How to Use Antigravity Agent Manager

The Problem: Single-Threaded AI

Most developers use AI like a junior intern: one task at a time. "Write this function." "Explain this error." This blocks your flow.

Antigravity Agent Manager unlocks parallel processing. It allows you to spawn multiple, specialized agents that run in the background.

Specialized Roles

The Researcher: Give it a URL (e.g., Stripe API docs). It browses the web, reads the pages, and saves a summary to `docs/stripe-notes.md`.
The Architect: Reads your entire codebase and maintains a `SYSTEM_DESIGN.md` file, ensuring new features match existing patterns.
The QA Engineer: Watches your file changes. When you save, it silently runs unit tests. If they fail, it drafts a fix in a background tab.

How to use it

Open the Command Palette and type "Spawn Agent". Select a template (e.g., "Documentation Bot"). Assign it a goal: "Read all files in /src/api and generate Swagger docs".

You can see the agent's progress in the "Agent Manager" panel on the right. It shows a log of every file it read and every thought it had.

The "Human in the Loop"

Agents in Antigravity are autonomous but obedient. They will never commit code without your approval. The Manager View shows a "Pending Changes" list for each agent, allowing you to review/reject their work in bulk.

Deep Dive: Key Takeaways

This article explores critical aspects of Antigravity IDE and its impact on modern development workflows. Understanding these concepts is essential for developers looking to maximize their productivity with AI-powered development tools. The insights presented here are based on extensive testing and real-world application experience.

Practical Applications

The techniques and features discussed in this article have immediate practical applications for your development process. Whether you're working on small personal projects or large enterprise applications, these insights can help you leverage Antigravity IDE's capabilities more effectively. Consider how these concepts apply to your specific use cases and development environment.

Common Challenges and Solutions

Many developers encounter similar challenges when adopting AI-powered development tools. This article addresses common pain points and provides tested solutions that have worked for thousands of developers. The solutions presented are not just theoretical—they've been validated through extensive community feedback and real-world usage.

Performance Considerations

Performance is a critical factor when working with AI development tools. The approaches outlined in this article have been optimized for both speed and resource efficiency. Understanding these performance characteristics helps you make informed decisions about when and how to use different features of Antigravity IDE.

Further Learning Resources

To deepen your understanding of the topics covered in this article, we recommend exploring these additional resources. Each resource has been carefully selected to complement the information presented here and provide different perspectives on AI-assisted development.

📚 Documentation

Official Antigravity IDE documentation provides comprehensive guides and API references for advanced features.

🎥 Video Tutorials

Community-created video tutorials demonstrate practical implementations and workflow optimizations.

💬 Community Forums

Join discussions with other developers to share experiences and discover new techniques.

Implementation Best Practices

Getting Started

Implementing the concepts from this article requires a systematic approach. Start with small, controlled experiments in non-critical projects before applying these techniques to production code. This allows you to understand the nuances and adjust your workflow accordingly.

Integration Strategies

Successful integration of Antigravity IDE into your existing workflow depends on gradual adoption. Rather than attempting to revolutionize your entire development process overnight, focus on integrating one feature at a time. This measured approach ensures team buy-in and minimizes disruption to ongoing projects.

Monitoring and Optimization

Regular monitoring of your AI-assisted development metrics helps identify areas for improvement. Track metrics like code generation accuracy, time saved, and bug reduction rates. Use these insights to continuously refine your approach and maximize the benefits of AI-powered development.