Thoroughly understand A2A—the “TCP/IP” of the AI world

Google’s recently released Agent2Agent (A2A) protocol, regarded as the “TCP/IP” of the AI world, is an open standard designed to enable AI agents developed by different platforms and developers to dialogue and collaborate across platforms and ecosystems. The emergence of the A2A protocol not only provides a general “communication rule” and “collaboration norm” for collaboration between AI agents, but is also expected to unlock infinite possibilities in the future of AI.

Since the birth of ChatGPT, AI has continued to evolve, and now, AI is not only “upgrading alone”, but also quietly learning how to “form groups to fight monsters”, and even establish its own “social network”.

Just recently, Google officially released the Agent2Agent (A2A) protocol, an open standard designed to enable AI agents to talk and collaborate across platforms and ecosystems.More importantly, this blockbuster release was obtainedMore than 50 industry giants(including Salesforce, Atlassian, Accenture, Deloitte, etc.).

The A2A protocol is no less than introducing the TCP/IP protocol to the AI world, which will unlock our imagination of the future of AI.

What is the A2A protocol?

The A2A (Agent to Agent) protocol is a new and open protocol designed to enable AI agents (Agents) developed by different platforms and developers to securely discover, communicate, exchange information, and collaborate on tasks with each other.

To put it simply, the A2A protocol establishes a common set of “communication rules” and “collaboration norms” for AI agents, allowing them to cross ecosystem barriers and work efficiently as humans.

A2A clearly defines two roles:

1. Client Agent:AI that initiates requests on behalf of users, such as your personal assistant.
2. Remote Agent:Specialized AI that provides specific services, such as airline booking AI.

Using the A2A protocol, agents can:

• Discover each other’s abilities.
• Negotiate interaction modes (such as text, forms, multimedia, etc.).
• Collaborate securely on long-running tasks.
• There is no need to expose its internal state, memory, or tools in operation.

The A2A protocol is cleverly built on top of existing and mature Internet technology standards, which makes it easier to be accepted and integrated into existing IT infrastructure.

If a single AI agent is an independent computer in the Internet world, then the A2A protocol is like the TCP/IP protocol, connecting these isolated “computers” to form a huge and intelligent “AI Internet”. In this network, information and tasks can freely flow, pass, and process between different AI agents, resulting in unprecedented productivity.

What happens when AI learns to “fight monsters in groups”?

For a long time, the AI world has been like a gorgeous but isolated “walled garden”. OpenAI’s ChatGPT, Google’s Gemini, Meta’s llama, and countless startups’ professional AI are all independent and incompatible.

This fragmented ecosystem greatly limits the potential of AI.

To achieve these three challenges, product managers will only continue to appreciate
Good product managers are very scarce, and product managers who understand users, business, and data are still in demand when they go out of the Internet. On the contrary, if you only do simple communication, inefficient execution, and shallow thinking, I am afraid that you will not be able to go through the torrent of the next 3-5 years.

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Users who want to complete a complex task, such as planning a trip, have to manually switch between multiple apps, and the experience is very fragmented.

The A2A protocol was born to smash these “walls”. Its mission is to build an open “agent universe” where any AI (as long as the protocol is followed) can freely collaborate with other AIs to create a seamless and intelligent experience for users. This is not only a technological upgrade, but also a complete reshaping of the future AI ecological pattern.

Imagine if autonomous vehicles could not only “see” the road conditions, but also “talk” and “negotiate” with hundreds or thousands of vehicles, traffic lights, and even the road itself in real time, would urban traffic congestion become a thing of the past?

If AI models in new drug development—from molecular structure prediction to clinical trial data analysis—can seamlessly share results and collaborate on reasoning, how many times faster will we overcome stubborn diseases?

This is exactly what the A2A protocol is committed to doing. It is not only to allow AI to “communicate”, but also to build an efficient and intelligent collaboration network so that the power of AI can grow exponentially. When AI learns to “fight monsters in groups”, its subversive energy will be immeasurable.

