Antigravity: My Approach to Deliver the Most Assured Value for the Least Money

As I'm not a professional developer but a guy who needs to use automation to get things done, I follow one main rule: keep it simple. Overengineering hurts. I use the Pareto rule—spend 20% of the effort to get 80% of the result. When I use AI agents like Antigravity, my goal is not to let the AI write complex code that no one can read. My goal is to build simple, secure features fast. At the same time, I control costs by saving tokens. Here is the exact workflow I use. The Token Economy Strategy LLM tokens cost money. Using a smart, expensive model just to fix code spaces is not worth the cost. I change models based on how hard the task is. High-Tier Models: They are for the big tasks: planning architecture, writing complex business logic, checking security, and counting cloud costs. Low-Tier Models: These folks are for simple tasks: fixing syntax errors, aligning code to Pylint, and writing standard code pieces. Task Decomposition & In-Repo Architecture Large prompts can break LLM