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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 a substitute of simply producing code reactively, genuinely perceive the distinctive processes behind code era.
Google Antigravity has loads to say on this matter. This device 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 learn to hyperlink these key ideas collectively to construct extra strong brokers and highly effective automated pipelines. Particularly, we are going to carry out a step-by-step course of to arrange a code high quality assurance (QA) agent workflow, based mostly on specified guidelines and expertise. Off we go!
# Understanding the Three Core Ideas
Earlier than getting our fingers 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 habits, in addition to how one can adapt it to our stack and match our fashion. They’re saved as markdown information.
- Talent: Think about expertise as a reusable bundle containing data that instructs the agent on how one can deal with a concrete job. They’re allotted in a devoted folder that accommodates 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 how one can configure Antigravity to assessment Python code, apply appropriate formatting, and generate exams — all with out the necessity for added 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 device, 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…” choice within the higher menu toolbar.
Say you might 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 are going to create two subfolders: one referred to as guidelines and one named expertise. Chances are you’ll guess that these two are the place we are going to outline the 2 pillars for our agent’s habits: 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. Contained in the guidelines subfolder, you may create a file named python-style.md and paste the next content material:
# Python Fashion Rule
All the time use PEP 8 requirements. When offering or refactoring code, assume we're utilizing `black` for formatting. Hold 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 might be launched later at all times applies the rule outlined once we are particularly engaged on Python scripts.
Subsequent, it is time to outline (or “train”) the agent some expertise. That would be the ability of performing strong exams on Python code — one thing extraordinarily helpful in at this time’s demanding software program improvement panorama. Contained in the expertise subfolder, we are going to 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 have no, 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 observed this Python code is deliberately messy and flawed. Let’s examine 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 are going to name qa-check, with this content material:
# Title: Python QA Verify
# Description: Automates code assessment and take a look at era for Python information.
Step 1: Assessment the at present open Python file for bugs and elegance points, adhering to our Python Fashion Rule.
Step 2: Refactor any inefficient code.
Step 3: Name the `pytest-generator` ability to jot down complete unit exams 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 a complete. With the messy Python file nonetheless open within the workspace, we are going to 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 may have revised the code and robotically recommended a brand new model within the Python file, as proven beneath:
The refactored code recommended by the agent | Picture by Writer
However that is not all: the agent additionally comes with the excellent high quality examine we have been on the lookout for by producing plenty of code excerpts you need to use to run various kinds of exams utilizing pytest. For the sake of illustration, that is what a few of these exams might appear to be:
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't divide by zero"):
divide_numbers(10, 0)
All this sequential course of carried out by the agent has consisted of first analyzing the code underneath the constraints we outlined by guidelines, then autonomously calling the newly outlined ability to supply a complete testing technique tailor-made to our codebase.
# Wrapping Up
Trying again, on this article, now we have proven how one can mix three key parts of Google Antigravity — guidelines, expertise, and workflows — to show generic brokers into specialised, strong, and environment friendly workmates. We illustrated how one can make an agent specialised in appropriately formatting messy code and defining QA exams.
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 actual world.