A2A’s core design philosophy

The core design ideas of A2A can be summarized as the following five design principles:

1. Embracing agentic capabilities:The protocol fully takes into account the unique advantages of AI agents in handling unstructured data, fuzzy instructions, and complex tasks. It allows agents to collaborate effectively through natural language, even if there is no shared memory, tools, or context.
2. Building on existing standards:The A2A protocol uses the familiar technical standards such as HTTP, Server Send Event (SSE), and JSON-RPC. This significantly lowers the barrier to entry for developers to learn and apply, making it relatively simple to integrate A2A into existing enterprise platforms or applications.
3. Ensuring security by default:In enterprise-level application scenarios, data security is a top priority. The A2A protocol is designed with built-in enterprise-grade authentication and authorization mechanisms, ensuring that all communication and collaboration across agents occur in a secure and controlled environment.
4. Supporting long-running tasks:Many complex tasks are not completed overnight and can take hours or even days to complete. The A2A protocol supports this long-running task and provides real-time progress feedback, status updates, and notifications.
5. Being modality agnostic:Future AI interactions will be multimodal, not limited to text, but also include voice, images, and videos. The A2A protocol has good scalability and can support multiple data modalities, including audio and video streaming, reserving space for richer AI collaboration scenarios in the future.

Key components of the A2A protocol

To understand how the A2A protocol works, we need to understand its four key components:

1. Capability discovery:Each AI agent will have an “Agent Card”, which records the agent’s “strengths” and “skills” in JSON format, that is, what it can do. Through this business card, other agents can quickly discover and understand its capabilities.
2. Task and state management:All communication in the A2A protocol revolves around the core goal of “getting the job done.” The agreement defines clear task objects and task lifecycles, ensuring that both parties have a unified understanding of task creation, execution, status updates, and final completion.
3. Secure collaboration:Agents communicate with each other by sending messages. These messages can be the context of the task, the response to the question, the exchanged documents (Artifacts), or even direct instructions from the user.
4. User experience negotiation:Different AI agents may have different user interfaces and capabilities. The A2A protocol allows agents to specify content types in messages and negotiate to determine the most suitable presentation format and interaction method, ensuring a smooth and consistent experience for end users.

How do agents know each other? – A digital business card called an agent card

An Agent Card is a standardized, public JSON file that is equivalent to a “digital business card” or “public homepage” of an AI agent.

Every agent who wants to participate in A2A collaboration needs to provide such a document. This document details the agent’s identity, functional features (i.e., what tasks it can accomplish), and other technical details about how the agent should communicate with it.

The official name of this standardized document is usually agent.json, and it is placed on a publicly accessible URL so that any other agent or developer can discover and read it.

What information is included in the Agent Card?

At its core, Agent Card provides a structured set of information that other AIs can “read” it. While its specific fields may be fine-tuned as the protocol evolves, its core information composition is stable. Here is the key information included in the Agent Card:

The role of the Agent Card is very important and can be summarized in the following three points:

1. Service Discovery:This is its primary role. In the vast AI network, how can an AI agent find another AI that can meet its specific needs? The answer is to search and parse the Agent Card. For example, if a “travel planning AI” needs to book a flight, it can find agents that have declared their ability to perform book_flight tasks in the Agent Card’s capabilities and choose one to collaborate.
2. Capability Declaration & Advertisement:Agent Card is a way for AI agents to “spread” what they can do to the outside world. It’s like a store’s sign and menu, clearly telling all potential “customers” (other AIs) what “dishes” (tasks) I offer here and what problems it solves.
3. Foundation for Automated Collaboration:Because Agent Cards are machine-readable, standardized JSON, the entire process of service discovery and collaborative initiation can be fully automated. AI agents can autonomously read and understand the other party’s agent card, and know how to correctly construct requests and initiate a task that the other party can handle without any human intervention. This lays the foundation for large-scale, dynamic AI autonomous collaboration.

