Google’s New Agent Development Kit: Build the Future of AI

The landscape of artificial intelligence is rapidly moving beyond single-purpose models towards intelligent, autonomous multi-agent systems. Google is at the forefront of this evolution, and their latest offering, the Agent Development Kit (ADK), unveiled at Google Cloud NEXT 2025 , is poised to revolutionize how developers build and deploy these sophisticated AI agents. This open-source framework is designed to simplify the full stack end-to-end development of agents and multi-agent systems , empowering developers to build production-ready agentic applications with greater flexibility and precise control.

Decoding Google’s Agent Development Kit: What’s Under the Hood?

Google’s newest Agent Development Kit (ADK) is an open-source framework written in Python that provides the necessary infrastructure and resources to create sophisticated AI agents capable of understanding context, making decisions, and taking actions in complex environments. It is the same framework powering agents within Google products like Agent space and the Google Customer Engagement Suite (CES). ADK is designed to be flexible, allowing the use of different models and enabling the building of production-ready agents for various deployment environments.

Key features of the ADK include:

• Multi-Agent by Design: Build modular and scalable applications by composing multiple specialized agents in a hierarchy, enabling complex coordination and delegation. ADK supports various agent types, including LLM Agents, Workflow Agents (Sequential, Parallel, Loop), and Custom Agents.  
• Rich Tool Ecosystem: Equip agents with diverse capabilities by using pre-built tools (like Search and Code Execution), creating custom functions, integrating third-party libraries (such as LangChain and CrewAI), or even using other agents as tools. ADK also supports integration with Google Cloud tools and services.  
• Built-in Streaming: Interact with your agents in human-like conversations with ADK’s unique bidirectional audio and video streaming capabilities.  
• Integrated Developer Experience: Develop, test, and debug locally with a powerful CLI and a visual Web UI, allowing step-by-step inspection of events, state, and agent execution. The CLI offers commands like adk web and adk run for smooth development.  
• Deployment Ready: Containerize and deploy your agents anywhere – run locally, scale with Vertex AI Agent Engine, or integrate into custom infrastructure using Cloud Run or Docker. Agent Engine is a fully managed runtime in Vertex AI that simplifies the deployment of agents to production.  
• Built-in Evaluation: Systematically assess agent performance by evaluating both the final response quality and the step-by-step execution trajectory against predefined test cases.  
• Artifact Management: Store and load files or binary data tied to a session, which is useful for building stateful agents.  
• Guardrail Callbacks: Implement safety controls to moderate agent responses, restrict access with identity permissions, screen inputs, and monitor agent behavior.  
• Support for Multiple Models: ADK offers built-in integration for LiteLLM and Vertex AI Model Garden, supporting a variety of models, including Gemini, GPT-4o, Claude Sonnet, and LLaMA.

The Power of Autonomous Agents: Real-World Applications

The potential applications of AI agents developed with Google’s new kit are vast and span numerous industries. While the initial release focuses on developer tools, the possibilities for end-user applications are significant. Examples include:  

• Enhanced Customer Support: AI agents can handle a wide range of customer inquiries, provide personalized assistance, and resolve issues efficiently.  
• Intelligent Personal Assistants: Future assistants could autonomously manage complex tasks like scheduling, travel planning, and smart home automation.  
• Optimized Business Operations: Agents can automate intricate workflows, manage supply chains, analyze data for insights, and support decision-making.  
• Advanced Robotics and Automation: Integration with robotic systems could lead to robots capable of performing complex tasks in various environments.  
• Personalized Education and Tutoring: AI agents could create customized learning experiences and provide adaptive feedback to students.  

Why Google’s New Agent Development Kit Matters

The introduction of Google’s newest Agent Development Kit (ADK) signifies a significant step forward in the accessibility and sophistication of AI agent development. By providing a comprehensive and user-friendly toolkit, Google aims to:  

• Democratize AI Agent Creation: Lower the barrier to entry for developers to build intelligent agents.  
• Leverage Google’s AI Expertise: Provide access to state-of-the-art models and techniques optimized for agent behavior.  
• Accelerate Development Cycles: Reduce the time and effort required to build complex AI agents.  
• Foster Innovation: Encourage experimentation and push the boundaries of what’s possible with intelligent agents.  
• Ensure Scalability and Reliability: Offer integration with Google Cloud infrastructure for building highly scalable and reliable deployments.  
• Promote Interoperability: With the introduction of the Agent2Agent (A2A) protocol, Google aims to enable agents built on different frameworks to communicate and collaborate seamlessly.

Getting Started with Google’s Agent Development Kit: A Practical Guide

To get started with Google’s Agent Development Kit (ADK), follow these general steps based on the official documentation and common software development practices :  

1. Set Up a Google Cloud Project: Create or select a Google Cloud project and enable billing.  
2. Enable the Vertex AI API: Ensure the Vertex AI API is enabled for your project.  
3. Set Up Google Cloud CLI: Install and initialize the Google Cloud CLI and authenticate your application.  
4. Install ADK: Create a virtual environment and install the ADK using pip.  
5. Create an Agent: Define your agent’s behavior, tools, and orchestration logic using Python code.  
6. Run and Test Your Agent: Use the ADK CLI to run your agent locally and access the developer Web UI for testing and debugging.

To write your first agent using the Agent Development Kit, start by installing the official python library as shown below:

pip install google-adk

Then kick it off by using the following snippet:

from google.adk.agents import LlmAgent 
from google.adk.tools import google_Search

dice_agent = LlmAgent(
    model="gemini-2.0-flash-exp", # Required: Specify the LLM 
    name="question_answer_agent", # Required: Unique agent name
    description="A helpful assistant agent that can answer questions.",
    instruction="""Respond to the query using google search""",
    tools=[google_search], # Provide an instance of the tool
)

For more detailed guides and sample agents, refer to the official ADK documentation available on GitHub and also check out the official youtube video.