7 tips for using Antigravity 2.0 on enterprise codebases, coding phase

Key Concepts

• Agentic Workflow: Shifting from "prompting" (writing code) to "coaching" (managing an AI agent to perform tasks).
• Antigravity 2.0: An AI-powered development tool capable of autonomous file editing, loop execution, and tool usage.
• Custom Skills: Markdown-based instructions that define team standards, design patterns, and development practices.
• Self-Correction Loop: Enabling the agent to run tests, analyze errors, and refactor code autonomously.
• Async Task Execution: Running time-intensive processes (like integration tests) in the background to maintain developer momentum.
• Mermaid Diagrams: Text-to-diagram syntax used for visualizing architecture and documenting code changes.

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1. Encoding Team DNA into Custom Skills

Instead of repeatedly instructing the AI on coding standards, developers should create "Custom Skills." These are simple Markdown files that act as a playbook for the agent.

• Application: Define specific requirements like Test-Driven Development (TDD) protocols, preferred design patterns, or linting standards.
• Benefit: Ensures consistency across the codebase without manual intervention for every task.

2. Establishing a Paper Trail

Before executing large updates, use the agent to generate documentation.

• Process: Ask the agent to create implementation plans, architecture specs, and test plans.
• Strategy: Save these files directly into the working branch. This provides context for the agent to "read" and improves the quality of the final Pull Request (PR) for human reviewers. Delete these files once the PR is merged.

3. The Self-Correction Loop

The goal is to remove the human from the immediate feedback loop to increase efficiency.

• Methodology: Define project rules (e.g., linter commands, type checkers, unit/integration tests) in a README or project configuration.
• Execution: Once the agent is given a task, it runs the tests, reads the stack traces, and iterates on the code until all tests pass, requiring no human input during the process.

4. Giving the Agent "Eyes"

For front-end development, text logs are insufficient for verifying UI integrity.

• Tooling: Utilize the built-in browser tool (or force it via /browser in the prompt) to allow the agent to interact with the app.
• Visual Feedback: If the environment is locked down, provide screenshots to the agent. The agent can then identify layout bugs and fix them before the developer even opens the application locally.

5. Async Task Management

Enterprise codebases often contain slow, heavy test suites that can block development.

• Strategy: Offload long-running integration or end-to-end (E2E) tests to background tasks.
• Benefit: This keeps the workspace responsive, allowing the developer to continue writing code or planning the next task while the agent handles the heavy lifting.

6. Visualizing Architecture with Mermaid

Use the agent to generate Mermaid diagrams to maintain architectural integrity.

• Use Case 1: Identifying circular dependencies or incorrect layer calls by visualizing module interactions.
• Use Case 2: Generating visual documentation for new features to include in PR descriptions, helping human reviewers understand complex changes quickly.

7. Pre-Review and Refactoring

AI makes writing code easy, but it can make reviewing harder. Use the agent to "pre-review" your own work.

• Framework: Create reusable skills that check for security vulnerabilities, idiomatic code usage, and library compliance.
• Refactoring: Spend roughly 50% of development time using the agent to refactor code based on these automated reports. This ensures that the code submitted to human reviewers is clean, compliant, and high-quality.

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Synthesis and Conclusion

The transition from "vibe coding" to enterprise-grade development requires a shift in mindset: the developer must act as a coach rather than a typist. By leveraging Antigravity 2.0’s agentic capabilities—specifically through custom skills, automated self-correction, and background task management—developers can maintain high production standards while significantly increasing their velocity. The ultimate takeaway is that AI should be used to handle the "heavy lifting" of testing and refactoring, allowing the human developer to focus on high-level architecture and ensuring the final output is ready for human review.