Build an AI Agent using Google Development Kit - ADK | നിങ്ങൾക്കും ഒരു AI Agent ഉണ്ടാകാം

Introduction

In this tutorial, we will explore how to build an AI agent using the Google Agent Development Kit (ADK). This revolutionary open-source toolkit allows developers to create production-ready AI agents with just a few lines of Python code. With features supporting both single and multi-agent systems, ADK is designed for flexibility and easy integration with various tools. Whether you are new to AI or looking to enhance your skills, this guide will help you get started with developing your own AI agents.

Step 1: Set Up Your Environment

To begin building your AI agent, you need to set up your development environment.

• Install Python: Ensure that Python is installed on your system. You can download it from the official Python website.
• Install Google ADK: Use pip to install the Google Agent Development Kit. Open your terminal and run:

  pip install google-adk
  

• Verify Installation: Confirm that ADK is installed correctly by running the following command:

  adk --version
  

Step 2: Create Your First AI Agent

Now that your environment is ready, let’s create a simple AI agent.

• Initialize a New Project: Create a new directory for your project and navigate into it.

  mkdir my_ai_agent
  cd my_ai_agent
  

• Create Agent Script: Create a new Python file (e.g., agent.py) and open it in your preferred code editor.
• Write Basic Agent Code: Add the following code to define a simple AI agent:

  from google_adk import Agent
  
  class MyAgent(Agent)
      def respond(self, input_text)
          return f"Hello, you said: {input_text}"
  
  if __name__ == "__main__"
      agent = MyAgent()
      print(agent.respond("Welcome to AI development!"))
  

Step 3: Implement Flexible Workflows

ADK allows you to create flexible workflows for your AI agents.

• Define Workflow Types: You can implement sequential, parallel, or looping workflows depending on your needs.
• Example of a Sequential Workflow:

  from google_adk import Workflow
  
  class MyWorkflow(Workflow)
      def run(self)
          step1 = self.step1()
          step2 = self.step2(step1)
  
      def step1(self)
          return "Step 1 completed"
  
      def step2(self, input)
          return f"Step 2 received: {input}"
  
  if __name__ == "__main__"
      workflow = MyWorkflow()
      workflow.run()
  

Step 4: Integrate Tools

ADK supports integration with various tools, enhancing your AI agent's capabilities.

Tool Integration Examples

• Google Search: Use Google’s API to fetch real-time data.
• Custom Tools: Create your own tools to extend functionality.

Step 5: Deployment Options

Once your agent is ready, you can deploy it in various environments.

Deployment Locations

• Locally: Run on your own machine.
• Cloud: Deploy using Google Cloud services.
• Docker: Use Docker containers for easy deployment.

Step 6: Evaluate and Secure Your Agent

Evaluating and securing your AI agent is crucial for production readiness.

• Built-in Evaluation: Use ADK’s evaluation tools to test your agent’s performance.
• Security Features: Implement security protocols such as MCP and A2A to protect your agent's data and interactions.

Conclusion

Building an AI agent with the Google Agent Development Kit is accessible and powerful. By following these steps, you can create functional agents capable of handling various tasks, implement flexible workflows, and integrate diverse tools. As you develop your skills, consider exploring more advanced features of ADK and experimenting with different deployment strategies. Happy coding!