Getting Started with Google Cloud ADK — Blog Series #1

For developers and organizations looking to move beyond simple Retrieval-Augmented Generation (RAG) and single-function Large Language Model (LLM) calls, the future of AI lies in agentic systems — autonomous entities capable of reasoning, planning, and tool use.

The Google Agent Development Kit (ADK) is Google’s answer to this challenge. It is an open-source, code-first Python framework designed to simplify the end-to-end development, evaluation, and deployment of sophisticated AI agents and complex multi-agent systems.

The Agent Development Kit (ADK) is Google’s flexible, modular framework for building and deploying AI agents. While it's optimized for the Google ecosystem (e.g. Gemini / Vertex AI), ADK is designed to be model-agnostic and deployment-agnostic, meaning you can swap models, run locally or in production, and integrate with whatever infrastructure you prefer.

2. Architecture Explained

The Google Agent Development Kit (ADK) provides a modular and flexible foundation for creating complex AI applications. Rather than relying on a single large language model (LLM) for all tasks (as shown in the bottom-left Single Model Architecture for context), ADK focuses on building specialized, collaborative units.

he Base Agent is the foundational building block from which all specialized agents (like LLM Agents, Workflow Agents, or Custom Agents) are derived. Its configuration dictates the agent’s identity, intelligence, and capabilities:

• Name: A unique identifier for the agent, essential for multi-agent collaboration and routing.
• Model: Specifies the underlying Large Language Model (LLM) that powers the agent’s intelligence, such as Gemini (as highlighted in the Models box) or other supported models.
• Instruction: The system prompt that defines the agent’s persona, goals, constraints, and operational rules. This is how you make an agent a specialized expert (e.g., a “Flight Booking Agent”).
• Tools: These are the external capabilities the agent can invoke to perform actions or retrieve real-time data, connecting the LLM’s reasoning to the real world.
• Sub Agents: This crucial component enables Multi-Agent Systems. It allows an agent to delegate specific tasks to other specialized agents, creating a hierarchical or collaborative structure (e.g., a “Trip Planner” delegating to a “Hotel Agent”).

Tools in ADK

In ADK, Tools are action modules — code components that agents can invoke to perform tasks beyond pure text reasoning. They are not reasoning engines themselves. Instead:

• The agent’s model decides which tool to call and with what arguments.
• ADK executes the tool and passes the result back.
• The agent then uses this output to continue reasoning or generate a final response.

This mechanism gives agents “superpowers” — enabling them to connect with APIs, run computations, query data, or interact with external systems.

ADK offers flexibility by supporting several types of tools:

Function Tools: Tools created by you, tailored to your specific application’s needs.

• Functions/Methods: Define standard synchronous functions or methods in your code (e.g., Python def).
• Agents-as-Tools: Use another, potentially specialized, agent as a tool for a parent agent.
• Long Running Function Tools: Support for tools that perform asynchronous operations or take significant time to complete.

Built-in Tools: Ready-to-use tools provided by the framework for common tasks. Examples: Google Search, Code Execution, Retrieval-Augmented Generation (RAG).

Third-Party Tools: Integrate tools seamlessly from popular external libraries. Examples: LangChain Tools, CrewAI Tools.

Agents in ADK

In the Agent Development Kit (ADK), an Agent is a self-contained execution unit designed to act autonomously to achieve specific goals. Agents can perform tasks, interact with users, utilize external tools, and coordinate with other agents.

Google Documentation

Types of Agents in ADK

• LLM Agents — Powered by Large Language Models, they handle natural language understanding, reasoning, planning, and tool selection. Best for flexible, language-driven tasks.
• Workflow Agents — Manage the flow of other agents in sequence, parallel, or loop patterns. They don’t use LLMs for control, making them ideal for structured, predictable processes.
• Custom Agents — Built by extending BaseAgent for unique logic, specialized control flows, or integrations beyond standard agent types.

3. Create Your Very First ADK Agent — Step by Step

Watch this video for an easy, step-by-step guide to creating your first Agent.

Conclusion

In this blog, we explored the core concepts of Google Cloud ADK and how you can start building intelligent agents step by step. Remember — ADK is all about combining agents, tools, memory, and deployment into powerful workflows that move beyond simple prompts into real-world applications.

If you’d like to continue your learning journey, check out the full video playlist for this series:

About Me

As an experienced Fully certified (11x certified) Google Cloud Architect, Google Developer Expert (GDE), with over 9+ years of expertise in Google Cloud Networking,Data ,Devops, Security and ML, I am passionate about technology and innovation. Being a Champion Innovator and Google Cloud Architect, I am always exploring new ways to leverage cloud technologies to deliver innovative solutions that make a difference.

If you have any queries or would like to get in touch, you can reach me at Email address — vishal.bulbule@techtrapture.com or connect with me on LinkedIn at https://www.linkedin.com/in/vishal-bulbule/. For a more personal connection, you can also find me on Instagram at https://www.instagram.com/vishal_bulbule/?hl=en.

Additionally, please check out my YouTube Channel at https://www.youtube.com/@techtrapture for tutorials and demos on Google Cloud.