As AI tools evolve beyond basic language models, Google’s Agent Development Kit (ADK) offers developers a powerful toolkit to create intelligent, action-taking agents. One of its standout features is the ability to build search-driven agents—ideal for crafting applications that can browse, summarize, and contextualize web content dynamically. Let’s explore how you can build one.

Why ADK is a Game-Changer for AI Agent Development

At its core, ADK bridges the gap between language models and external tools, turning passive LLMs into proactive agents. These agents can search, execute functions, and adapt to complex workflows—all triggered by natural language input.

ADK supports three categories of tools:

• Custom Functions – Define Python-based tools tailored to specific needs.

• Google Native Tools – Leverage built-in capabilities like live web search and code execution.

• Third-Party Integrations – Connect frameworks like LangChain or CrewAI for advanced orchestration.

For search-based applications, ADK’s native Google Search tool taps into live web content, giving your agent the ability to respond in real time with relevant information.

Step-by-Step: Building Your First Search Agent

1. Set Up Your Project

Begin by installing ADK in your Python environment:

bash

pip install google-adk

Organize your files in the recommended structure:

bash

your_project/

├── search_agent/

│ ├── __init__.py

│ ├── agent.py

│ └── .env

2. Create the Search Agent

Inside agent.py, define a simple agent using Google’s built-in search tool:

python

from google.adk.agents import Agent

from google.adk.runners import Runner

from google.adk.sessions import InMemorySessionService

from google.adk.tools import google_search

from google.genai import types

APP_NAME = "web_search_agent"

USER_ID = "user001"

SESSION_ID = "sess001"

search_agent = Agent(

name="web_lookup_agent",

model="gemini-2.0-flash",

description="Fetches and summarizes online content.",

instruction="I answer questions by searching the web. Ask me anything!",

tools=[google_search] )

session_service = InMemorySessionService()

session = session_service.create_session(APP_NAME, USER_ID, SESSION_ID)

runner = Runner(agent=search_agent, app_name=APP_NAME, session_service=session_service)

def query_agent(prompt):

content = types.Content(role="user", parts=[types.Part(text=prompt)])

events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)

for event in events:

if event.is_final_response():

print("Agent Response:", event.content.parts[0].text)

query_agent("latest updates in artificial intelligence")

This code launches a basic search-enabled AI agent, capable of answering questions with fresh data from the web.

3. Add Custom Functions to Expand Agent Capabilities

In addition to built-in tools, you can build Python functions that the agent can call when needed. For example, you could create a custom tool to provide current timestamps, generate summaries, or interface with APIs.

Design Tips for Building Robust Search Agents

Here are some field-tested practices for working with ADK:

• Use Explicit Return Formats – Always return structured dictionaries from your custom tools for better response formatting.

• Write Clear Docstrings – ADK agents decide when to use a tool based on its docstring. Be precise and informative.

• Add Type Annotations – Help the agent understand what input parameters are required.

• Avoid Optional Parameters – The current ADK implementation doesn’t handle default values well. Always define full parameter sets.

• Mind Tool Limitations – Only one built-in tool can be active per agent, and you can’t mix built-in tools with custom ones. However, you can combine multiple custom functions.

Final Thoughts

Google’s ADK opens the door to more than just static AI responses—it enables agents that think, act, and search dynamically. With built-in tools like live search and the flexibility to add custom logic, developers now have the power to build agents that operate with real-world awareness. Whether you're crafting assistants, research bots, or domain-specific helpers, the combination of Gemini models and ADK tools is a formidable foundation.