Gajae-code brings workflow discipline to AI agents

AI coding agents are notorious for jumping straight into code modifications and creating messy branch states, making external constraints like Gajae-Code a necessary guardrail for production environments.

• –**Workflow discipline over raw intelligence:** By forcing agents to go through a requirements clarification interview (`deep-interview`) and multiple planning review stages (`ralplan`), it reduces hallucination rates and poorly conceived implementations before file changes occur.
• –**Clean execution environments:** Leveraging isolated Git worktrees and tmux sessions ensures that agent processes do not pollute active workspaces and can run complex tasks concurrently without manual oversight.
• –**Interoperability as a key advantage:** Because it acts as an external harness with an MCP/RPC control plane rather than a vendor-locked IDE extension, it can orchestrate diverse, multi-agent frameworks seamlessly.
• –**Early-stage limitations:** Currently in beta and experimental status, meaning developers must still carefully verify the output verification artifacts before deployment.