Building Connected Agents with MCP and A2A

To build a production-ready agentic system, where intelligent agents can freely collaborate and act, we need standards and shared protocols for how agents talk to tools and how they talk to each other.

In the Agent Production Patterns module in the Production-Ready AI with Google Cloud Learning Path, we focus on interoperability, exploring the standard patterns for connecting agents to data, tools and each other. Here are three hands-on labs to help you build these skills.

The Foundations of ADK, MCP, A2A

This lab serves as your "Hello World" for the modern agent stack. You will build a simple, specialized agent that demonstrates how Agent Development Kit (ADK), Model Context Protocol (MCP), and Agent to Agent Protocol (A2A) work together. 

Connecting to Data with MCP

Once you understand the basics, the next step is giving your agent access to knowledge. Whether you are analyzing massive datasets or searching operational records, the MCP Toolbox provides a standard way to connect your agent to your databases.

Expose a BigQuery database to an MCP Client

This lab shows you how to expose BigQuery tables to an MCP client.

Expose a CloudSQL database to an MCP Client

If you need your agent to search for specific records—like flight schedules or hotel inventory—this lab demonstrates how to connect to a CloudSQL relational database.

From Prototype to Production

By moving away from custom integrations and adopting standards like MCP and A2A, you can build agents that are easier to maintain and scale. These labs provide the practical patterns you need to connect your agents to your data, your tools, and each other.

These labs are part of the Agent Production Patterns module in our official Production-Ready AI with Google Cloud Learning Path. Explore the full curriculum for more content that will help you bridge the gap from a promising prototype to a production-grade AI application.

Share your progress using the hashtag #ProductionReadyAI. Happy learning!