Interview guide for Claude Code — CLI coding agents, AGENTS.md workflows, sandboxes, and how Claude Code compares to Cursor and Copilot.
Claude Code has become one of the highest-demand AI coding agents in developer hiring. Interviewers no longer ask only about Claude the model — they ask how you would run Claude Code safely in a monorepo, how it differs from IDE copilots, and how you measure productivity without confusing merge rate with quality.
Microsoft's enterprise research on CLI coding agents found developers merged roughly 24% more pull requests during agent rollouts. That statistic shows up in interviews — but strong candidates pair it with review quality, revert rate, and security controls.
This guide covers the Claude Code questions appearing in Anthropic-ecosystem, platform engineering, and AI tooling loops: agent vs copilot framing, production workflows with AGENTS.md, permissions, CI verification, and human approval gates. Work the questions in order — fundamentals first, then system design.
Deep explanations with architecture diagrams for every question below.
Claude Code is Anthropic's CLI coding agent designed for repository-level work: reading the codebase, editing multiple files, running tests/builds, and iterating until a task succeeds. Unlike autocomplete-first copilots that live inside the editor, Claude Code operates as an agent loop — observe → plan → act (edit/run tools) → verify.
Read full explanationA production Claude Code rollout is an agent platform problem, not a 'give everyone a CLI' problem.
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