Understanding Artifacts in Google Antigravity: Transparency in AI Coding

One of Antigravity's most innovative features is Artifacts—a system that makes AI decision-making transparent.

This guide explains what artifacts are and how to use them.

What Are Artifacts?

Artifacts are visual representations of AI's thought process. They show:
* What the AI considered
* Why it made certain decisions
* Alternative approaches it rejected
* Confidence levels

Think of it as “showing your work” in math class—but for AI.

Why Artifacts Matter

The Black Box Problem

Traditional AI coding tools are black boxes:
* AI generates code
* You don't know why
* You can't verify the reasoning

The Artifact Solution

With artifacts:
* AI generates code
* Shows its reasoning
* You can verify and learn

Types of Artifacts

1. Decision Trees

Shows the decision-making process.

Example:
“`
Task: “Implement user authentication”

Decision Tree:
├─ Authentication Method?
│ ├─ JWT (chosen) ✓
│ │ Reason: Stateless, scalable
│ ├─ Sessions (rejected)
│ │ Reason: Requires server-side storage
│ └─ OAuth (rejected)
│ Reason: Overkill for this use case
├─ Password Hashing?
│ ├─ bcrypt (chosen) ✓
│ │ Reason: Industry standard, secure
│ └─ SHA-256 (rejected)
│ Reason: Not designed for passwords
“`

2. Code Alternatives

Shows different implementation options.

Example:
“`
Function: calculateDiscount

Option A (chosen):
function calculateDiscount(price, percent) {
return price * (1 – percent / 100);
}
Pros: Simple, readable
Cons: No validation

Option B (rejected):
function calculateDiscount(price, percent) {
if (price < 0 || percent < 0 || percent > 100) {
throw new Error(‘Invalid input');
}
return price * (1 – percent / 100);
}
Pros: Robust, handles edge cases
Cons: More complex

Recommendation: Use Option B for production
“`

3. Architecture Diagrams

Visual representation of system design.

Example:
“`mermaid
graph TD
A[User] –> B[Frontend]
B –> C[API Gateway]
C –> D[Auth Service]
C –> E[User Service]
D –> F[Database]
E –> F
“`

4. Test Coverage Maps

Shows what's tested and what's not.

Example:
“`
File: auth.js
├─ login() ✓ Tested
├─ logout() ✓ Tested
├─ register() ✓ Tested
└─ resetPassword() ✗ Not tested

Coverage: 75%
Recommendation: Add tests for resetPassword()
“`

How to Use Artifacts

Viewing Artifacts

1. After AI generates code, click “View Artifact”
2. Artifact panel opens on the right
3. Explore the decision tree, alternatives, etc.

Interacting with Artifacts

Ask Questions:
> “Why did you choose JWT over sessions?”

Request Changes:
> “Use Option B instead of Option A”

Explore Alternatives:
> “Show me how this would look with sessions instead of JWT”

Real-World Example

Scenario: Building an API

Prompt:
> “Create a REST API for a blog with posts and comments”

Artifact Generated:

“`
Architecture Decision:
├─ API Style?
│ ├─ REST (chosen) ✓
│ │ Reason: Simple, widely understood
│ ├─ GraphQL (rejected)
│ │ Reason: Overkill for simple CRUD
│ └─ gRPC (rejected)
│ Reason: Not needed for web clients
├─ Database?
│ ├─ PostgreSQL (chosen) ✓
│ │ Reason: Relational data, ACID compliance
│ ├─ MongoDB (rejected)
│ │ Reason: Relational structure fits better
│ └─ SQLite (rejected)
│ Reason: Not suitable for production
├─ Authentication?
│ ├─ JWT (chosen) ✓
│ │ Reason: Stateless, scalable
│ └─ Sessions (rejected)
│ Reason: Requires server-side storage

Endpoints:
GET /api/posts
POST /api/posts
GET /api/posts/:id
PUT /api/posts/:id
DELETE /api/posts/:id
GET /api/posts/:id/comments
POST /api/posts/:id/comments

Database Schema:
posts
├─ id (UUID, primary key)
├─ title (VARCHAR)
├─ content (TEXT)
├─ author_id (UUID, foreign key)
└─ created_at (TIMESTAMP)

comments
├─ id (UUID, primary key)
├─ post_id (UUID, foreign key)
├─ content (TEXT)
├─ author_id (UUID, foreign key)
└─ created_at (TIMESTAMP)
“`

Benefits

1. Transparency: You see why AI made each decision
2. Learning: You understand the trade-offs
3. Verification: You can check if decisions are sound
4. Modification: You can request changes based on reasoning

Advanced: Custom Artifacts

Creating Custom Artifact Templates

You can define what artifacts to generate:

“`yaml
artifact_template:
– decision_tree: true
– code_alternatives: true
– architecture_diagram: true
– test_coverage: true
– performance_analysis: true
– security_checklist: true
“`

Sharing Artifacts

Export artifacts to share with team:
1. Click “Export Artifact”
2. Choose format (PDF, Markdown, JSON)
3. Share with team

Comparison with Other Tools

| Feature | Antigravity | Cursor | Windsurf |
|———|————-|——–|———-|
| Artifacts | ✅ Yes | ❌ No | ❌ No |
| Decision Transparency | ✅ High | ❌ Low | ❌ Low |
| Alternative Options | ✅ Shows | ❌ Hidden | ❌ Hidden |

Antigravity's Advantage: You can verify AI reasoning.

Best Practices

1. Always Review Artifacts

Don't just accept AI code. Review the artifact to understand why.

2. Question Decisions

If a decision seems wrong, ask:
> “Why did you reject Option B? It seems better for this use case.”

3. Learn from Artifacts

Use artifacts as learning tools. Understand the trade-offs.

4. Share with Team

Export artifacts for code reviews. It helps reviewers understand the AI's reasoning.

Conclusion

Artifacts make AI coding transparent. You're not blindly trusting the AI—you're verifying its reasoning.

This is crucial for production code where mistakes are costly.

At BYS Marketing, we require all AI-generated code to have artifacts. It's part of our quality assurance process.

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Want to implement transparent AI development?
Contact BYS Marketing. We'll help you set up processes for safe AI coding.