All in allAgent Card solves the most precedented question in AI collaboration: “Who are you?” What can you do? How can I contact you? ”。 It is the “yellow pages” and “instructions” that realize the mutual discovery, understanding and connection between agents in the entire A2A ecosystem.

The basis for working together between agents – tasks in the A2A protocol

The task object here can be called the soul of the A2A protocol, let’s talk about it next.

Task is the core unit of collaboration between agents。 Think of it as a “shared work folder” or a “project order.”

When one AI agent (client) needs another AI agent (server) to complete a relatively complex task that may require multiple steps or a longer time to complete (e.g., “book flights and hotels to Shanghai next week” or “generate a third-quarter sales report”), the client will create a task.

task objectIt encapsulates all the information, interaction history, and final result needed from the start of the request to the completion of the task。 It is aStatefulmeans it keeps track of where it is currently progressing, supporting long runs and multiple rounds of complex conversations.

In simple terms, task is a standardized and structured work instruction defined in the A2A protocol, which is the starting point and carrier of all collaboration.

Here are the core elements that make up a task:

Understanding the state is especially important for understanding the task, which defines the complete flow of the task from start to finish:

• submitted(Committed): The task has been created and sent by the client, but the server has not yet started processing it.
• working(Processing): The server is actively performing the task.
• input-required(Input required): The server is paused, and additional information is required from the client to continue.
• completed(Completed): The task has been successfully completed and the final artifacts have been generated.
• failed(Failed): The task was terminated due to an error.
• canceled(Canceled): The task was actively canceled by the client or server.

Through this standardized task structure, any AI agent that supports the A2A protocol can accurately understand each other’s intentions, track collaboration progress, and obtain final results, thereby achieving effective collaboration across platforms and ecosystems in the true sense.

The task result of agent cooperation – artifacts in the A2A protocol

In the A2A protocol,Artifacts are the final, deliverable work results produced after the execution of the task。

If Task is the “work order” of the entire project, and history is the “meeting minutes” of the two parties during the project process, then Artifacts is the “final product” or “deliverable” delivered to the customer when the project is finally completed.

When the state of a task changes to completed, the Artifacts field is the most important thing for the client agent because it contains the final result of the task execution.

What information does Artifacts contain?

An Artifact object itself is a structured container designed to support a rich variety of deliverables. It is mainly composed of the following two parts of information:

MessagePart

In order to understand Artifacts, we must understand MessagePart. It is the smallest unit in the A2A protocol that carries everything, whether it is a conversational message or the final product, it is composed of MessagePart. A MessagePart contains:

• content-type:Similar to MIME types, it explicitly defines the format of the data in content. This allows the receiver to know exactly how to parse and process this data. Common types include text/plain (plain text), application/json (JSON object), image/png (PNG image), etc.
• content (content itself):The actual data, whose format is specified by the content-type.

With this design, an Artifact can deliver complex results with great flexibility. For example, an Artifact named “flight-itinerary” can contain two parts at the same time: one part is structured itinerary data in application/json format for the program to automatically process; The other part is text summaries in text/plain format for direct human reading.

Artifacts play several key and distinct roles in the A2A protocol:

1. Package and deliver the end result:This is its primary role. It clearly separates the final work from the dialogue (history) in the process. When a task is completed, the client does not have to parse the entire conversation history to find an answer, but simply accesses the artifacts array in the Task object to obtain a standardized, final deliverable.
2. Support for rich and multimodal deliverables:By using a MessagePart that contains the content-type, Artifacts can deliver more than just simple text. It can deliver structured data (JSON), files (by base64 encoding, etc.), images, and even references to video streams. This flexibility is crucial for powerful AI agents.
3. Deliver clear, traceable results:Each Artifact has a clear name, and its content consists of structured parts. This provides great convenience for automated processing. The AI that receives the task results can decide what to do with this deliverable based on the name and content-type, and the entire process is clear, reliable, and traceable.

All in allArtifacts is a “standardized container” for mission outcomes in the A2A protocol。 It ensures that no matter how complex the task, its final deliverables are clearly encapsulated and delivered in a structured, parsable, and format-rich manner, enabling efficient and reliable automation collaboration closed loops.

