A former Meta L8 engineer shares his end-to-end agentic workflow, from terminal setup to prompt strategies. He outlines the tools (WezTerm, tmux, Neovim, Claude Code/OpenCode), voice input, and delegation techniques that let him manage AI agents like a dev team.

This article traces the evolution of AI loops—small programs that run, check, and re-prompt coding agents—from early ReAct and AutoGPT examples to today’s durable, multi-agent orchestration with scheduling and self-verification. It shows why loop management, not model calls, is now the biggest cost in AI coding and outlines best practices: cap iterations, build reusable skills, and include feedback checkpoints.