Production-Ready AI Agents Faster: Leveraging Google Antigravity + Gemini 3 (Pro & Flash Thinking)

Overview

In the rapidly changing world of AI development things need to move in a faster and more efficient way possible.
Google Antigravity together with Gemini 3 Pro represents a paradigm shift in how software developers used to build
intelligent agent systems.

This detailed visual and step-by-step guide takes you through the entire agent development lifecycle process from
initial prompt generation to production deployment using real screenshots and implementation steps.

At Quadrant Technologies, we showcase a practical walk-through of Google Antigravity simulating how exactly the
framework transforms simple natural language prompts into production ready multi-agent applications.

The Journey: Seven Steps from Concept to Reality

Step 1: The Prompt – Where It All Begins

The journey begins in Google Antigravity’s intuitive interface where developers describe their desired agent system
using natural language. As shown in the screenshot, Antigravity provides a Planning Mode where the AI analyzes your
requirements before execution, Gemini 3 Pro (High Intelligence) model selection for advanced reasoning capabilities,
real-time progress tracking during agent creation, and automatic file generation for configuration and entry points –
transforming simple text descriptions into complete, structured agent systems with defined roles, tool assignments,
and expected deliverables.

For our LeadDeveloper agent, this starts with a clear instruction:

Create a Google Antigravity agent named ‘LeadDeveloper’. It should have a ‘Designer’ sub-agent to research portfolio
trends using the browser, and a ‘Coder’ sub-agent to write the HTML/CSS using the code editor. Output the agent.yaml
configuration and the index.js entry point.

This single prompt triggers the entire orchestration process, setting the stage for sophisticated multi-agent
architecture.

Step 2: Task Breakdown – Structured Development

Once the prompt is submitted, Antigravity automatically breaks down the work into a structured, hierarchical task
list with clear checkboxes for progress tracking. The Task panel displays main tasks with nested sub-tasks,
specifically for our LeadDeveloper project, this includes initializing the project structure, creating the
agent.yaml to define the Designer and Coder sub-agents, and generating the index.js entry
point.

Timestamps indicate recent activity, and a Review button allows for user approval before proceeding, ensuring that
every component of the LeadDeveloper system is correctly planned before implementation begins.

Step 3: Implementation Plan – The Blueprint

Before any code is written, The Implementation Plan artifact provides a detailed blueprint of the changes.

Goal Description

Define the agent architecture with specialized sub-agents, each equipped with specific tools for their designated
responsibilities (e.g., research agents with browser access, coding agents with file editing capabilities).

Proposed Changes

Configuration:

• [NEW] agent.yaml
  • Define the main LeadDeveloper orchestrator agent
  • Define the Designer sub-agent with browser tool assignments
  • Define the Coder sub-agent with code_editor tool assignments
  • Specify Gemini 3 Pro as the AI model for all agents

Entry Point:

• [NEW] index.js
  • Initialize the LeadDeveloper agent system
  • Implement message routing logic to delegate “design” tasks to the Designer and “coding” tasks to the Coder
  • Set up event handling and response management

Verification Plan:

• Inspect agent.yaml for correct syntax and tool definitions
• Verify index.js for proper initialization and delegation logic
• Test agent routing and sub-agent communication to ensure queries reach the correct specialist

Step 4: Walkthrough – Step-by-Step Execution

The Walkthrough artifact documents exactly what was simulated and how to use it. As shown in the screenshot,
Antigravity generates comprehensive documentation including verification confirmations, progress updates in the
right panel showing real-time file creation status, and a complete summary of the LeadDeveloper agent system.

Created Files:

1. agent.yaml – Defines the complete agent architecture:
   • LeadDeveloper: The main orchestrator.
   • Designer: Sub-agent assigned the browser tool for research.
   • Coder: Sub-agent assigned the code_editor tool for implementation.
   • Model: All agents configured with gemini-3-pro.
2. index.js – The entry point that initializes the system:
   • Initializes the LeadDeveloper agent instance.
   • Delegates tasks based on keyword detection.

