OpenAI Codex CLI interview guide — when to use a CLI coding agent vs Copilot, plus cost optimization for high-volume agent jobs.
Codex CLI represents OpenAI's push into terminal-native coding agents — the same category as Claude Code. Interviews test whether you understand CLI agents as job runners with tools, sandboxes, and budgets, not just 'ChatGPT that edits files.'
You should be ready to compare Codex CLI with GitHub Copilot (IDE flow vs autonomous batches) and to discuss cost controls: context budgets, iteration caps, model routing, and cost per successful PR.
Use this guide for OpenAI-stack and platform-engineering interviews where coding agents are part of the SDLC.
Deep explanations with architecture diagrams for every question below.
Codex CLI represents OpenAI's push into agentic coding outside the editor — similar category to Claude Code and other CLI agents. The core loop is: understand task → explore repo → edit → run commands → iterate.
Read full explanationCost optimization for coding agents is context engineering + runtime policy.
Read full explanation