---
type: raw-transcript
title: "Google is Quietly Revolutionizing AI Agents (This is HUGE)"
youtube_id: ywMWpmOOaSo
url: https://www.youtube.com/watch?v=ywMWpmOOaSo
slug: google-is-quietly-revolutionizing-ai-agents-this-is-huge
published: 2025-04-16
duration: "0:23:22"
fetched_at: 2026-07-21
immutable: true
---

## Transcript

**[0:00:00]** Google recently introduced their agentto aagent protocol which is a standard for agents to communicate effectively with each other. It's very similar to how MCP is the standard to connect agents to tools. In fact, you could call MCP an agentto tool protocol. And just like MCP, A2A is very revolutionary and not

**[0:00:21]** getting the attention that it deserves with its initial launch. Plus, both are very complimentary. More on that later. MCP did not blow up right away. It took time for people to realize its true power. But just look at the interest over time compared to other technologies like Deepseek and Manis. It's clear that

**[0:00:39]** MCP was never a hype train that people just gradually realize over time that it's the real deal. And A2A was released very recently, but it's already looking like it's going to follow a similar path. And it makes sense. For more technical protocols, it takes time for people to grasp them. and for the

**[0:00:57]** creators to perfect and simplify things enough for wide adoption. That's what's happening with A2A and it is going to be a big deal. So, right now, let's cover what A2A is and what makes it so powerful. It's worth investing the time to learn this now because this is the

**[0:01:13]** future of AI agents and how they're going to communicate with each other. Plus, I do have some concerns with A2A and other protocols like MCP. So, I'll cover that at the end of this video. It's definitely worth talking about that. But for now, let's get right into A2A. So, the first thing that I want to

**[0:01:29]** show you is the announcement post from Google introducing A2A. It's very short and sweet, just introducing the protocol at a high level. And the thing that stands out to me the most is the number of partners that they already have on board with A2A. Salesforce, Accenture MongoDB Neoforj Oracle Langchain so

**[0:01:47]** many companies, a lot of these AI specific. It's really cool to see them all on board. There's a lot of stock behind A2A already. One of the big reasons that I think it certainly has legs and most of this article is just a bunch of testimonies from their

**[0:02:01]** partners, which is cool to see, but it also means that we don't really get that many details for A2A. It's super high level and kind of vague, honestly. I mean, a lot of people don't even know what interoperability is. And so, a lot of this reminds me of the article that

**[0:02:16]** we first got from Anthropic introducing the model context protocol. This was given November 25th of 2025, but we all know that MCP didn't really blow up until maybe around March of 2025. And so clearly a lot of people read this higher level kind of vague article similar to

**[0:02:33]** A2A and they just completely glossed over it. It wasn't until we had the official documentation from MCP and things were explained in more layman terms like it's the USBC port for AI applications that people really started to latch on to the concept of the MCP protocol overall. And honestly, I think

**[0:02:52]** I should have covered MCP a lot earlier on my channel. I know that people like you trust me with your time to keep you up to date with the latest AI technologies and how to leverage them effectively. And I feel like I dropped the ball with MCP. I covered it way

**[0:03:08]** later than I should have. And so right now with A2A, I'm making sure that I don't miss out on that. This is a very important technology and there's still a lot to be developed out for it. But I want to introduce you to it now because it is a gamecher. So to help you

**[0:03:22]** understand the true power of A2A, I want to give you a crash course really quick here with a theoretical example and then I've got a bunch of benefits of A2A listed on the right hand side. So I'm not going to dive into all of these in detail. You can read these here if

**[0:03:35]** you're curious, but there are a couple that I want to focus on. But first, let me explain this example. So, something that is very common with agent architecture in general is to have many specialized agents that are all working together. And the reason for that is just think of humans working together.