Step 5: The YAML File – Agent Configuration

The screenshot shows the actual agent.yaml file created by Antigravity and displaying the complete agent
configuration with proper YAML syntax highlighting, line numbers for easy reference and the project structure.

• Agent Metadata: Defines the name LeadDeveloper, version 1.0.0, and description.
• Sub-Agents Array:
  • Designer: Configured with the browser tool and gemini-3-pro model.
  • Coder: Configured with the code_editor tool and gemini-3-pro model.
• Tools Definition: Lists the available capabilities for the system.

Step 6: Browser Extension Integration

The
Antigravity Browser Extension is the official Chrome extension that enables Antigravity agents to access and
interact with websites.

Simply click “Add to Chrome” from the Chrome Web Store to install it in your Antigravity Chrome profile and start
building with browser-enabled agents today.

Step 7: Browser Testing – Proof of Functionality

With the Antigravity browser extension, our agents run complete browser tests start to finish fully automated without help.
Navigation through pages, interacting with UI elements, capturing results in real-time while validating the workflows.
This automation process eliminates the need for manual QA and agents execute real user flows in the browser ensuring
accuracy and reliability.

The browser testing proof-of-concept for the LeadDeveloper agent demonstrates:

1. Code Completion: The agent correctly interpreting a prompt to write a mergeSort function.
2. Interactive Workflow: The agent successfully handling user input and generating appropriate responses in the chat interface.

This capability makes Antigravity a powerful platform for testing complex web applications with minimal human intervention.

Quadrant’s Perspective: Lessons from Real Implementation

With our daily operations at Quadrant Technologies, we are not just exploring these tools in isolation.
We are constantly integrating Google Antigravity and Gemini 3 Pro into live production workflows to work over real-world problems.
Antigravity not just another tool out in the market it’s a robust platform capable of handling and solving complex scenarios.

Moving forward, we are leveraging this technology to:

• Accelerate Development Cycles: By automating the boilerplate and structural setup of complex agent systems.
• Enhance Code Quality: Utilizing Gemini 3 Pro’s advanced reasoning to make sure best practices and regression safety from day one.
• Scale Autonomous Testing: deploying browser-enabled agents to perform continuous, end-to-end testing of our web applications.

Our journey with LeadDeveloper agent just feels like the beginning.
We see a future where such intelligent multi agent systems will become an integral part of our core development teams
which will drive innovation and efficiency across our entire project portfolio.

Here at Quadrant Technologies, We’re excited about how the AI-driven development will shape the future.
Google Antigravity and Gemini 3 aren’t just set of tools, it’s a cognitive platform that transforms how we will build
these future ready applications.

Token economics (why this matters when you’re shipping enterprise agents):

Everything above helps us build and validate agents faster but once an agent moves from demo → production,
the next question is simple:

What does it cost to run this agent 1,000+ times a day?

Enterprise-grade agents don’t make one call to an LLM.
They usually do multiple steps per request (plan → retrieve → reason → generate → verify),
and each step consumes tokens.
That’s why, at Quadrant, we look at price per 1M tokens as a practical benchmark for cost efficiency
especially for high-volume automations.

Pricing benchmark (API token rates)

Below is a simple benchmark of text token pricing for comparable, widely-used models:

A quick “real agent call” example (typical enterprise step)

A common pattern for one agent step is ~10k input tokens (instructions + context + retrieved chunks)
and 2k output tokens (final response / structured result):

• Gemini 3 Flash: (10k × $0.50/1M) + (2k × $3.00/1M) ≈ $0.011
• GPT-4o: (10k × $2.50/1M) + (2k × $10.00/1M) ≈ $0.045
• Claude Sonnet: (10k × $3.00/1M) + (2k × $15.00/1M) ≈ $0.060

What this means in practice: for high-volume enterprise automations, Gemini 3 Flash can be roughly 4× cheaper
than GPT-4o and ~5× cheaper than Claude Sonnet for a similar token pattern—so you can scale more workflows
without the cost curve exploding.

Kaivalya L Hanswadkar

Areef Shaik

Sai Saketh Cholleti