Google Agent2Agent (A2A) protocol : Connect AI Agents together
Agent2Agent (A2A) vs Model Context Protocol?
Google is now leading almost all the facets of GenAI, be the best LLM (Gemini 2.5 Pro), ease of use for common folks (AI Studio) or Agentic framework (Google ADK). Adding to this list, they are trying to rival Anthropic’s Model Context Protocol (MCP Servers) by launching Agent2Agent protocol (they are not the same though).
Data Science in Your Pocket – No Rocket Science
What is Google A2A Protocol?
AI agents are tools that help businesses automate tasks like ordering supplies, handling customer service, or managing schedules. But right now, these agents often struggle to work together if they’re built by different companies or use different systems. To fix this, Google created the Agent2Agent (A2A) protocol — a shared set of rules that lets any AI agent, no matter who made it, collaborate smoothly.
Think of A2A like a universal translator for AI agents. It helps them share information, assign tasks, and get things done faster, even if they’re running on different platforms (like Salesforce, SAP, or Slack).
How Does A2A Work?
Imagine two people working together: one person gives instructions (“Client”), and the other does the work (“Helper”). A2A uses similar roles for AI agents:
- Client Agent: Starts a task (e.g., “Process this invoice”).
- Helper Agent: Does the work and sends back results (e.g., “Invoice paid”).
Step by Step explanation
Discover Skills:
- Agents share a simple “profile” (like a resume) listing what they can do. For example, Agent A says, “I can pay invoices,” and Agent B says, “I can track shipments.”
Start a Task:
- The Client Agent sends a request (e.g., “Pay Invoice #123”) to the Helper Agent. Each task gets a unique ID to track progress.
Do the Work:
- The Helper Agent processes the task. If it’s quick (like paying an invoice), it sends back results immediately.
- For longer tasks (like researching a report), the Helper sends updates in real-time via email, messages, or dashboards.
Share Results:
- Once done, the Helper sends back an output (like a paid invoice confirmation or a document).
Key Benefits
- Works Everywhere: Agents can team up across apps and tools (e.g., Salesforce + Google Sheets). No more getting stuck with one company’s system.
- Any Content Type: Agents can share text, files, images, or even video calls. For example, a customer service agent could send a video explanation instead of just text.
- Secure & Reliable: Built-in security (like login credentials) ensures only authorized agents can access data.
- Handles Long Tasks: If a task takes days (e.g., planning a project with human input), A2A keeps everyone updated with live notifications.
Is Agent2Agent protocol similar to MCP?
Nopes
- MCP is primarily concerned with connecting AI agents to external tools, APIs, and data sources. It acts as the middleware or orchestration layer that enables agents to do things — fetch data, call APIs, interact with databases, run code, and more. MCP makes agents useful by giving them access to capabilities beyond themselves.
- A2A, on the other hand, focuses on enabling multiple AI agents to communicate, collaborate, and coordinate tasks among themselves. It’s more about inter-agent collaboration, rather than tool integration.
Now, in a real-world setup, these layers often complement each other.
Example
Scenario: Automated Research & Report Generation
Agent A (Research Agent): Finds and compiles recent articles on a topic.
Agent B (Summarizer Agent): Summarizes documents.
Agent C (Report Builder): Formats everything into a report.
Here’s How A2A + MCP Work Together:
Agent A uses MCP to call a news API and pull recent articles on “AI in Healthcare.”
Agent A uses A2A to message Agent B:
“Here are 5 articles. Can you summarize each of them?”
Agent B uses MCP to access a summarization API or model to generate summaries.
Agent B sends the summaries to Agent C via A2A.
Agent C uses MCP to format the content, maybe calling a Markdown-to-PDF API to produce the final report.
Hence,
MCP = Agents + Tools
A2A = Agents + Each Other
Conclusion
Google’s Agent2Agent (A2A) protocol marks a significant leap in enabling seamless collaboration between AI agents, regardless of who built them or where they run. While MCP equips agents with tools and APIs to act, A2A empowers them to interact, delegate, and cooperate like a well-orchestrated team. Together, these protocols don’t compete — they complement each other, forming the backbone of more autonomous, capable, and connected AI ecosystems. As GenAI continues to evolve, it’s this combination of interoperability and tool integration that will define the next generation of intelligent, agent-driven workflows.
Google Agent2Agent (A2A) protocol : Connect AI Agents together was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
