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.
Wire OpenAI’s Codex CLI into a Ralph loop with one flag, pass model and approval flags after the separator, and let it grind through your task list inside a sandbox.
One-shot prompting solves small changes in a single response and stalls on hard ones. A Ralph loop iterates with fresh context and tests until the work is actually done.
Long-running agents lose the plot when one context window fills up. Start each iteration fresh and keep durable state on disk so progress never lives in chat history.
