Google AI × Antigravity × Claude Code — A Developer's Day Using Three AI Tools in Combination

Why Use Three Tools?

In 2026, full-stack development has transformed. Instead of "one AI tool for everything," the strategy is "combine multiple AI tools, offsetting individual weaknesses, maximizing strengths."

Three tools stand out: Google AI Pro (research), Antigravity (UI/frontend), Claude Code (backend/CLI).

Clear role division:

• Google AI Pro: Project planning stage—market research, competitive analysis, trend identification
• Antigravity: User-visible parts (UI, layout, interaction) implementation
• Claude Code: Server-side, APIs, databases, system core

Integrating these produces remarkably efficient development cycles.

Project Example: "AI-Powered Task Management App"

For concrete understanding, a real project demonstrates this.

Project name: "AIDE (AI-Driven Efficient Scheduling)"

A simple task management app with advanced features: AI auto-classification, priority assignment, progress prediction. React frontend, Python FastAPI backend.

9am: Project Kickoff Meeting

Project officially begins. Rough requirements exist, but "what's actually possible?" and "how do similar market apps work?" need verification.

Market Research with Google AI Pro

9am, launch Google AI Pro's Deep Search.

Query: "2026 task management app trends Japan market"

Deep Search auto-traverses multiple websites, aggregates:

• Popular domestic task management apps (Notion, Asana, Microsoft To Do)
• Top 10 user-desired features
• AI-enabled app examples
• Pricing trends

Further consolidate with NotebookLM:

1. Upload Notion official site
2. Upload Asana product introduction
3. Upload multiple Japanese blog articles

Then ask:

"From these materials, name 3 essential features for AI-assistant task
management apps succeeding in market."

AI response:

1. Natural language input: "Tomorrow 2pm meeting, 60 minutes, high priority"—auto-creates task
2. AI automatic priority assignment: AI proposes priority with reasoning for ambiguous tasks
3. Prediction and recommendation: Suggest based on past data patterns

9am-9:30am: Complete detailed market research.

10am: UI/UX Design Phase

Based on research, UI design begins. Antigravity's turn.

Prototype Creation with Antigravity

Create main screen prototype. Antigravity instructions:

Design requirements:
- Main screen: Task list (separate complete/incomplete)
- Left sidebar: Category filter, priority filter
- Top bar: New task input field, search, user menu
- Each task: Title, description, priority (color-coded), deadline,
  AI recommendation display area
- Bottom: AI assistant launch button

Design language: Modern, minimal, dark mode support

Antigravity's AI-driven development auto-generates:

1. Layout auto-generation: Optimal component structure from requirements
2. Responsive design: Auto-adjust desktop, tablet, smartphone display
3. Interaction definition: Button clicks, form validation, loading states
4. Dark mode support: Auto-reflect light/dark toggle

10am-12pm: Complete basic UI framework (2 hours).

Antigravity Implementation Approach

Antigravity's "Agent-First" workflow follows:

1. Planning: Document what to build (markdown)
2. Proposal: AI presents design in Artifact
3. Review: Developer confirms/revises
4. Coding: AI generates component code
5. Integration: Add code to project

Key: "Design → Propose → Review → Code"—human-AI collaboration. This reflects developer intent accurately.

1pm: Backend API Design

After lunch, backend API basic design begins. Claude Code's spotlight.

Server-Side Implementation with Claude Code

Claude Code is Anthropic's CLI development environment. Access Claude directly from terminal, coordinate file operations, code generation, test execution.

$ claude-code
> design FastAPI task management API
 
Requirements:
- Task CRUD (Create, Read, Update, Delete)
- Task category, priority management
- AI priority suggestion endpoint
- User authentication (JWT)
- Task timestamp management

Claude Code AI auto-proposes:

1. Database schema (PostgreSQL)
2. API endpoints (RESTful design)
3. Error handling strategy
4. Authentication/authorization implementation
5. Boilerplate code (FastAPI basics)

Generated code validates immediately:

$ claude-code > test-api
> For proposed schema, query time with 1M task inserts?

Claude Code executes actual tests, provides performance predictions.

2pm-4pm: Frontend-Backend Integration Implementation

Mid-afternoon: Integrate previously designed elements, implement.

React Component Implementation with Antigravity

Antigravity converts designed UI to React:

// Antigravity auto-generates component structure
/src/components
  ├── TaskList.jsx
  ├── TaskCard.jsx
  ├── TaskForm.jsx
  ├── FilterSidebar.jsx
  ├── AIAssistant.jsx
  └── UserMenu.jsx

Each component auto-generates:

• TypeScript type safety: Complete props/state typing
• Accessibility: ARIA labels, keyboard navigation
• Responsiveness: Tailwind CSS auto-adjustment
• Tests included: Jest unit tests included

Backend Implementation with Claude Code

Simultaneously, Claude Code implements backend:

$ claude-code > create tasks/routes.py
# FastAPI task management endpoints
# - GET /tasks (retrieve list)
# - POST /tasks (create new)
# - PUT /tasks/{id} (update)
# - DELETE /tasks/{id} (delete)
# - POST /tasks/{id}/priority-suggest (priority suggestion)

Claude Code auto-generates API documentation:

$ claude-code > generate api-docs
# Auto-generates OpenAPI spec, accessible via Swagger UI

4pm: Bug Discovery and Fix Cycle

As implementation progresses, bugs emerge. Three-tool coordination excels here.

Example: Task Creation Error

Frontend (TaskForm from Antigravity) sends new task, receives error.

