Antigravity Agent-First Workflow Practical Guide — From Project Management to Skill Design

What is Agent-First Development?

"Agent-First development" in Antigravity represents a paradigm shift from the traditional approach of "writing the same prompt repeatedly" to a new model of "cultivating and managing autonomous AI agents."

Difference from Traditional AI Development

The traditional approach involves writing the same prompts repeatedly, losing context and making reuse difficult across projects. Agent-First development creates organized AI agents that retain memory, learn over time, and can be reused across multiple projects and teams.

Example: Instead of manually prompting "write a React component" each time, you configure a "React Agent" once and then issue instructions subsequently.

Antigravity's Recommended Project Structure

Antigravity recommends clear project separation: "1 project = 1 folder."

Recommended Folder Layout

my-projects/
├── project-alpha/          # Project A
│   ├── agents/              # Agents for this project
│   │   ├── frontend-agent.md
│   │   ├── backend-agent.md
│   │   └── qa-agent.md
│   ├── skills/              # Reusable skills
│   │   ├── api-integration.md
│   │   └── database-query.md
│   ├── artifacts/           # Generated code & docs
│   │   ├── components/
│   │   ├── services/
│   │   └── tests/
│   ├── docs/                # Project documentation
│   │   ├── requirements.md
│   │   ├── architecture.md
│   │   └── api-spec.md
│   └── context.md           # Project-wide context

├── project-beta/           # Project B
│   └── [same structure]

└── shared-skills/          # Shared skills across projects
    ├── code-review.md
    ├── testing.md
    ├── documentation.md
    └── deployment.md

The Role of context.md

Each project's context.md serves as the project's "memory," storing all essential information about the project, including technical stack, architectural decisions, constraints, completed features, and team member roles.

This ensures that the agent (AI) consistently understands the project context throughout development.

Building a Skills Library — Reusable AI Behaviors

One of Antigravity's powerful features is the "Skills Library," which contains reusable AI behavior definitions for specific tasks.

Skill Design Principle: "One Skill = One Job"

A skill should be focused and self-contained. For example, a "React-Form-Generation" skill:

• Purpose: Auto-generate React form components
• Input: Form field definitions (JSON)
• Processing Steps: Parse fields, generate components using React Hook Form, add validation, style with Tailwind, ensure TypeScript safety
• Output: FormComponent.tsx, FormComponent.test.tsx, types.ts
• Configuration Parameters: variant, darkMode, submitButtonText
• Limitations: Complex conditional display and file uploads unsupported

Once defined, the skill enables straightforward usage without rewriting instructions each time.

Example Skills Library

A production project might include:

• React-Component-Gen: Generate React components from requirements
• API-Endpoint-Gen: Generate FastAPI endpoints
• Database-Migration-Gen: Generate DB migrations
• Test-Suite-Gen: Generate test code
• Documentation-Gen: Generate API docs

The Artifact System — Planning, Proposal, Implementation Cycle

Antigravity's Artifact system clearly separates drafts from final implementations.

Typical Workflow

Step 1: Planning (Markdown) - Document feature requirements and design approach.

Step 2: Proposal (Artifact) - Antigravity generates an artifact proposal based on the plan.

Step 3: Review - Developer examines and provides feedback.

Step 4: Refinement - Antigravity automatically updates the artifact.

Step 5: Confirmation - When satisfied, save to the artifacts folder.

This iterative process ensures accuracy, enables team sharing via artifact URLs, and maintains complete version history.

Agent-Assisted vs Autonomous Modes

Antigravity provides two agent operation modes.

Agent-Assisted Mode (Recommended: Implementation Stage)

The AI waits for human instructions at each step.

Flow: 1. Developer issues instruction → 2. Antigravity proposes artifact → 3. Developer reviews and provides feedback → 4. Antigravity refines → 5. Developer confirms

Benefits: Ensures developer intent is accurately reflected, high quality, unified team understanding.

Drawbacks: Time-consuming.

Autonomous Mode (Use Limited: Simple Tasks)

The AI automatically judges and executes tasks.

Flow: 1. Developer issues instruction → 2. Autonomous agent automatically analyzes, designs, implements, and commits → 3. Developer reviews results

Benefits: Fast, optimal for simple mechanical tasks.

Drawbacks: Developer may lose control, unsuitable for complex decisions.

Usage Guidelines

A Day's Practical Workflow

Morning: Daily Task Planning

1. Review project context.md
2. Create today's task list in Markdown

# Today's Tasks (2026-03-20)
 
## Completion Goals
- [ ] Complete task priority display component
- [ ] Implement API endpoint
- [ ] Add unit tests
- [ ] Update documentation
 
## Implementation Order
1. Agent-Assisted: UI component design
2. Agent-Assisted: Backend API implementation
3. Autonomous: Test code generation
4. Autonomous: Documentation generation

Morning: Frontend Implementation

Developer requests component creation, Antigravity proposes artifact in Agent-Assisted mode, developer reviews and provides feedback for improvement, Antigravity updates, developer confirms and artifact is saved.

Afternoon: Backend Implementation

Developer requests FastAPI endpoint implementation with specific requirements, Antigravity proposes implementation in Agent-Assisted mode, developer confirms after review.

Evening: Testing & Documentation

Developer specifies Autonomous mode for automatic test and documentation generation, Antigravity automatically generates tests and documentation.

Best Practices

1. Regular Context Updates

Update project context.md weekly to maintain accurate project understanding and agent alignment.

2. Gradual Skill Building

Start with few skills at project beginning, gradually add general-purpose skills as implementation progresses.

3. Artifact Usage Rules

• New implementation: Always propose via artifact → review → confirm
• Bug fixes: Propose fix via artifact → test → confirm
• Documentation: Autonomous generation OK

4. Agent Training Investment

Spend the first 3 days ensuring the agent understands the project rather than just generating code. This significantly improves subsequent efficiency.

Troubleshooting

Agent Misunderstands Requirements

Update context.md with background reasoning for architectural choices.

Artifact Too Complex

Break skills into smaller, more focused components.

Inconsistent Instructions Among Team Members

Share context.md and Skills Library. Prioritize documentation.

Looking back: Transitioning to Agent-First Development

Agent-First workflow may initially seem complex. However, once mastered:

1. Agents (AI) learn and develop through experience
2. Team-wide consistency in approach
3. Dramatically improved scalability
4. Faster new member onboarding

These benefits significantly enhance both individual productivity and team output.

Give it a try!