AI agents are no longer just research concepts — they’re being deployed in production systems across industries. Google’s Agent Development Kit (ADK) makes it easier than ever to build, orchestrate, and deploy intelligent agents using Python. In this guide, I’ll walk you through building your first AI agent from scratch using Google ADK.
What is Google ADK?
Google ADK (Agent Development Kit) is an open-source framework designed to help developers build multi-agent AI systems. It provides:
• A structured way to define agents and their tools
• Built-in orchestration for sequential and parallel workflows
• Native integration with Gemini AI models
• YAML-based configuration for agent behavior
Think of it as the backbone that connects your AI model to real-world actions.
Prerequisites
Before we start, make sure you have:
• Python 3.9+
• A Google Cloud account
• Gemini API access
• Basic Python knowledge
Installation
pip install google-adk
pip install google-generativeai
Building Your First Agent
Let’s build a simple research agent that can answer questions using tools.
Step 1 — Define Your Tool
Tools are functions your agent can call to interact with the world.
def search_knowledge_base(query: str) -> str:
"""Search internal knowledge base for information."""
# Your logic here
return f"Results for: {query}"
Step 2 — Create the Agent
from google.adk.agents import Agent
from google.adk.models import Gemini
agent = Agent(
name="research_agent",
model=Gemini(model="gemini-2.0-flash"),
description="An agent that answers questions",
instruction="You are a helpful research assistant. Use your tools to find accurate information.",
tools=[search_knowledge_base]
)
Step 3 — Run the Agent
from google.adk.runners import Runner
runner = Runner(agent=agent)
response = runner.run("What is machine learning?")
print(response)
How It Works
When you send a query to the agent:
1. Gemini receives your input
2. Decides whether to use a tool or respond directly
3. Calls the tool if needed and gets results
4. Formulates a final response
This loop is what makes agents powerful — they can reason, act, and respond dynamically.
Real-World Use Case
At my day job, I use Google ADK to build multi-agent pipelines for banking compliance automation. One pipeline I built has 9 sequential agents — each handling a specific task like schema discovery, data sampling, and SQL generation. ADK made orchestrating all of them clean and maintainable.
What’s Next?
Once you’re comfortable with a single agent, you can:
• Chain multiple agents sequentially
• Run agents in parallel for faster pipelines
• Add memory and context persistence
• Integrate with databases and external APIs
Conclusion
Google ADK is one of the most practical frameworks for building production-grade AI agents today. With just a few lines of Python, you can have an intelligent agent running and ready to extend.
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