Agent collaboration process under the A2A protocol

Assuming that the AI assistant “Xiaozhi” and “Shenzhen Airlines AI Booking Service” want to collaborate through the A2A protocol to complete a ticket booking, the collaboration process between them will look like this:

Target:

Xiao Ming booked a plane ticket through the AI assistant “Xiaozhi”.

Role:

• Client Agent:“Ash”
• Server Agent:“Shenzhen Airlines AI Booking Service”

Process:

Step 1: Service Discovery

What happened between the two agents:“Ash” needs to find an agent who can book air tickets. It startedProgrammatic queryService catalog. This process is machine-to-machine:

1. “Xiaozhi” reads andparseA series of agent.json files (ieAgent Card）。
2. It looks for the capabilities.tasks array in each file, looking for tasks with the name attribute “book_flight”.
3. It found the Agent Card of “Shenzhen Airlines AI” and successfully matched it.
4. “Ash” from this cardExtract key information: The other party’s unique IDcom.shenair.booking and APIEndpoint URLhttps://api.shenair.com/a2a/v1。

Step 2: Security & Authorization

What happened between the two agents:Before initiating a mission, both parties must establish a secure relationship of trust.

1. Transport Layer Authentication (mTLS):The back-end server of “Xiaozhi” initiates a connection to the endpoint URL of “Shenzhen Airlines AI”. Both are carried outMutual TLS (mTLS) handshake, by exchanging and verifying their respective SSL certificates, to prove their “machine” identity in both directions. This ensures that the connection is established between two trusted servers and that all subsequent communications are encrypted.
2. Application Layer Authorization (OAuth 2.0):“Xiaozhi” initiated to the authorized server of “Shenzhen Airlines AI”OAuth 2.0 authorization process, requesting permissions to operate on behalf of Xiao Ming. After Xiao Ming (the user) is guided to the authorization page to agree, the authorization server of “Shenzhen Airlines AI” will generate oneAccess Token, and sent it back to “Ash”. This token (usually in JWT format) contains the user’s identity and approved scopes of permissions, such as booking:create.

Step 3: Task Creation & Submission

What happened between the two agents:Ash packages the user’s request into an A2A standard formatTaskObject.

1. It constructs a JSON object with a history array containing the first message: { “role”: “user”, “parts”: [{ “content-type”: “text/plain”, “content”: “next Monday, Beijing to Shenzhen, one-way” }] }.
2. “Xiaozhi” initiates one to the endpoint URL of “Shenzhen Airlines AI”HTTP POST request。
3. This requestBody sectionis the full Task JSON object.
4. requestedHeader sectionContains authorization information: Authorization: Bearer<上一步获取的Access_Token>.

Step 4: Collaborative Interaction

What happened between the two agents:This is a stateful, multi-round conversation process, and the Task object is the carrier for passing state.

1. Shenzhen Airlines AI receives a POST request, verifies that the token passes, accepts the task, assigns it a unique id, and sets the state to working.
2. “Shenzhen Airlines AI” queries the database based on the task information, and thenModify the Task object, appends a new message from the agent role to the end of the history array, which contains JSON data for flight options.
3. “Shenzhen Airlines AI” through preset webhooks or other notification mechanisms,noticeThe Ash mission status has been updated.
4. “Ash” presents the options to the user and gets a choice, againModify the local Task object, append a new message from the user role (user’s choice) to the end of history.
5. “Ash” will thisAn updated Task object that contains the full conversation history, and send it to Shenzhen Airlines AI again (usually via HTTP PUT or POST to a specific task URL).

Step 5: Completion & Artifact Delivery

What happened between the two agents:After the task is executed, the server-side agent delivers the final result.

