Episode 10: Practical Q&A (Part 2) - Advanced Usage and Security

⏱ Est. reading time: 3 min Updated on 5/7/2026

For advanced users, here are some deeper discussions.

Q5: My company doesn't allow code to leave the premises. Can I use these tools?

  • Gemini CLI: Because it's open-source, you can theoretically modify the source code to connect it to your company's private LLM nodes or use it through Vertex AI's private compliance regions.
  • Claude Code & Antigravity: These default to public cloud APIs. For highly sensitive projects, it's recommended to access them through enterprise-grade compliance channels like Amazon Bedrock or Google Vertex AI and enable no-logging modes.

Q6: I want to use the Claude model but prefer the Gemini CLI experience. Is that possible?

Not officially supported. Gemini CLI is focused on Google's Gemini ecosystem. If you truly need "multi-model mixing," Antigravity is the only native solution, allowing you to switch seamlessly within the same interface.

Q7: Antigravity doesn't have Hooks. How do I implement hard constraints?

Workaround: Official advice is to use strong guidance via Markdown rules in .agents/rules/ (e.g., "Unit tests must be run before modifying code"). While this is a "soft constraint" (the AI might forget), the user can manually oversee the process using Antigravity's Verification Mode.

Q8: How can I deeply customize my own Coding Agent?

You have two paths:

  1. Lightweight: Achieve this by defining sub-agents, Skills, and Rules on top of existing tools. This solves 80% of needs.
  2. Heavyweight: If you need to control the entire execution cycle and reflection logic of the Agent, it's recommended to build it from scratch using the Claude Agent SDK or Google ADK.

💡 Conclusion

By 2026, AI Coding Agents are no longer just "autocomplete" tools—they are true digital employees. Choosing the right tool for yourself and learning to write high-quality rule files (AGENTS.md) will be a core competency for future developers.