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# Introduction
Likelihood is, you have already got the sensation that the brand new, agent-first synthetic intelligence period is right here, with builders resorting to new instruments that, as an alternative of simply producing code reactively, genuinely perceive the distinctive processes behind code technology.
Google Antigravity has rather a lot to say on this matter. This software holds the important thing to constructing extremely customizable brokers. This text unveils a part of its potential by demystifying three cornerstone ideas: guidelines, expertise, and workflows.
On this article, you may discover ways to hyperlink these key ideas collectively to construct extra sturdy brokers and highly effective automated pipelines. Particularly, we’ll carry out a step-by-step course of to arrange a code high quality assurance (QA) agent workflow, primarily based on specified guidelines and expertise. Off we go!
# Understanding the Three Core Ideas
Earlier than getting our palms soiled, it’s handy to interrupt down the next three parts belonging to the Google Antigravity ecosystem:
- Rule: These are the baseline constraints that dictate the agent’s conduct, in addition to tips on how to adapt it to our stack and match our type. They’re saved as markdown recordsdata.
- Talent: Take into account expertise as a reusable bundle containing information that instructs the agent on tips on how to deal with a concrete process. They’re allotted in a devoted folder that comprises a file named SKILL.md.
- Workflow: These are the orchestrators that put all of it collectively. Workflows are invoked through the use of command-like directions preceded by a ahead slash, e.g. /deploy. Merely put, workflows information the agent by an motion plan or trajectory that’s well-structured and consists of a number of steps. That is the important thing to automating repetitive duties with out lack of precision.
# Taking Motion
Let’s transfer on to our sensible instance. We are going to see tips on how to configure Antigravity to evaluation Python code, apply appropriate formatting, and generate checks — all with out the necessity for extra third-party instruments.
Earlier than taking these steps, ensure you have downloaded and put in Google Antigravity in your laptop first.
As soon as put in, open the desktop utility and open your Python mission folder — if you’re new to the software, you can be requested to outline a folder in your laptop file system to behave because the mission folder. Regardless, the best way so as to add a manually created folder into Antigravity is thru the “File >> Add Folder to Workspace…” possibility within the higher menu toolbar.
Say you could have a brand new, empty workspace folder. Within the root of the mission listing (left-hand facet), create a brand new folder and provides it the title .brokers. Inside this folder, we’ll create two subfolders: one known as guidelines and one named expertise. You could guess that these two are the place we’ll outline the 2 pillars for our agent’s conduct: guidelines and expertise.
The mission folder hierarchy | Picture by Writer
Let’s outline a rule first, containing our baseline constraints that can make sure the agent’s adherence to Python formatting requirements. We do not want verbose syntax to do that: in Antigravity, we outline it utilizing clear directions in pure language. Inside the principles subfolder, you may create a file named python-style.md and paste the next content material:
# Python Type Rule
At all times use PEP 8 requirements. When offering or refactoring code, assume we’re utilizing `black` for formatting. Maintain dependencies strictly to free, open-source libraries to make sure our mission stays free-friendly.
If you wish to nail it, go to the agent customizations panel that prompts on the right-hand facet of the editor, open it, and discover and choose the rule we simply outlined:
Customizing the activation of agent guidelines | Picture by Writer
Customization choices will seem above the file we simply edited. Set the activation mannequin to “glob” and specify this glob sample: **/*.py, as proven beneath:
Setting the glob activation mode | Picture by Writer
With this, you simply ensured the agent that can be launched later at all times applies the rule outlined after we are particularly engaged on Python scripts.
Subsequent, it is time to outline (or “educate”) the agent some expertise. That would be the talent of performing sturdy checks on Python code — one thing extraordinarily helpful in as we speak’s demanding software program growth panorama. Inside the talents subfolder, we’ll create one other folder with the title pytest-generator. Create a SKILL.md file inside it, with the next content material:
Defining agent expertise inside the workspace | Picture by Writer
Now it is time to put all of it collectively and launch our agent, however not with out having inside our mission workspace an instance Python file containing “poor-quality” code first to strive all of it on. If you haven’t any, strive creating a brand new .py file, calling it one thing like flawed_division.py within the root listing, and add this code:
def divide_numbers( x,y ):
return x/y
You could have seen this Python code is deliberately messy and flawed. Let’s have a look at what our agent can do about it. Go to the customization panel on the right-hand facet, and this time give attention to the “Workflows” navigation pane. Click on “+Workspace” to create a brand new workflow we’ll name qa-check, with this content material:
# Title: Python QA Examine
# Description: Automates code evaluation and take a look at technology for Python recordsdata.
Step 1: Overview the presently open Python file for bugs and elegance points, adhering to our Python Type Rule.
Step 2: Refactor any inefficient code.
Step 3: Name the `pytest-generator` talent to put in writing complete unit checks for the refactored code.
Step 4: Output the ultimate take a look at code and counsel working `pytest` within the terminal.
All these items, when glued collectively by the agent, will remodel the event loop as an entire. With the messy Python file nonetheless open within the workspace, we’ll put our agent to work by clicking the agent icon within the right-hand facet panel, typing the qa-check command, and hitting enter to run the agent:
Invoking the QA workflow by way of the agent console | Picture by Writer
After execution, the agent could have revised the code and mechanically advised a brand new model within the Python file, as proven beneath:
The refactored code advised by the agent | Picture by Writer
However that is not all: the agent additionally comes with the great high quality test we had been in search of by producing various code excerpts you should utilize to run several types of checks utilizing pytest. For the sake of illustration, that is what a few of these checks may seem like:
import pytest
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=”Can not divide by zero”):
divide_numbers(10, 0)
All this sequential course of carried out by the agent has consisted of first analyzing the code beneath the constraints we outlined by guidelines, then autonomously calling the newly outlined talent to supply a complete testing technique tailor-made to our codebase.
# Wrapping Up
Wanting again, on this article, we now have proven tips on how to mix three key parts of Google Antigravity — guidelines, expertise, and workflows — to show generic brokers into specialised, sturdy, and environment friendly workmates. We illustrated tips on how to make an agent specialised in accurately formatting messy code and defining QA checks.
Iván Palomares Carrascosa is a frontrunner, author, speaker, and adviser in AI, machine studying, deep studying & LLMs. He trains and guides others in harnessing AI in the true world.