1. After receiving the final confirmation, “Shenzhen Airlines AI” will perform the booking operation.
2. After the operation is successful, it is the last timeModify the Task object: Update state to completed.
3. The most critical step: it populates the Task object with an array of artifacts. It creates a JSON object asArtifactFor example: { “name”: “flight_confirmation”, “parts”: [{ “content-type”: “application/json”, “content”: “{… Full JSON with order number, seat, QR code data…}” }] }.
4. “Shenzhen Airlines AI” pushed this final form of Task object to “Xiaozhi”, marking that all the work on its side has been completed.

Step 6: Result Processing & Presentation

What happened between the two agents:The client agent receives and utilizes the final product.

1. Ash receives a Task object with completed state.
2. itNo longer care about history, but directlyParse the artifacts array。
3. It finds a product called flight_confirmation and extracts a part with a content-type application/json from it to obtain structured order data.
4. Ash uses this accurate data to render a visual booking success card on the user interface. Technically, this is the end of the A2A collaboration. The follow-up action of “Ash” is the interaction between itself and the user.

Through this process, two originally independent AI agents completed a complex collaborative task safely, efficiently, and clearly under the standardized “collaboration framework” of the A2A protocol.

How does A2A ensure safety?

The A2A protocol builds a multi-layered security defense system in depth to ensure that collaboration between agents is secure from the start.

It mainly has the following three levels:

Layer 1: Transport Layer Security – Verifying the Identity of the “Machine” (mTLS)

Before two AI agents start a conversation, address the most basic questions first:Am I talking to the right “machine”? Are our call channels private?A2A protocol passed”MutualTransport Layer Security (mTLS)to solve this problem.

What is mTLS?HTTPS (or TLS) is used by the client (your browser) to verify the server’s certificate to ensure that the website is authentic and not a counterfeit. It is oneOne-way verification。 mTLS is oneBidirectional verificationProcess. Not only does the client verify the identity of the server, but the server also verifies the identity of the client in turn. Both parties need to present and verify each other’s digital certificates to establish an encrypted communication channel.

What does it do?

• Strong Authentication:mTLS ensures that both parties involved in communication, the two AI agents, are authenticated, trusted entities. This effectively prevents unauthorized “fake” agents from accessing the system.
• Data Encryption:Once the mTLS handshake is successful, all data transmitted between the two is encrypted. This prevents any intermediary parties from eavesdropping or tampering with the content of the communication.

Regular HTTPS is like you go to a government agency to run errands, and you need to show your ID card to the staff (one-way authentication). mTLS, on the other hand, is like two agents who need to show each other their credentials (two-way authentication) and confirm each other’s identities before starting to exchange information in an encrypted channel.

Layer 2: Application Layer Security – Validating Authorization for “Actions” (OAuth 2.0)

It is not enough to confirm the identity of the “machine”, it is necessary to solve a second, more important problem:Do you (the AI) have the authority to perform this on behalf of your users?The A2A protocol uses the most mainstream licensing framework in the industryOAuth 2.0to solve this problem.

What is OAuth 2.0?OAuth 2.0 is an authorization standard that allows third-party applications (e.g., a client-side AI) to access protected resources on another service (e.g., a server-side AI) on behalf of a user without obtaining a user’s password. This is achieved through “tokens”.

How does it work in A2A?

• When a client AI wants to request another server AI (such as an airline’s booking AI) on behalf of a user (such as you), it will first obtain one from the user through the OAuth 2.0 processAccess Token。
• This token is like a time-bound, scoped “power of attorney”. It is included in every request sent by the client AI to the server AI.
• After receiving the request, the server AI verifies the validity of the token and checks the “scopes” contained in the token to determine whether the client AI has the authority to perform the requested operation (e.g., whether it has read:flights or write:booking permissions).

For exampleYou authorize a third-party app (client AI) to read your album (server AI). Instead of giving it your password, you agree to grant it “read-only” permissions through an authorization page. The system will issue a token to the app, which can read your album, but cannot be deleted or modified because the “scopes” of the token limit its operation permissions.

Level 3: Design Philosophy – Principle of Least Privilege

This is the core guiding ideology throughout the entire A2A safety design.

