If you’re like me, you’ve toggled between AI coding assistants trying to find the "best" one. Gemini generates features fast, while OpenCode’s models are good for catching edge cases. But why choose? I built a custom workflow using Gemini CLI to orchestrate three specialized agents that bridge these two worlds. Here’s how I get the best of both: Gemini's speed for implementation and OpenCode's rigor for review. The Three-Agent Setup My .agents directory contains three distinct roles. The magic of Gemini CLI is its ability to not only write code but also manage other CLIs and agents: code-writer (Gemini-powered): The primary builder. It handles the heavy lifting of implementation and iterates on feedback. opencode-code-reviewer (Gemini-powered): The "Bridge Agent." This Gemini agent knows how to run the opencode CLI, capture its feedback, and hand it back to the writer.…