Author: Shreyas Bilikere Shantharaju
Agent-to-Agent (A2A) protocol is a way for different software agents or AI systems to directly communicate and share information with each other. Each agent is usually responsible for handling a specific task, and A2A makes it possible for them to collaborate without needing manual coordination. This is especially useful in complex systems where multiple services need to work together seamlessly. The A2A protocol defines how these agents send messages, understand each other’s requests, and securely exchange data, allowing processes to run automatically and efficiently.
Think of an HR Payroll system where each agent has its own job. The Leave Agent takes care of leave requests, and the Payroll Agent handles salaries. Now, let’s say an employee’s unpaid leave gets approved—the Leave Agent instantly shares that info with the Payroll Agent. The Payroll Agent then adjusts the salary automatically, without anyone having to step in. This back-and-forth between agents keeps things running smoothly, avoids mistakes, and saves everyone a lot of time.
Key problems addressed by agent-to-agent communication (A2A) protocol:
Imagine you’ve set up a team of AI assistants—one specializes in analysing data, another writes detailed reports, and a third manages meetings and schedules. Each one is excellent at its own job. But the problem is, they don’t communicate in the same way. One understands Python, another works with JSON, and the third uses complex API commands. So, when you ask them to work together, It leads to confusion because they don’t understand each other.
Google’s Agent2Agent (A2A) Protocol was designed to solve this exact problem by helping different AI agents communicate and collaborate seamlessly.
Key difference between MCP and A2A:
- MCP (Model Context Protocol) MCP is designed to give a single AI agent access to external tools and data sources it needs to work effectively. Think of it as connecting one AI to all the resources it might need – databases, files, APIs, and various tools.
- A2A (Agent-to-Agent Protocol) A2A focuses on allowing multiple AI agents to communicate and work together as a team to accomplish tasks. Instead of one AI doing everything, multiple specialized agents can collaborate and share information with each other.
The Bottom Line MCP connects AI to tools; A2A connects AIs to each other.
Key capabilities of A2A protocol:
- Client agent/A2A client: App or agent that consumes A2A services. This is your “main” agent, the one that puts together tasks and communicates them to other agents.
- Remote agent/A2A server: Agent exposing an HTTP endpoint that uses the A2A protocol. These are the supplementary agents that handle task completion.
Core components of A2A:
- Agent Card: Typically hosted at a well-known URL like /.well-known/agent.json, this JSON file describes an agent’s capabilities, skills, endpoint URL, and authentication requirements.
- A2A Client: The app or AI system that consumes A2A services.
- Task: The central unit of work in A2A. Each task has a unique ID and progresses through defined states (like submitted, working, completed, etc.).
- Message: Communication turns between the client and the agent. Messages are exchanged within the context of a task and contain Parts that deliver content.
Part: The fundamental content unit within an message. Parts can be:
TextPart: For plain text or formatted content
FilePart: For binary data (with inline bytes or a URI reference)
DataPart: For structured JSON data (like forms)
- Artifact: The output generated by an agent during a task. Artifacts also contain Parts and represent the final deliverable from the server back to the client.
Understanding how it works:
A2A works using a simple client-server model where different agents communicate with each other through standard web protocols like HTTP, gRPC etc. They exchange information using structured JSON messages, making it easier to connect with existing systems and ensuring consistent communication between agents.
Next, we’ll break down the key parts of A2A and look at a typical example of how agents interact using this protocol.
Discovery Phase: The process starts when the user asks the main AI agent for help—for example, “Can you help me plan a business trip to Tokyo next month?”
The main agent understands that this task has different parts: booking flights, finding a hotel, and planning activities. It doesn’t handle everything itself. Instead, it looks for other specialist agents who are experts in each area.
To find the right specialists, the main agent checks their Agent Cards. These cards contain key details about what each agent can do, allowing the main agent to choose the best one for each task.
Task Initiation: Once the main agent selects the right specialists, it assigns specific jobs to each of them. For example, it may tell the flight booking agent:
“Find flights to Tokyo from May 15th to May 20th.” This task is sent as a POST request to the specialist agent’s server, usually at an endpoint like /taskssend. The request creates a new task, assigns it a unique task ID, and includes all the instructions needed to begin the work.
Processing: After receiving the task, the specialist agent starts working on it. Depending on the task, the agent can respond in different ways:
- Immediate completion: If the job is simple, the agent can quickly send back results, like a list of flights.
- Request for more details: If the task needs clarification, the agent will ask follow-up questions. For example, “Do you prefer a specific airline?”
- Long-running task: For complex work, the agent will set the task status to working. For example, “I’m comparing prices to find the best deal.”
This flexible process allows agents to handle simple and complex tasks efficiently.
Multi-Turn Conversations: Sometimes a task needs more back-and-forth communication. This happens when the specialist agent needs extra information to proceed. For example:
- Agent asks: “Are connections okay, or do you want only direct flights?”
- Main agent replies: “No, only direct flights.”
All these messages happen under the same task ID, keeping the conversation organized and linked to the original request.
Status Updates: For tasks that take time, the system provides different ways to share updates with the main agent:
- Polling: The main agent can check the task’s status at regular intervals.
- Server-Sent Events (SSE): The server can stream real-time updates directly to the main agent.
- Push Notifications: The specialist agent can send updates to a callback URL provided by the main agent.
This ensures that the user is always informed about progress, even for long-running tasks.
Task Completion: When the specialist agent finishes its work, it marks the task as completed and sends back the final results, often called an artifact (for example, the list of flights, hotel bookings, or activity plans).
If there’s a problem, the agent may set the task status to:
- Failed – If it couldn’t complete the job.
- Cancelled – If the task was stopped before completion.
Meanwhile, the main agent might be collecting results from multiple specialist agents—like the hotel agent, transport agent, and activities planner. Once all the tasks are done, the main agent combines everything into a full travel plan and presents it to the users.
Dive into the code on GitHub:
Agent-to-Agent (A2A) protocols represent a significant step forward in making AI systems work together more effectively. Instead of relying on a single AI to handle everything, A2A allows specialized agents to communicate and collaborate seamlessly, much like how different department experts work together in a company. This approach reduces errors, saves time, and enables more complex automated workflows that would be difficult for one agent to manage alone. As businesses increasingly adopt multiple AI tools, A2A protocols will become essential for creating integrated, intelligent systems that can handle sophisticated tasks without constant human oversight. The result is a more efficient and reliable way to automate complex processes across various industries.
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