Introduction to Google ADK

The field of artificial intelligence is rapidly evolving from generative models that respond to prompts toward agentic systems designed to autonomously pursue goals. This shift from passive responders to proactive problem-solvers represents a significant leap in the capabilities and usefulness of AI.

However, this increased capability brings with it a new set of engineering challenges. Building a simple chatbot that answers questions is one thing; building a reliable system that can reason, plan, and execute a series of actions using external tools is another entirely. Such systems require a structured and disciplined approach that prioritizes reliability, maintainability, and scalability. This is why a dedicated agent development framework is essential.

The Google Agent Development Kit (ADK) is a framework created to address these engineering challenges directly, providing the necessary tools and structure for building professional, production-ready AI agents. Let’s explore it in detail.

What is the Google ADK?

At its core, the Google Agent Development Kit (ADK) is an open-source, code-first framework for building, evaluating, and deploying sophisticated AI agents and multi-agent systems. Released by Google on April 9, 2025, it is the same production-proven toolkit used to power agentic systems within Google’s own enterprise products. This origin is important, as it means the ADK was designed from the ground up to meet the demands of real-world production environments.

It is more than just a library of functions; it is a comprehensive toolkit that provides the structure, components, and best practices needed for the entire agent development process. Let’s explore the key capabilities that define the ADK.

Multi-agent by design

One of the most foundational principles of the ADK is that it is built to support multi-agent systems. In traditional software engineering, a common best practice is to break down a large, complex problem into smaller, manageable modules. Each module has a specific, well-defined responsibility. This approach, often called modularity or separation of concerns, makes the resulting system easier to build, debug, and maintain.

The ADK applies this same principle to AI agents. Instead of creating a single, monolithic agent that tries to do everything, the framework encourages us to build a team of smaller, specialized agents. Each agent can be an expert at a single task.

A rich tool ecosystem

An agent’s true power comes from their ability to interact with the outside world. An LLM’s knowledge is static and limited to its training data. To perform timely and relevant tasks, an agent needs access to external information and the ability to execute actions. In the ADK, these capabilities are provided by tools. A tool is essentially a function or service that an agent can call upon to perform a specific action.

The ADK provides a flexible and powerful system for equipping agents with tools, which can include:

• Custom Python functions: Any Python function can be easily converted into a tool for an agent. This is the primary way we will give our agents custom capabilities, such as writing a file to disk or calling a specific internal API.

• Built-in tools: The ADK comes with a set of prebuilt tools for common services, such as Google Search, allowing an agent to access real-time information from the web with minimal configuration.

• Agents as tools: In a multi-agent system, one agent can be used as a tool by another. This enables a powerful hierarchical structure where a manager agent can delegate complex sub-tasks to subordinate agents, who then report back with their results.

• Third-party integrations: The ADK is designed to be interoperable with the broader AI ecosystem. It can integrate with and leverage tools from popular libraries like LangChain and LlamaIndex, allowing us to incorporate existing functionalities into our ADK agents.

Deterministic orchestration

While allowing an LLM-powered agent to dynamically decide which tool to use next is incredibly flexible, some business processes require predictable, repeatable steps. For these scenarios, the ADK provides a set of special agents called Workflow Agents. These agents are not driven by an LLM’s reasoning but instead follow a deterministic, predefined logic to control the flow of execution. This gives us precise control over how and when our agents run.

The primary Workflow Agents are as follows:

• SequentialAgent: It executes a list of agents or tools in a strict, linear order. The output of one step is passed as the input to the next, creating a reliable pipeline.

• ParallelAgent: It executes multiple agents or tools simultaneously. This is useful for tasks that are not dependent on each other and can be run in parallel to save time, such as gathering information from multiple sources at once.

• LoopAgent: It repeats a task or a series of tasks until a specific condition is met. This is ideal for processes like gathering a certain number of research sources or polling a service until a result is ready.

A professional framework does more than just provide features; it establishes a clear and repeatable process for building high-quality applications. The ADK achieves this through its well-defined development life cycle.

