Google DeepMind Folds Computer Use Directly Into Gemini 3.5 Flash

Google DeepMind just folded computer use directly into Gemini 3.5 Flash as a native built-in tool. That means the same model you already use for function calling, Search grounding, and Maps integration can now also see a screen, click, type, scroll, and navigate software without any human at the keyboard. It is available right now through the Gemini API and the Gemini Enterprise Agent Platform.

From two models to one

This is a consolidation story as much as a capability story. Previously, developers had to call a separate Gemini 2.5 Computer Use model for browser-based agent tasks, feeding it screenshots and receiving structured commands in a loop. Now, computer use is a native tool inside the same model developers already use for function calling, Search grounding, and Maps integration , meaning a single Gemini 3.5 Flash agent can see a screen, look something up on Search, and interact with a map without routing requests between multiple models.

The practical implication: agent pipelines that previously required orchestrating two separate models can now be expressed as a single tool call. Computer use now sits alongside code execution, search, and function calling as a native tool inside Flash, the model Google launched at I/O 2026 as its fastest agentic AI model.

How it actually works

The model operates through a continuous observe-think-act loop. Your application captures a screenshot of the target environment and sends it to the API along with the user's goal. The model analyzes the screen and returns a structured action , a click, a scroll, a keystroke , along with an intent field explaining its reasoning. Your code executes that action, captures a new screenshot, and sends it back. Repeat until done.

Here is the minimal setup using the new Interactions API:

from google import genai
client = genai.Client()
interaction = client.interactions.create(
    model="gemini-3.5-flash",
    input="Search for 'Gemini API' on Google.",
    tools=[{
        "type": "computer_use",
        "environment": "browser",
        "enable_prompt_injection_detection": True
    }]
)
print(interaction)