Agent-to-Agent (A2A) Protocol
Heet Vekariya
We’ve been following the Model Content Protocol (MCP) for the past couple of weeks. But in the meantime, Google introduced something new Agent-to-Agent (A2A) Protocol which further expands the capabilities of AI agents. Interestingly, it complements the Model Content Protocol we discussed in recent blogs.
In this post, we deep dive into the A2A Protocol, exploring its functionalities and how it complements MCP.
Introduction of A2A Protocol
The Need for A2A: Why Was It Developed?
Every innovation arises from a need. Similarly, the development of A2A protocol was motivated by a need for efficient, autonomous agent collaboration.
Let’s understand this through a real-world example:
Suppose you want to order lunch but are unsure what to pick. You decide to go with the cheapest option among pasta, pizza, or a full meal. You start by interacting with Domino’s agent to get pizza options, then switch to McDonald’s agent for meals, and another for pasta. You manually compare the responses from each agent.
But what if McDonald’s or Domino’s doesn’t even have a chatbot or an agent available?
Now imagine if A2A protocol was implemented.
You go to a third-party service like Zomato’s agent and provide your requirement. Zomato’s agent will then automatically:
Discover available agents from Domino’s, McDonald’s, and other vendors.
Send your requirements directly to these agents.
Retrieve, compare, and format the responses.
Present the final decision-ready data to you.
This not only saves time but eliminates the need for human-driven agent discovery and communication.
Just like a team project, each member takes a piece of the task, and the team lead delivers the final outcome - A2A empowers agents to communicate autonomously and collaboratively.
What is the Agent-to-Agent (A2A) Protocol?
Agent-to-Agent Protocol (A2A) is an open protocol that enables agents to interoperate across different ecosystems, frameworks, or organizations. It facilitates a dynamic, multi-agent environment where agents can communicate, discover one another, and collaborate without manual intervention.
Let’s break this down:
Open Protocol: A communication standard that’s publicly available and free to use, like HTTPS.
Multi-Agent Ecosystem: An environment where specialized agents perform different tasks collaboratively.
Interoperability: Agents can work together even if developed by different vendors.
Two Key Functionalities of A2A
1. Agent Discovery
A valid question here is: How does Zomato’s agent discover Domino’s or McDonald’s agent?
Think of how we sign up for third-party services using Google or GitHub. The third party needs to know how to authenticate you via Google. This is possible via the OpenID Connect authentication protocol, which standardizes where to find authentication details—usually hosted at:
For example, Google’s config provides all the endpoints a developer needs for integration.
Google accounts .well-known response
Similarly, Google’s A2A protocol suggests a standardized URL for agent discovery:
From this URL, Zomato’s agent can retrieve:
The list of available agents at Domino’s.
The capabilities of those agents.
This enables real-time agent discovery with no manual lookup or hardcoded integration.
📌 Imagine a future where every major company hosts an
agent.json file on their domain, making them accessible for smart assistant agents globally.2. Agent Communication
Once agents are discovered, Zomato’s agent can now:
Talk to Domino’s agent to get available pizza options.
Interact with McDonald's agent to fetch full meal deals.
Understand the client's previous preferences and budget constraints.
Fetch data back and forth as needed.
Summarize the best choices and present them in a curated response.
This mirrors how human teams work, dividing and collaborating to deliver a comprehensive solution.
Evolution of Agent Ecosystems
Stage 1 – Standalone agents
Perform specific tasks using built-in tools with no external communication.
Stage 2 – Multi-agent Framework
Utilizes parent and child agents to delegate and manage tasks collaboratively.
Stage 3 – MCP-enabled agents
Access additional functionalities and third-party services through MCP servers.
Stage 4 – Multi-agent systems with A2A
Agents collaborate, communicate, and leverage each other’s strengths to solve complex tasks.
✅ We are currently in Stage 4, where agents operate collaboratively using both MCP and A2A protocols.
A2A vs MCP: What’s the Difference?
Feature A2A Protocol MCP Protocol Purpose Enables collaboration between agents Allows agents to access 3rd-party tools Focus Communication & Coordination Capability Enhancement Example Use Case Agents discovering and collaborating Retrieving data from external services
Example Use Case: Sales Data Analysis
Let’s say you want a market report comparing your company's past sales with competitors.
The multi-agent system might involve:
A Parent Agent coordinating the process.
A Finance Agent fetching internal sales data.
A Research Agent gathering competitor insights.
An MCP Server providing access to databases.
Other agents discovered via A2A.
A2A and MCP Implementation Architecture
The Workflow:
Parent Agent assigns tasks to Finance and Research Agents.
Finance Agent uses MCP to retrieve internal data.
It summarizes the insights and forwards them to the Research Agent.
Research Agent uses A2A to discover external agents (e.g., news or analytics providers).
It gathers competitor data and creates a comparative analysis with charts.
Parent Agent compiles the results and presents it to the end user.
Future Possibilities with A2A
Enterprise Integrations: Cross-company AI systems working together securely.
Agent Marketplaces: Public registries of specialized agents for plug-and-play collaboration.
Decentralized Intelligence: Independent agents sharing knowledge in real-time.
Auto-agent Builders: Systems generating agents on the fly for temporary collaborative tasks.
As more companies embrace this protocol, the internet could evolve from static APIs to a living web of interoperable agents.
Conclusion
The A2A protocol represents a major leap toward creating a collaborative AI ecosystem where agents don’t just serve isolated tasks but form intelligent, interconnected systems. Paired with the Model Content Protocol, it lays the groundwork for scalable, secure, and smart multi-agent systems.
We are just beginning to tap into this potential and the possibilities are immense.
👉 If you found this helpful, don’t forget to share and follow for more agent-powered insights. Got an idea or workflow in mind? Join the discussion in the comments or reach out on Twitter.
Photo Credit: Edwin Lisowski
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Posted May 15, 2025
Explored A2A Protocol's functionalities and its complement to MCP.
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Apr 28, 2025 - Apr 30, 2025