**[0:03:50]** It's always beneficial to distribute responsibility between many people so they can all be very focused. And it the same thing applies to agents. And so like in this example, we have a sales agent that can interact with CRM tools to do something in your CRM, maybe HubSpot or Salesforce, go high level

**[0:04:09]** whatever that might be. And that is through MCP. More on that later. But yeah, this also kind of shows you a little sneak peek of how we can use MCP and A2A together. But that's our sales agent. But then it can also call into these other specialized agents like our

**[0:04:21]** data analytics agent or finance agent when the question from the user would require one of these agents more than the sales agent itself. And this kind of communication has always been available to us. I mean similar to MCP where we've always been able to give tools to our

**[0:04:36]** agents before MCP. We've always been able to connect agents together before we had A2A. But A2A just makes the entire process more accessible and standardized. And that's very powerful for quite a few reasons. The first one is that we can have these agents all built in completely different ways. Like

**[0:04:57]** maybe our sales agent is built with Langraph and then our finance agent is built without a framework at all. And then our data analytics agent is built with crew AAI. They can be hosted in different parts of the cloud from different vendors. as long as they're all following the A2A protocol, they can

**[0:05:11]** communicate with each other seamlessly. And we don't have that kind of flexibility without a protocol like A2A. And then the other benefit that I want to focus on right now out of all of these in this list is think about what we have without A2A. Usually when we

**[0:05:28]** connect agents together, we take the functionality of the finance agent and then we program some sort of integration into the sales agent to leverage that. But the problem with that is we have to know ahead of time within the sales agent how we interact with the finance agent and what it is capable of doing.

**[0:05:45]** So as soon as we update this agent, we are at a very high risk of completely breaking the integration of the finance agent within the sales agent and that is a problem. We need something much more dynamic and that is what A2A gives us. It gives us the concept of agent

**[0:06:01]** discovery where the sales agent in real time can learn what the finance agent is capable of and how to interact with it so that it can make that determination at runtime. And so then when we update the finance agent, we have much less of a risk of breaking the integration with

**[0:06:17]** the sales agent because everything is just much more dynamic. So, those are the two big things to hit on, but there really is a big list here, and we'll dive into a lot more of these things throughout the video, but I hope that this can just help you see at a high

**[0:06:29]** level the insane amount of benefits of using a protocol like A2A. Next up, we have the GitHub repository for A2A. This is where you can really start to dive into the details behind this protocol. And since this is a GitHub repo, we know that A2A is 100% open- source, which is

**[0:06:47]** crucial for any protocol to be widely adopted at all. So already a win here. And then scrolling down in the readme we get to the conceptual overview where we can start to dive into all the different components for the A2A architecture. And so, as we know, it's a

**[0:07:02]** protocol for agents to communicate with other agents. And the first big component to that is we need a way for agents to know what they can do with other agents. And we do that through what's called an agent card. And honestly, this is a long time coming. I'm surprised that there hasn't been

**[0:07:18]** another protocol that's implemented something like this. It's so powerful to have this way for an agent to describe its capabilities, how to interact with it, any kind of authentication requirements to other agents so they know how to work with each other all through a single metadata file. Very

**[0:07:36]** very powerful. And the way that agents communicate with each other and the agent card is a part of this is we have agents that run as servers and as clients. And so this is very similar to a micros service architecture if you're familiar with that. You just have all

**[0:07:51]** these agents that are running as API endpoints. They're each individual nodes that are all connected to each other and they use the agent cards to understand how to interact with each other. And so your agents that are running on servers it's just an HTTP endpoint that's exposed for other agents or other users.

**[0:08:09]** Like you can have an application if you want to interact directly with an agent as well. And then the clients, it's just you or another agent and you just consume an A2A service as in you call into one of these servers. And the way that you do that is with tasks. And so

**[0:08:24]** you generate an identifier for your task that has your request to the agent and you know what kind of requests you can make based on the agent card. And then you just send that into the agent server. So you have your message, any other components to your request and

**[0:08:39]** then you just get a response back. It's very simple, very similar to just standard API operations and they there's support for push notifications. So you can have your agents that are running in servers update the other agent clients in real time which is one of the more technical but also one of the more

**[0:08:57]** powerful parts to this protocol. And then that brings us into what a typical flow looks like for agents to interact with each other. But to make this crystal clear for you, I took this text and some other stuff from their documentation and I distilled it into a couple of beautiful diagrams that I want

**[0:09:13]** to share with you now. So with all the concepts explained from the GitHub repo I hope this diagram makes a lot of sense to you. So we start with our client agent and it's of course going to start by fetching the agent card from the A2A server, which is our other agent that