• What is the principle of least privilege?This principle requires that any agent (or any component of the system) should be granted only the least privileges necessary to complete its task. No more, no less.
• How does it manifest itself?When designing the Agent Card’s capability list, developers are encouraged to follow this principle strictly. For example, an AI used to “query the weather” should never be granted access to the user’s address book or the task defined in the Agent Card. The scopes of tokens assigned through OAuth 2.0 should also be as granular and limited as possible.

Doing so can be greatReduce the attack surface。 Even if an agent is compromised for some reason, the damage that an attacker can cause will be limited because its privileges are very limited.

Through the combination of these three layers of capabilities, the A2A protocol builds a robust default security model:

A2A makes 1+1 >> N’s magic

Why does A2A have great potential?

Because it can release the “network effect” of AI and achieve the amazing effect of “1+1 is much greater than N”.

Functional stacking is just an appetizer:

For example, your personal assistant AI (responsible for schedules) connects to weather AI (providing forecasts) and news AI (filtering information) via A2A to generate a highly customized morning briefing for you. It’s just a simple addition of abilities.

The real magic lies in the “emergence of intelligence”:

Taking it a step further, imagine a network of multiple medical research AIs specializing in genomics, proteomics, medicinal chemistry, and more. Through the A2A protocol, they not only share data but also cooperate in reasoning, potentially uncovering complex disease patterns or potential drug targets that human researchers may not notice. This leap from quantitative change to qualitative change is the “emergence of intelligence”. This is similar to the simple nature of a single neuron, but hundreds of millions of neurons form human intelligence through complex connections (similar to A2A).

Historically, the emergence of the TCP/IP protocol has revolutionized the way information is disseminated by connecting decentralized computers to the Internet. The unification of USB standards allows countless peripherals to be plugged and played, which greatly facilitates users.

The A2A protocol has high hopes and is expected to become the “TCP/IP” of the AI era, connecting isolated AI intelligence into a huge “smart network”, and its potential value will be immeasurable.

As Google emphasizes, this “universal interoperability is critical to unlocking the full potential of collaborative AI agents.”

For example, in response to global climate change, environmental monitoring AI, climate model AI, and energy dispatching AI distributed in various regions can work together through A2A to provide unprecedented insights and solutions.

The difference between A2A and API, function calls, MCP, and federated learning

It’s easy to confuse A2A with the concepts of API, function calls, MCP, and federated learning. They seem to be related, but in fact they are very different in terms of goals and mechanisms:

• API (Application Programming Interface):Like a restaurant’s “menu”. It stipulates what you can order (function) and how to order (call method).At its core, it’s a “programmatic call to a function.”
• Function Calling:It’s like giving AI a “smart toolbox”. AI knows which tool to use (calling a function) to enhance its capabilities in a given scenario.The core is “the expansion of AI capabilities and the use of tools”.
• MCP (Model Context Protocol):This is like providing a standardized “device operation guide” for the “toolbox” of AI, making the tool easier to use.The core is “interaction specifications between AI and tools/data sources”.
• Federated Learning:Federated learning is indeed also “distributed collaboration”, but its goal is completely different from A2A. Imagine a global consortium of culinary schools wanting to create the perfect cake recipe. Each school conducts experiments with its own secret ingredients (local data), and then only sends the “baking experience” (model update) obtained from the experiment back to the headquarters. The headquarters combined all the experience to update a more perfect version of the “master formula” (global model), and then sent it to everyone.The whole process is to produce a better “recipe”, not to complete a “cake order”.

Core differences at a glance:

The essence of A2A is to build a “social relationship” and “collaborative ecology” between AI agents, emphasizing the combination and emergence of autonomy, negotiation and intelligence.It is not to make AI more “use tools”, but to let AI learn to “find partners”, “form teams”, and “do big things”!