// Error message
{"error": "UNIQUE constraint failed: tasks.user_id, tasks.title"}

Consult Claude Code:

$ claude-code > debug
> Improve error handling when user creates task with identical title

Claude Code proposes:

1. Root cause: Users creating multiple same-title tasks is actually common—previous implementation was wrong
2. Solution: Remove UNIQUE constraint, allow same titles
3. Frontend logic: When user enters existing title, display existing tasks—"Create new anyway?"

Coordinate with Antigravity:

Add warning UI to TaskForm.
When user attempts duplicate title, show existing task list.
Confirm "Create new or select existing?"

Antigravity auto-generates warning UI, integrates validation. Bug fix cycle (discovery → analysis → backend fix → frontend fix) completes in 15 minutes.

5pm: AI Assistant Feature Implementation

Project's centerpiece "AI priority suggestion" feature begins.

Leverage Google AI's Gemini 3.1

This feature calls Google AI Pro's Gemini 3.1 Pro via API.

# Claude Code generates AI priority suggestion endpoint
 
@app.post("/tasks/{task_id}/priority-suggest")
async def suggest_priority(task_id: int):
    task = db.query(Task).filter(Task.id == task_id).first()
    user_tasks = db.query(Task).filter(Task.user_id == task.user_id).all()
 
    prompt = f"""
    User's task list:
    {format_tasks(user_tasks)}
 
    New task: {task.title}
    Description: {task.description}
    Deadline: {task.due_date}
 
    Based on past priority patterns, suggest priority (high/medium/low)
    and reasoning.
    """
 
    response = gemini.generate(prompt)
    return {"priority": response.priority, "reasoning": response.reasoning}

This endpoint called from Antigravity's "AI Assistant" button.

Antigravity UI Integration

Frontend displays AI suggestion:

// AIAssistant.jsx section
function AIAssistant({ taskId }) {
  const [suggestion, setSuggestion] = useState(null);
  const [loading, setLoading] = useState(false);
 
  const handleGetSuggestion = async () => {
    setLoading(true);
    const res = await fetch(`/api/tasks/${taskId}/priority-suggest`);
    const data = await res.json();
    setSuggestion(data);
    setLoading(false);
  };
 
  return (
    <div className="ai-assistant-panel">
      <button onClick={handleGetSuggestion} disabled={loading}>
        {loading ? "Getting suggestion..." : "Ask AI"}
      </button>
      {suggestion && (
        <div className="suggestion">
          <p>AI suggests: <strong>{suggestion.priority}</strong></p>
          <p className="reasoning">{suggestion.reasoning}</p>
          <button onClick={() => accept(suggestion.priority)}>
            Accept suggestion
          </button>
        </div>
      )}
    </div>
  );
}

Antigravity auto-delivers dark mode, accessibility, mobile support.

6pm: Testing and Quality Assurance

Final work phase: testing.

Auto-Generated Tests with Claude Code

$ claude-code > generate tests
# Auto-generate unit, integration tests for all API endpoints

Claude Code generates:

1. Unit tests: Individual function confirmation
2. Integration tests: Full API endpoint coordination
3. Edge case tests: Abnormal input, boundary values
4. Performance tests: Large data handling

Cross-Browser Testing with Antigravity

Verify UI displays correctly on multiple browsers (Chrome, Safari, Firefox)
and devices (desktop, tablet, smartphone)

Antigravity automates this verification.

7pm: Daily Achievement Review and Tomorrow Handoff

Evening: Summarize day's work.

Today's results:

✅ UI framework complete (Antigravity) ✅ Backend API basics (Claude Code) ✅ Frontend-backend integration confirmed ✅ AI assistant feature basics ✅ Test suite created

Remaining work:

• Refined AI priority algorithm (reflect Google AI market research trends)
• Complete authentication flow
• Database schema optimization
• Performance measurement and optimization

Tomorrow handoff:

Claude Code auto-commits all code to GitHub, auto-generates status documentation:

$ claude-code > commit-and-document
> Submit today's implementation to GitHub, update README
 
# Auto-generated:
# - Detailed commit message
# - IMPLEMENTATION_STATUS.md (status report)
# - API_DOCUMENTATION.md (specification)
# - KNOWN_ISSUES.md (known issues)

Three-Tool Integration Benefits Summary

Efficiency gains

Quality benefits

1. Consistency: All three tools share project structure—frontend/backend mismatch prevented
2. Early bug detection: Market research captures user needs accurately, reducing implementation design errors
3. Test coverage: Auto-generated tests catch edge cases manual testing misses
4. Maintainability: AI-generated code and docs are structured, understandable, maintainable

Team development extension

This three-tool approach scales to team development:

• Google AI research: Product managers
• Antigravity UI design: Designers and frontend engineers
• Claude Code backend: Backend engineers

Information automatically syncs between roles—no more "frontend finished, backend constraints require rebuilding."

Getting Started

For adopting three-tool integration:

Step 1: Tool Preparation (1 week)

• Google AI Pro subscription (¥2,900/month)
• Antigravity account setup
• Claude Code CLI environment

Step 2: Small Project Trial (2 weeks)

• Execute full workflow on simple CRUD app
• Verify inter-tool coordination
• Customize workflow to suit your needs

Step 3: Real Project Deployment (4+ weeks)

• Large-scale project launch
• Establish team best practices

Looking back

Google AI Pro × Antigravity × Claude Code three-tool integration isn't just "efficiency"—it's development model transformation.

Previously "one engineer does everything" was standard. Now "multiple AI tools specialize in different domains, humans orchestrate" is established.

Mastering this model expands individual-achievable project scale measurably.

Try it.