**[0:09:26]** we're communicating with. It'll return the agent card. So we now know what we can do with this second agent. And so first we'll generate a task ID. This is just a unique identifier for the request that we're about to make. And then we'll send that ID in along with the JSON

**[0:09:42]** payload for the request that we want to make. So knowing what we can do from the agent card, we'll form some sort of request like we maybe wanted to send an email or summarize some text, whatever this agent is capable of. And then that agent will process the task and then

**[0:09:56]** return the results of executing that task along with some other metadata like if the request was successful or not. And that's it. That's A2A at a very basic level. It's not that complex overall. It certainly feels complicated when you try to really dive into the GitHub repo, but really this is just an

**[0:10:13]** architecture. And that's the other really important thing to clarify with A2A. It is not a tool that you download. You don't pip install it like you would with lang chain or crew AI or pinantic AI. It's just a highlevel architecture and all the code in the GitHub repo is

**[0:10:28]** just examples for how you can build agents in a way that fits with the A2A protocol. And that also means that it's a more important skill to learn in general. Like when you learn from A2A how to build agents that communicate with each other effectively, you can apply that to your own architecture that

**[0:10:44]** you would make or you could build on top of A2A. It doesn't matter if A2A goes anywhere because anything in the end that standardizes the way that agents communicate with each other, it's going to look very similar to A2A. And then I already alluded to this at the start of

**[0:10:57]** the video, but you can use MCP and A2A together. They are very complimentary to each other. And the reason for that is they operate on different layers of the agent architecture because MCP is agents to tools, right? And then A2A is agent to agent. And this diagram shows you

**[0:11:15]** very clearly what that looks like. So we have our client agent that's running on the left hand side. This uses the A2A protocol to call into some other agent that we have running as an A2A server. And this second agent that's running in the server, maybe it's using the Brave

**[0:11:32]** MCP server to give the tools for it to search the web. And so we have a web search request that comes in from the first agent that goes into our second one which then uses the Brave API through MCP to search the web and get some results back from the web search.

**[0:11:47]** And then our server agent will reason about that, craft that final response to fulfill the task that our client initially gave. And so we're using A2A at the higher level to call into one agent and then that one agent is using MCP for its tools. Side note really

**[0:12:04]** quick. I don't know if you realize this but with A2A and MCP, we have our entire backend built out. We have our servers and agents with A2A, and then we have the tools for our agents with MCP. The only thing we're missing for a full AI application here is the front end to

**[0:12:20]** wrap around everything that we've got going on with A2A and MCP. So, we just need a standard, a way to easily create our frontends as well. And I have you covered for that because we have Lovable. This is my solution to build out almost all of my front-end

**[0:12:35]** applications for everything that I'm doing with AI. And they are sponsoring this video, but I reached out to them specifically for this because it is my genuine recommendation. And I'll even show you that I used it to build out my landing page for Dynamis, which is my AI

**[0:12:50]** early adopter community that I've started. Obviously, I care about making this website perfect. And I chose Lovable to do that. And if you look at my conversation, just look at how long this is with all of the work that I put into perfecting this landing page. And Lovable handles it all. And it handles

**[0:13:05]** it very, very gracefully. And so it doesn't matter if you're a developer looking to code faster, you're an entrepreneur that doesn't have the technical know-how, but you have all these good ideas that you want to build out, or you're a designer that wants to take your designs and things like Figma

**[0:13:19]** and turn it to life with a full website. You can do it all with Lovable. And that really is the last component to combine with things like MCP and A2A to build an end-to-end AI application. It's a great thing. So I always love to give very concrete examples when I cover anything

**[0:13:34]** on my channel. And obviously with A2A it's no exception. And so what I've done here in Python, I've created a very basic implementation of a server and client following everything with the A2A protocol. I'm not importing anything from A2A because it's again not a tool. It's an architecture. So I'm just

**[0:13:52]** building everything out following all the practices laid out in the GitHub repository. And this is a very basic implementation because I want to cover the core concepts that are important. It's much simpler than anything you'll find in the GitHub. So look at those examples if you want a full full

