Google Antigravity: Customize Your AI Agents Effectively

Google Antigravity: A Breakthrough for AI Agents

The era of agent-centric artificial intelligence has truly arrived, and Google Antigravity is positioning itself as a key player in this revolution. This tool offers unprecedented possibilities for building highly customizable AI agents, relying on three fundamental concepts: rules, skills, and workflows.

Exploring the Key Concepts of Google Antigravity

Rules: Rules are the basic constraints that dictate the behavior of agents. They allow the agent to conform to specific standards, such as formatting code according to PEP 8 guidelines. These rules are defined in markdown files, making them easy to manage and update. For example, a rule might state that all code must be formatted using the black tool, and that only open-source libraries can be used to ensure project compatibility.

Skills: Skills represent sets of knowledge that the agent uses to accomplish specific tasks. For instance, a skill may be dedicated to generating tests for Python code. These skills are stored in a dedicated folder containing a SKILL.md file, allowing for easy reuse across different projects. A skill could include the ability to generate unit tests using Pytest, a popular tool in Python development.

Workflows: Workflows orchestrate all the actions of the agent, guiding it through a well-structured action plan. They are invoked by specific commands, such as /deploy, and allow for the automation of complex tasks without loss of precision. A typical workflow might include several steps, such as code review, refactoring, and test generation, all smoothly coordinated by the agent.

Putting It Into Practice: Setting Up a QA Agent for Code

To illustrate the effectiveness of Google Antigravity, we will set up a quality assurance (QA) agent workflow for Python code. Before we begin, it is essential to download and install Google Antigravity on your computer. Once the installation is complete, open the desktop application and create a Python project folder.

At the root of the project directory, create a folder named .agents. Inside this folder, create two subfolders: rules and skills. These folders will be used to define the rules and skills of our agent.

Defining a Python Style Rule

Let's start by defining a rule that will ensure the agent adheres to Python formatting standards. Create a file named python-style.md in the rules subfolder and paste the following content:

# Python Style Rule

Always use PEP 8 standards. When providing or refactoring code, assume we are using `black` for formatting. Keep dependencies strictly to free and open-source libraries to ensure our project remains compatible with free software.

To customize this rule, open the agent's customization panel and select the rule you just defined. Set the activation pattern to "glob" and specify the glob pattern: **/*.py.

Teaching Skills to the Agent

Next, we will teach the agent an essential skill: performing robust tests on Python code. In the skills subfolder, create a folder named pytest-generator. Inside, create a file SKILL.md with the following content:

# Skill: Pytest Generator

This skill enables the agent to generate comprehensive unit tests for Python code using the Pytest framework.

Integrating the Elements and Running the Agent

To implement these elements, we will create a Python file containing "bad quality" code in our workspace. For example, create a file named flawed_division.py with the following code:

def divide_numbers(x, y):

This code is intentionally messy to test our agent's capabilities. Create a new workflow named qa-check with the following content:

# Title: Python QA Check

# Description: Automates code review and test generation for Python files.

- Step 1: Review the currently open Python file for bugs and style issues, adhering to our Python style rule.
- Step 2: Refactor any inefficient code.
- Step 3: Call the **pytest-generator** skill to write comprehensive unit tests for the refactored code.
- Step 4: Output the final test code and suggest running **pytest** in the terminal.

With the messy Python file open, activate the agent by typing the command qa-check and pressing Enter to execute the workflow.

Results Achieved by the Agent

After execution, the agent will review the code and automatically propose a new version in the Python file. Additionally, it will generate comprehensive unit tests that you can use to run various types of tests with pytest. Here’s an example of what these tests might include:

from flawed_division import divide_numbers

def test_divide_numbers_normal():
    assert divide_numbers(10, 2) == 5.0
    assert divide_numbers(9, 3) == 3.0

def test_divide_numbers_negative():
    assert divide_numbers(-10, 2) == -5.0
    assert divide_numbers(10, -2) == -5.0
    assert divide_numbers(-10, -2) == 5.0

def test_divide_numbers_float():
    assert divide_numbers(5.0, 2.0) == 2.5

def test_divide_numbers_zero_numerator():
    assert divide_numbers(0, 5) == 0.0

def test_divide_numbers_zero_denominator():
    with pytest.raises(ValueError, match="Cannot divide by zero"):
        divide_numbers(10, 0)

In conclusion, Google Antigravity allows generic agents to be transformed into specialized tools capable of efficiently managing complex and repetitive tasks in software development. Through the integration of rules, skills, and workflows, developers can now automate entire processes, thereby improving the quality and efficiency of their projects.