The Ultimate AI Coding Workflow: Antigravity, Claude Code, and Token-Saving Strategies

If you’ve been experimenting with AI-assisted development tools like Google’s Antigravity and Anthropic’s Claude Code, you’ve likely run into the challenge of rate limits and token usage. Conserving those valuable credits, especially when tackling complex features like application authentication, is essential for an efficient workflow.

The Wanderloots tutorial showcases a powerful hybrid three-agent workflow that is designed to maximize coding power while minimizing token burn, combining the strengths of Google Gemini, Claude Code, and an autonomous testing agent.

Here is a breakdown of how to build robust features, like a sign-in system, using this optimized strategy.

1. The Strategy: Planning, Coding, Testing

The core principle of this workflow is to use each AI for its best function, offloading high-usage tasks to specialized tools:

• Planning: Use Google Gemini 3 Pro (via Antigravity) for research, context organization, and creating detailed implementation roadmaps [[03:07]].
• Coding: Use Claude Code (with access to powerful models like Opus 4.5 Thinking) for the actual code execution and building the features [[03:24]].
• Testing & Debugging: Use a separate, autonomous testing service like Test Sprite (connected via the Model Context Protocol, or MCP) to catch bugs and validate features without wasting valuable coding agent tokens [[00:06]], [[00:45]], [[20:35]].

2. Phase 1: Planning and Context with Gemini

Since Antigravity is built on a VS Code fork, it easily accommodates external extensions like Claude Code, allowing developers to extend their usage limits [[01:31]], [[01:54]].

The process begins in Antigravity’s agent manager:

• Research & Stack Selection: Direct Gemini 3 Pro to perform state-of-the-art research for the best authentication stack (e.g., Better O + Neon or Superbase) [[11:02]].
• Generate the Roadmap: Crucially, instead of having Gemini execute the code, you instruct it to create a detailed roadmap document containing all the necessary research, architecture, and planning notes [[13:04]]. This step conserves Gemini’s execution tokens by leveraging its large context window for free-form planning.

3. Phase 2: Execution and Building with Claude Code

The Gemini-generated roadmap is then passed to the Claude Code extension, ensuring the coding agent is immediately working from a fully researched context.

• Install and Configure: Install the Claude Code extension and connect it to your Anthropic account [[04:54]]. You can then use the /mcp command to manage Model Context Protocol (MCP) servers [[02:08]].
• Code Implementation: Claude Code is directed to audit the roadmap, create an MVP plan (e.g., using a local SQLite database for the backend), and then auto-accept the edits to build the features, such as the O client and sign-up form [[14:09]], [[16:34]], [[18:11]].

4. Phase 3: Autonomous Testing with Test Sprite (MCP)

Debugging is one of the quickest ways to deplete your coding agent tokens [[19:07]]. The final and most token-efficient step is to offload testing to a specialized MCP server:

• MCP Integration: Install Test Sprite and connect it to Claude Code via the MCP terminal interface [[22:01]]. This grants Claude Code access to Test Sprite’s suite of tools.
• Autonomous Test Runs: Instruct Claude to use Test Sprite to run the authentication tests [[23:05]]. Test Sprite autonomously:
  • Bootstraps the testing environment (e.g., localhost:3000).
  • Generates 17+ tests (sign-up/sign-in with valid/invalid data, sign-out) based on your product specification doc [[25:07]].
  • Runs the tests using a browser tester like Playwright and tracks the progress [[26:56]].
• Targeted Debugging: After the run, Test Sprite produces a human-readable report with detailed errors and visualizations [[26:29]]. This specific report is then fed back to Claude Code, allowing the agent to pinpoint the issue and implement a fix quickly (e.g., fixing a connection error between Better O and SQLite) [[27:44]], using minimal tokens compared to blind debugging [[28:19]].

By structuring your work this way, you ensure that the most powerful, token-costly LLMs (Gemini/Claude) are reserved for high-value tasks (planning and coding), while the repetitive and iterative work (testing and initial debugging) is handled by an efficient, credit-conserving agent. The result is a fully functioning authentication system built with maximum efficiency [[28:52]], [[31:03]].

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