Loop/Prompt Library / Running AI agents well
๐ Manage a fleet of workers
Watches many computers or agents doing a big job, splits up the work, and moves it from slow or stuck workers to fast ones.
Use it when: Many approved workers are crunching through a large batch job.
๐ Stops when: No approved work remains, use cannot safely improve, or you hit the limit.
Copy this loop
Coordinate only these approved machines and job types: [list]. Check each worker and label it idle, active, stuck, failed, slow, or fast. Split remaining work into numbered chunks with no overlap. Give chunks to workers based on speed and health. Restart idle or failed workers and requeue their unfinished work. Remove duplicate results and send strong results to [verification step]. Re-check every [interval], for at most [N] cycles. Stop when work is done or you need approval.
Review every change the agent makes before you keep it. Set a budget or round limit โ the prompt includes one.
Idea from Forward Future's Loop Library ยท by Frosty40 (@frostforger). Prompt rewritten in plain English by FixKit AI.
More loops like this
Agent workspace health check
Reads a project's notes and state, without changing anything, and grades whether a new agent could safely pick up the work.
Stop and check before continuing
After an agent finishes a task, it reports what is done and whether the next step is safe, then waits for you.
Clean handoff note
Before a session ends, writes a note so the next person or agent can continue without guessing.
Predict, act, check
Before every meaningful action, the agent writes what it expects to happen, then checks if that is what really happened.
Lessons that stick
Keeps a playbook of lessons from repeated runs, tests one lesson at a time, and only keeps lessons that keep working.
Fair-test experiments
Improves a prompt or piece of code through saved, fairly compared experiments that can pick up again later.