**[0:14:07]** implementation. But if you want to just understand the important parts, I've got you covered with this, including how you can use MCP within the A2A agent servers. So a lot of value packed in right here. So the first thing that I'm doing is just defining my Brave MCP

**[0:14:22]** server with the Pyantic AI integration. I don't want to dive into Pantic AI here, but that's just the framework I'm using to build this agent. And then I just add it as an MCP server to my agent definition. So now my Pantic AI agent can use the Brave MCP server to search

**[0:14:37]** the web. Nice and simple. And then the next thing is I need to define my agent card. And so I just followed the specifications on GitHub for this. a very basic card where I'm just giving the name, description, how to reach this agent, its version, and then describing

**[0:14:51]** its capabilities at a very high level. Not really giving it any because I want it to be simple. But yeah, this is a very basic agent card. And then in the standard endpoint, we give the ability for other agents to call this to fetch the agent card and know what this agent

**[0:15:06]** can do. Again, everything that we've already covered within those diagrams. And then the other endpoint that we have is the one to handle all of the tasks. And so any client is going to call this endpoint and then generate a task and call this endpoint. So we fetch the ID

**[0:15:21]** of the task. Also the user text, this is basically just the text for the request. And then we call our Pantic AI agent to handle that and it can use Brave to search the web if the user is requesting something that would elicit that. And then we just get the response from

**[0:15:35]** invoking our agent. And we build up this JSON body which represents what we're going to return as a response to the task request just saying that the task was completed successfully and then passing in the results of calling that agent. It's very nice and simple. And then we just have this as an API

**[0:15:52]** endpoint running on port 5000. So overall, it's a pretty standard API implementation just with a couple of things that are specific to what we need for the A2A protocol like the agent card and then the specific endpoint to handle task requests that are coming in. But it is very similar to just a standard AI

**[0:16:10]** agent that's running behind an API endpoint. And then for my client, I just wanted to make this as simple as possible for this demo. And so I show you what it looks like to fetch the agent card. I don't actually use that to figure out which kind of task I can run.

**[0:16:24]** So I'm just hard coding this here, but this is how you can just fetch the agent card just hitting the agent.json endpoint. And then to build up a task request, we just generate an ID like this. And then we build up our payload. So just like we have a JSON response for

**[0:16:38]** the task, we also have JSON for the input. So we have our ID and then we have our messages which we're just sending in the request from the user which in this case I just have a very basic question. What is Google A2A? Which is the kind of thing that an LLM

**[0:16:51]** wouldn't know because of its training cutoff. And so it will have to use the Brave MCP server to answer this question to fulfill my task request. And so I build up the task payload. And then I just send it in like this. Really nice and simple, very typical API stuff. And

**[0:17:06]** then I just handle any errors that might come up. And then if we have a success which we're always going to get from this server because it's a very simple implementation, just always returning uh completed. But as long as it's completed, then we'll print out the agents reply. And so the way that this

**[0:17:20]** works in my terminal is in one tab I have this server running. So I just ran python server.py. We've got the server running on port 5000. And then in my other tab, this is where I'll run my client. And so the command is very simply pythonclient.py. And so this is going to

**[0:17:37]** take that question. What is Google A2A? It's going to generate a task ID and then send that task into my API endpoint. You can see that we got that first request to get the agent card. And then once it processes, yep, there we go. We got the final result from sending

**[0:17:52]** in the task as well. And then back in our client, we can see that we got a response for what Google A2A is. All right. If it's for agent interoperability, I'm trying my best to say that word correctly. Um, and yeah we we see that it's got support from

**[0:18:06]** partners. A lot of these things that it's pulling from, yeah, the Google developer blog. It's clearly using Brave to get this response. And so there we go. That's A2A in a very basic sense. also leveraging MCP for the tooling. So I know at this point I've only been

**[0:18:20]** focusing on the positives for these protocols, and for good reason. They're super powerful, and they definitely have a bright future, but there are also a lot of issues and concerns that I have with them that I certainly owe to explain to you right now. There's a lot of work that has to go on to really make

**[0:18:35]** it so that these protocols are production ready and that they're ready for wide adoption. And so it's going to be doom and gloom for a couple of minutes. So just bear with me on this but then I'll lighten it up at the end because there certainly are solutions to

