There is no single best agentic CLI. Here is how Claude Code, Codex, Cursor, Gemini, Copilot, and opencode hold up over a long autonomous loop, and how to pick per task.
A Ralph loop fails in a handful of predictable ways. Here is each failure mode, why it happens, and the specific guardrail that stops it.
A PRD for an AI coding agent needs goals, constraints, and verifiable acceptance criteria, not a feature wishlist. Here is what goes in PRD.md and SUMMARY.md and how to draft it.
An autonomous agent only stays safe when every task ends in a machine-checkable result. Tests, type checks, lint, and screenshots are the feedback loop that lets the agent grade its own work.
Point Ralph at Google’s Gemini CLI with one flag, pick a model after the separator, log in once inside the sandbox, and let it grind through your task list with a fresh context each iteration.
A Ralph loop stops on an explicit signal, not a guess. How promise tags and exit codes tell the loop and your scripts when the agent is COMPLETE, BLOCKED, or needs a decision.
An autonomous agent is only as trustworthy as what it shows you. Here are the live stream, per-iteration history, progress log, screenshots, and timing metrics that make a Ralph loop auditable.
Point Ralph at the headless cursor-agent with one flag, log in once inside the sandbox, pass a model after the separator, and review a finished diff in the morning.
PROMPT.md is the instruction sent to the agent on every iteration. Here is how to structure it so a fresh-context agent reorients from disk and ships one verified task at a time.
An autonomous loop burns money when it thrashes. Cap iterations, match the model to the task, and gate every pass on tests so the loop stops instead of looping forever.