A2A application scenarios are forward-looking

The emergence of the A2A protocol will greatly expand the application boundaries of AI, and some imaginative application scenarios can be deduced:

• Super Personal Assistant:Your personal AI assistant will be a “master dispatcher.” When you need to arrange a complex cross-border trip, it will automatically connect with the airline’s AI to book tickets, the hotel’s AI to make reservations, the car rental company’s AI to arrange pick-up and drop-off, and even the destination’s guide AI to plan your itinerary. You just need to say what you think, and let the AI group take care of the rest.
• Automate business operations:Businesses will be more efficient than ever. Procurement AI automatically compares and negotiates prices with AI from global suppliers; Production AI adjusts production lines in real time based on market forecasts provided by sales AI; Financial AI automatically completes the bookkeeping and auditing of all transactions. The entire company is like a precision machine of countless AI agents working together, operating efficiently 24 × 7 hours a day.
• Smart Cities and Public Services:Urban traffic management AI can be interconnected with weather forecasting AI and large-scale event management AI to predict and relieve traffic congestion in advance. Citizens’ help AI can be directly connected to community service AI and medical emergency AI to get the most accurate and fast help at the first time.
• “Unmanned” global intelligent logistics network:Unmanned trucks, drones, automated warehouses, and port dispatching AI form a global intelligent logistics network through A2A, and the efficiency and cost of cargo transportation will be revolutionized.
• “Digital Twin Earth” and Crisis Warning:Countless sensor AI, environmental simulation AI, and socio-economic model AI build a real-time “digital twin Earth” through A2A, which can simulate climate change, predict natural disasters, optimize resource allocation, and even deduce policy impacts, providing decision-making support for human beings to address global challenges.
• Decentralized “AI Research Community”:Scientific research AI around the world can safely and efficiently share encrypted data, algorithm models and calculation results based on the A2A protocol, and launch a “distributed all-out attack” on major topics such as cancer, AIDS, and new energy to accelerate the process of scientific discovery.

These exciting scenarios are becoming out of reach because of A2A.

Why is the emergence and development of A2A an inevitable trend?

The development of AI did not happen overnight. From the early “calculators” that only executed fixed instructions, to the “expert systems” that later showed extraordinary capabilities in specific fields (chess, image recognition), AI has gone through a long process of “learning”. However, most of these “genius AIs” are “lone rangers”, independent and difficult to integrate.

This is like human society, no matter how strong an individual’s ability is, it cannot build a pyramid or land on the moon. Only when individuals learn to communicate, divide and cooperate, can civilization progress. The same goes for AI. As the capabilities of a single AI model gradually hit the ceiling and real-world problems become increasingly complex,Letting AI get out of the “cocoon” and learn to “socialize” and “collaborate” has become an inevitable choice to break the bottleneck and release greater potential.

The emergence of the A2A protocol is in line with this historical trend. It is a key step for AI to move from “individual intelligence” to “collective intelligence”, marking the opening of a new chapter in the “history of social evolution” of AI.

In the past, we paid more attention to how to make individual AI models stronger and smarter, which was like trying to cultivate “individual champions”. The emergence of the A2A protocol provides us with a framework to form a “dream team” composed of countless “individual champions” to solve more ambitious and complex challenges through collaborative cooperation.

This is not only an improvement in efficiency, but also a paradigm shift. Just as the emergence of the TCP/IP protocol eventually gave birth to the Internet ecosystem as we know it today, the A2A protocol is also very likely to become the cornerstone of the future “AI Internet”. In this new ecosystem, the value of an AI agent will no longer depend only on its own capabilities, but also on its ability to connect and collaborate with other agents.

Of course, the development of the A2A protocol has not been smooth sailing. How to establish a broad industry consensus, how to ensure the absolute security of cross-ecological collaboration, and how to deal with the possible attribution of responsibilities during the collaboration process are all challenges that need to be continuously explored and solved in the future.

But regardless, the A2A protocol has painted an exciting blueprint for the future. In this blueprint, AI becomes a “digital life” that can communicate and collaborate with each other. They will be integrated into human society with unprecedented depth and breadth, reshaping the way we live, work, and even the world as a whole.