

• Code State: The code documentation needs improvement, making it challenging for developers unfamiliar with JavaScript and Python to set up and run the system.
• Reliability and Testing: Reliability and testing are expected to be a challenge as agent networks grow.
• Multi-Agent Hairball: The complexity of multi-agents, particularly in planning and connecting with other agents, is identified as a potential issue. Tracing prompts generated by multi-agents will require significant effort.
• Centralized Identity/Billing: The lack of a centralized identity or billing system raises questions about contract management with individual agents in a marketplace scenario.

A2A vs. MCP

Does A2A Replace MCP?

The speaker argues that A2A does not replace MCP. MCP is a lower-level standard that provides tools to LLMs. A2A does not offer the same level of granularity in accessing these tools.

Do We Need A2A if We Have MCP?

The speaker acknowledges that it might be possible to achieve similar functionality within MCP, but believes that having a specific protocol for agent-to-agent communication is valuable. A2A and MCP are seen as complementary, with MCP providing the tools that agents use within the A2A framework.

"A2A loves MCP"

The speaker concludes by stating that "A2A loves MCP," emphasizing the synergistic relationship between the two protocols. LLMs plus tools are agents, and MCP provides agents with those tools.

Conclusion

A2A is a promising protocol for enabling communication and collaboration between AI agents. While still in its early stages, it offers several advantages, including a well-defined communication schema, the concept of an agent marketplace, and the ability to select the right model for each task. However, challenges remain in terms of code maturity, reliability, and the complexity of multi-agent interactions. A2A is not intended to replace MCP but rather to complement it, providing a higher-level framework for agent communication while MCP provides the underlying tools.