Lab 8: Creating a "Researcher" Agent with Google Search Challenge

Goal

Your task is to build and configure a new agent that can search the web to answer questions about current events.

Create a new agent named researcher_agent using the adk create command, specifying the Python type.
Enable the Vertex AI API in your Google Cloud project.
Configure the .env file inside the researcher_agent directory to use Vertex AI, providing your project ID and a location.
Modify the agent.py file (Python approach):
- Define an LlmAgent named root_agent.
- Set the model to gemini-2.5-flash.
- Write a clear instruction that directs the agent to use a search tool for any questions about recent or up-to-date information.
- Add the google_search tool to the tools list.
Run the agent from your main adk-training directory using the adk web command (without specifying the agent name).
Test the agent by asking it a question about a recent event (e.g., "Who won the last major sports championship?").
Verify that the google_search tool was used by inspecting the "Trace" view in the Dev UI.

Why is it important to explicitly instruct the agent when to use the google_search tool? What might happen if you just gave it the tool with no instructions?
The google_search tool requires a Vertex AI configuration. Why do you think this is a requirement, as opposed to using a simple Google AI Studio API key?
How does giving an agent access to real-time information fundamentally change the kinds of problems it can solve compared to an agent that only relies on its internal knowledge?

Looking for the solution? Here's a hint (Base64 decode me): L2RvYy1hZGstdHJhaW5pbmcvbW9kdWxlMDgtaW50cm8tdG8tdG9vbHMvbGFiLXNvbHV0aW9u

The direct link is: Lab Solution