**[0:18:46]** all the problems that we have here. So the first big problem is testing complexity. Just think about this. If you're building an agent allin-one codebase, you're not using MCP and A2A. Everything's nice and simple. It's in one place. It's going to be very easy at least, relatively,, to, build, out, your

**[0:19:03]** unit testing, your integration testing just make sure everything is reliable. But when you have all of these nodes running in the cloud or wherever for your different A2A servers and MCP tools, it becomes a lot more complex. And if you're familiar with microservices, you know about edge case

**[0:19:18]** explosions where there's just so many different nodes where you can have problems now. And it's really hard when you encounter these issues to also reproduce them because there's just so many components to your system now. And you have to add in the fact that all of these different nodes are relying on

**[0:19:33]** LLMs now and they're also not predictable. And so you might run into a problem that's just caused because an LLM hallucinated. And so maybe you don't have to worry about it, but also it's always going to be on the back of your mind that this thing failed once because

**[0:19:44]** of the LLM. Who knows if it's going to happen again? It can be very stressful and hard to engineer well for these challenges. And then we also have security concerns because when you have all of these nodes for your servers and your tools, there's just an increased surface area for any kind of cyber

**[0:20:01]** security attack. And with these protocols, the dream is to have it so other people can build these MCP servers or A2A servers that you leverage. But that also means that you're just sending all of your data into more third parties. So it's not just going to the OpenAI API now. It's also going to

**[0:20:18]** OpenAI and whoever is hosting that A2A server. And so you have to be even more concerned about all these other people that are touching your data. And there's also a lot of authentication challenges with making sure that that one request that's coming from the agent or the client, whatever it is, is carried

**[0:20:34]** through the entire AI system with all of these sub aents and tools. There's so much engineering challenge that goes into this. And then you also have hidden complexity. So things become more of a black box when we rely on these protocols because maybe we don't entirely understand how A2A works or we

**[0:20:52]** don't know all the code behind MCP. And so we're building solutions around things that we don't understand fully. And so it makes it very hard to debug things sometimes when they go wrong because it might be just the way that we're interacting with MCP is wrong because we just understand how to work

**[0:21:08]** with the protocol incorrectly. So it can be tough. And also when we have these distributed systems, error attribution can be very difficult. If you don't have very good logging and monitoring and tracing set up, it can be impossible to know when one node fails. Well, which node is it? in this entire AI system

**[0:21:26]** what actually went wrong and the accountability can be difficult as well. Because think about it, if an MCP server fails to use a tool for whatever reason it might actually be because the agent gave the wrong parameters, not because the tool is actually broken. And so it can be difficult to hold the right node

**[0:21:44]** accountable for your entire AI system. So many problems with this. And like I said, there are solutions to all of this. Google is working on things for authentication. A lot of these issues really I think Anthropic is working on a lot of these things for MCP and we know

**[0:22:00]** from past engineering problems with things like databases and microservices like you can solve for edge case explosion. You can solve for being able to debug these systems even when you have so many different components. It's definitely possible. So that's everything that I've got for A2A. And I really think this protocol is going

**[0:22:19]** places. Google has done a great job laying out a foundation that just needs to be built out to tackle all these issues that I just went over. But I think that's going to happen. We're going to get to the point maybe in a year, maybe in two years where we can

**[0:22:31]** use these protocols like MCP and A2A to build these AI systems very easily that can scale, that are secure, that can handle all these different requests and be tested easily. There's a lot of work that will need to go into doing that but I think it's going to happen. So

**[0:22:45]** let me know in the comments what you think of A2A. Are you going to use it to build out your own AI agents? I'm curious what you think of everything the good, the bad, and the ugly. I certainly think it's going to create a new standard for AI agents going

**[0:22:57]** forward, but it's going to take a long time for it to be widely adopted especially because of all of these different issues that Google and other companies are going to have to address. It's definitely going to get there though, and I'll keep covering these protocols as things progress. So, if you

**[0:23:14]** appreciated this video and you're looking forward to more things AI agents, I would really appreciate a like and a subscribe.
