Agent2Agent Protocol (A2A), clearly explained (why it matters)

Key Concepts

• A2A (Agent-to-Agent) Protocol: A new standard developed by Google for AI agents to communicate with each other, regardless of their framework, company, or API.
• MCP (Model Context Protocol): A protocol that facilitates connecting tools and data to AI agents.
• Agent Card: A JSON file that acts as an AI agent's business card, announcing its identity, capabilities, and communication details.
• A2A Server: The live AI agent running on a network, processing requests and providing results.
• A2A Client: A program or agent that initiates requests to an A2A server, handling communication and data exchange.
• A2A Task: A single unit of work or request handed off to an AI agent, tracked throughout its lifecycle.
• B2B (Business-to-Business): A product or service sold to other businesses.
• Vectal: An all-in-one productivity app with AI agent capabilities.

A2A Protocol Explained

The A2A protocol is a new standard released by Google designed to address the fragmentation of AI agents across different frameworks, companies, APIs, and tools. It enables seamless communication between AI agents, acting as a common language. This protocol allows AI agents built with different technologies to interact with each other, enhancing scalability and future-proofing.

Key Points:

• A2A allows a main AI agent to communicate with remote agents from other companies, tools, and frameworks.
• It simplifies communication by requiring each agent to expose an HTTP endpoint and a JSON card.
• A2A complements MCP, with A2A focusing on agent-to-agent communication and MCP focusing on connecting tools and data to agents.

Example:

If you build an AI agent compatible with A2A, it can call agents from JetBrains, Cohere, or Deoid, leveraging their AI capabilities.

A2A vs. MCP

While MCP focuses on connecting tools and data to AI agents, A2A focuses on connecting agents with each other. MCP uses a server to connect a source agent to various tools and APIs, while A2A uses a protocol to enable direct communication between agents. These protocols are complementary, making AI agents more powerful and easier to build and distribute.

Visual Representation:

The video uses a graphic to illustrate how A2A and MCP can work together. An AI agent using A2A can contact other AI agents, while an MCP server allows the agent to access prompts, resources, and tools.

Four Key Concepts of A2A

To understand A2A, there are four key concepts:

1. Agent Card: A JSON file that announces the agent's identity, capabilities, and communication details. It allows automatic discovery of the right agent for a task.
2. A2A Server: The live bot running on the network, processing requests and sending back results or follow-up questions.
3. A2A Client: A program or agent that wants something done, reading the agent card, packaging requests, and collecting answers. It acts as a bridge between users/systems and agents.
4. A2A Task: A single to-do item handed off to an agent, tracked throughout its lifecycle (submitted, in progress, finished).

Building an A2A B2B MCP AI Agent

The video demonstrates how to build an A2A B2B MCP AI agent using Google's open-source repository.

Steps:

1. Clone the Repository: Clone the A2A repository from GitHub into a local setup.
2. Activate Conda Environment: Activate a Conda environment for A2A.
3. Install UV: Install the UV package manager using conda install -c conda-forge uv.
4. Update Agent Prompts: Modify the prompts of the Crew AI and Google ADK agent files to provide concise and detailed responses, respectively.
5. Get Google API Key: Obtain a Google API key from Google AI Studio.
6. Export API Key: Export the API key as an environment variable or create a .env file with the key.
7. Run Agents: Start the Crew AI agent and Google ADK agent in separate terminals using uv run ..
8. Test Agents: Verify the agents are running by accessing their JSON descriptions in a browser (e.g., localhost:10001/.well-known/agent.json).
9. Set Up UI: Clone the demo UI from the repository and configure it with the agent addresses and API key.
10. Add Agents to UI: Add the running agents to the UI by providing their local host addresses.
11. Test Communication: Initiate a conversation in the UI to test the communication between the agents.

Example:

The video showcases two agents: an image generator (Crew AI) and a reimbursement agent (Google ADK), communicating through the A2A protocol.

The Future of AI Agents

The A2A protocol represents a major breakthrough in the field of AI agents, enabling seamless communication and collaboration between different AI systems. While it is still in its early stages, it has the potential to revolutionize how businesses operate and how AI agents are developed and deployed.

Key Takeaways:

• A2A is a foundational technology that will shape the future of AI agents.
• It allows AI agents from different frameworks and companies to communicate with each other.
• Understanding and adopting A2A early can provide a significant advantage in the AI field.

Vectal Promotion

The video promotes Vectal, an all-in-one productivity app with AI agent capabilities, emphasizing its ability to save time and enhance productivity. It encourages viewers to switch to Vectal and upgrade to Vectal Pro for access to advanced AI models and features.