Google has introduced a new protocol called A2A which stands for agentto agent protocol. It enables communication between two agentic applications or between two agents. And the crazy part is that this protocol can connect AI agents from any framework whether it's Langchain, Crew AI, Google's ADK or even customuilt systems. A2A runs on a shared protocol built on HTTP. This protocol is honestly wild and the kind of applications and implementations we're going to see from it are going to be mind-blowing. Just like MCP, it's expected to gain a lot of traction. As more applications are developed with it, the momentum will only grow. The best part is that it doesn't replace MCP. Instead, it works alongside it and both can be used together to build powerful systems. Let's jump into the video and see how it all works. Right now, I'm going to show you an official demo from Google. This is exactly what they demonstrated. It starts with the end user which is you. The client in the demo is Google agent space but it could be any client. It begins with a single agent called the remote agent. Based on the task you give it, this agent looks for other agents to hand the task over to. This is where the A2A protocol steps in. It enables smooth communication between two AI agents. Now what exactly is an AI agent? They're just large language models with a set of tools. These tools define what the agent can do. In this new protocol, every agent has an agent card that describes its abilities. The remote agent reads the agent cards of other agents and picks the one best suited for the task. That agent can then pass the task on to another agent, creating a chain. This forms a multi- aent workflow. This is how A2A makes the process much easier. I'll show you in the GitHub repo how it works. You can connect agents from any framework and once this protocol is in place, those agents can talk to each other without friction. Now this is the demo that they have shared. The example they've provided shows the agentto agent protocol being used to hire a software engineer along with a job description. You can clearly observe how the A2A protocol functions. First the protocol is initiated which is visible in the thinking process. Then to discover different agents suitable for the task, it examines their agent cards. These are the main source for understanding the capabilities of each agent. There are several ways to explore agent cards. In this case, there's a tool registry where it finds the sourcing agent and initiates a call to it. You can see here that additional constraints have also been provided to the agent to find the best possible candidate. Once the sourcing agent completes its task, we can see it identifies five candidates for the job. If you continue watching the video, you'll notice that two weeks later, after the interviews are done, the agent is used again to gather updates and perform background checks. Based on the candidate data, the system is capable of running a background check on a single candidate. This entire process of hiring a software engineer based on a job description is automated using these agents. The most important thing is that this wasn't handled by a single agent. The A2A protocol allowed multiple agents to work together. all communicating through a single protocol. Let's clear up another concept about the agentto agent protocol and MCP. A2A is meant to work with MCP and even Google has confirmed this. To explain why, think of it like this. MCP is an LLM with tools or access to specific data. Picture it as a repair man. He has a screwdriver and the knowledge to fix cars. That's the MCP part. But this repair man also needs to talk to others. Maybe he needs to speak with clients or borrow parts from someone else. That's where the agentto aagent protocol comes in. It allows agents to communicate with each other. These agents could even be separate MCP servers acting as independent agents. They can share tools or request help when needed. The key to all of this is the agent card. It defines what each agent is capable of and helps them interact in a structured way. They've explained a clear connection between A2A and MCP. In their official documentation, it's outlined that future Agentic applications will need both of these protocols to become more capable. As an example, they use the same auto repair shop analogy. There are multiple handyman sub agents working in the shop. They need tools, but to use those tools properly, they also need extra context from customers. Those customers could be other agents. The interesting part is how MCP fits into this setup. We know that to identify an agent using the A2A protocol, it must have an agent card. These agent cards can be listed as resources. The MCP server can then provide access to them. The LLM fetches these agent cards and passes them to sub agents. Sub agents read the cards and based on the information, the LLM decides which external agent should be used. It's a clever integration and shows how both systems can work together in a flexible way. The agent card structure is clearly defined in their documentation. It includes key information about the AI agent such as the version, name, description, and its intended use. Then it lists the skills which are the core capabilities the agent can perform. It also shows the default content type the agent supports along with the parameters it accepts. Basically, it shows the kind of input the agent needs. For some agents, authentication might be required. So, specific document details would also be part of the agent card. When an LLM or another agent tries to access this agent, it first reads the agent card. Based on that, it decides whether to use the agent and how to interact with it. This makes the accuracy of the agent card critical to how the entire agentto agent system functions. Further in the documentation, they've provided sample agent cards and methods for sending tasks and receiving responses. One of the examples is a Google Maps agent. The card includes a clear description of the tasks it can perform along with the URL and provider details. It also specifies the type of authentication the agent needs. Below that, there's a format showing how a client can send a task to a remote agent to start a new job. In one simple example, the task is to tell a joke. The response comes back as a text output from a model which delivers the joke. This is one way to send a task and get a result. Other methods are also documented below. To get started, there's no need to memorize the syntax. You can feed the documentation into cursor with the at docs feature which will pick up the context and generate code accordingly. In the GitHub repo, they've included some sample agents that show how A2A agents can be implemented. One example is using Crew AI, a simple image generation agent that uses the Google Gemini API. You can install it easily. It's a basic agent that just runs on the A2A protocol. To get started, you need to clone the full GitHub repo because the commands depend on that structure. Once it's cloned, you can run the setup using a few simple commands. Just copy and paste them into your terminal. They're very straightforward. Once you run the command, it opens a command line interface for the A2A agent. The server passes it to the crew agent which uses the Gemini API to generate an image. That image is then returned to the server and finally back to the client. This is a simple implementation and it's not widely used just yet, but it will be. We saw the same thing happen with MCP. Adoption took some time, then it exploded. The same will happen here. Once people start building on top of it, AI agents will become incredibly powerful. They'll automate a huge amount of work and change how we use AI everyday. If you found this video helpful, subscribe to the channel. And if you'd like to support the content, check out the donation link in the description. Thanks for watching and I'll see you in the next video.