The ADK development life cycle

A key advantage of using a mature framework is that it provides a structured and repeatable process for taking an idea from conception to production. The ADK establishes a clear, four-phase development life cycle that guides us through building, testing, and deploying our agents professionally.

Let’s walk through each of these phases.

1. Build: This is the core development phase where we write the code for our agents. It involves defining an agent’s purpose, equipping it with the necessary tools to perform its tasks, and configuring its behavior. The ADK’s code-first approach means that our agent’s entire definition (its logic, tools, and configuration) lives in our codebase. This allows us to use standard software engineering practices like version control, code reviews, and automated builds.

2. Interact: Once an agent is built, we need a way to run it, test it, and debug its behavior. The ADK includes a command-line interface (CLI) and a simple web-based user interface for this purpose. These tools allow us to interact with our agents on our local machine, sending them messages and observing their step-by-step reasoning process. This interactive feedback loop is crucial for rapidly iterating on an agent’s design and prompts.

3. Evaluate: Perhaps the most critical phase for building professional-grade AI systems is evaluation. It is not enough to manually test an agent a few times and assume it works. The ADK includes a built-in evaluation framework that allows us to create a suite of automated tests. We can define specific test cases with clear success criteria and run these tests automatically. This systematic approach allows us to measure the quality of our agent’s performance, catch regressions when we make changes, and provide objective evidence that our system is reliable.

4. Deploy: The final phase is to package our agent and deploy it so that it can be used by end-users or other applications. Because an ADK application is self-contained, it can be easily containerized. This creates a portable, lightweight image of our agent that can be run anywhere, such as on a local server, in a private data center, or on any cloud provider. This decoupling of the agent from the underlying infrastructure is a core principle of modern application development.

This structured life cycle provides a robust foundation for building an agent. To ensure this foundation can support a wide range of real-world applications, the ADK is built upon two key principles of flexibility and openness.

A flexible and agnostic framework

Two of the most important philosophical pillars of the ADK are its commitments to being model-agnostic and deployment-agnostic. These principles ensure that we are not locked into a single ecosystem and can adapt our solution as the technology evolves.

Model-agnostic

While the ADK is developed by Google and is highly optimized for use with its state-of-the-art Gemini family of models, it is not exclusively tied to them. The framework is designed with a pluggable architecture that allows it to work with a wide variety of large language models from different providers. Through integrations with libraries like LiteLLMIt is a Python library that provides a single, unified interface to call over 100 different LLM APIs, simplifying the process of switching between model providers., we can configure our ADK agents to use models from OpenAI, Anthropic, Mistral, and many others. This flexibility allows us to choose the best model for a specific task based on its performance, cost, or other requirements.

Deployment-agnostic

Similarly, an agent built with the ADK is not tied to a specific hosting environment. The ability to containerize our agent application means we have full control over how we deploy it. We can run it on a developer’s laptop for testing, deploy it to an on-premises server for internal use, or run it at scale in the cloud. This portability ensures that our work is not locked into a single platform and can be integrated into any existing infrastructure. For cloud-native deployments, it is particularly well-suited for serverless platforms like Google Cloud Run, which can automatically scale our agent based on demand.

Multi-language support

The Google ADK is a polyglot framework with official implementations in major programming languages, like Python, Java, and Go. This allows development teams to build agents using the language that best fits their existing technology stack and expertise.

Note: Throughout this course, our focus will be exclusively on the Python implementation of the ADK, which is the most mature and widely used language for AI and machine learning development.

The Google ADK provides the structure, tools, and best practices necessary to elevate agent development into a reliable and professional engineering discipline. By embracing a code-first, multi-agent, and life cycle-aware approach, it enables us to build AI systems that are not only powerful and intelligent but also reliable, testable, and ready for the demands of production environments. It serves as an important bridge between the raw potential of LLMs and the robust requirements of real-world applications.

Now that we have a solid high-level understanding of the framework’s philosophy and architecture, our next step is to take a closer look at how it works.