| name | autopilot |
| description | Continuous-operation mode for rockie — runs the experiment queue autonomously, using Zero-Cost Monitoring ($0 LLM cost during training), anti-burn exponential cooldown on failures, and ntfy to wake the human only when a decision is needed. Use when you want agent-driven research to proceed for days without human input. Not appropriate for unproven projects — only enable after you have a populated queue, budget ceilings, and a working launcher. |
/autopilot — days-long autonomous operation
This is the "just keep going while I sleep" mode. It composes the
pieces you already have (queue, ZCM, journal, calibration, budget,
dead-ends, [LEARN]) into a single daemon that can drive an 8×H100
project for a week without human input.
Pre-flight checklist — DO NOT skip
Before starting autopilot, you MUST:
The daemon
nohup bash .claude/scripts/autopilot_loop.sh > .claude/memory/autopilot.log 2>&1 &
Stop:
bash .claude/scripts/autopilot_loop.sh --stop
What the loop does
loop forever:
item = queue.py next --json # atomic claim, $0 LLM cost
if item is None:
if queue_under_target: ntfy(tier=2, "queue empty")
sleep 300
continue
pid = LAUNCHER_CMD < item # launcher backgrounds the job
zcm.sh --pid $pid --log $log # poll, $0 LLM cost, wake on anomaly
match zcm_exit:
0 (clean): leave claimed — agent will close via post-run-review
1 (crash): kill process, increment failures
2 (anomaly): kill process, increment failures
3 (timeout): kill process, increment failures
if consecutive_failures >= max:
ntfy(tier=1, "cooldown, $seconds sleep")
sleep $seconds # anti-burn
seconds = min(seconds*2, max)
Cost model (measured pattern from arXiv 2604.05854)
Conventional polling (LLM call per tick): ~$1.60/24h.
rockie ZCM polling (LLM only on event): ~$0.08/24h.
What autopilot does NOT do
- Does NOT decide which experiment to run next semantically. It
just pops priority order from the queue. If the queue is full of bad
ideas, autopilot will dutifully run bad ideas. Quality of the queue
is your responsibility (or the
/queue-refill skill's).
- Does NOT interpret results. When ZCM returns, the journal node is
still in
running state. The agent (woken by ntfy on anomaly, or
explicitly run after a clean completion) invokes
/post-run-review to close it.
- Does NOT self-modify. No autonomous
[LEARN] authorship, no
CLAUDE.md rewriting, no rule changes. Those remain user/agent turns.
(See docs/PORTS.md → GVU operator separation for the safe
self-improvement roadmap item.)
Failure modes and their backstops
| Failure | Backstop |
|---|
| Queue drains, no refill happens | ntfy tier-2 when queue goes empty + idle 5 min |
| Launcher crashes repeatedly | anti-burn cooldown (exponential), eventually ntfy tier-1 |
| Training never dies (hang) | ZCM --max-seconds terminates with exit 3 |
| Training dies with silent exit 0 but bad output | post-run-review catches (see journal failure_class) |
| Out of budget mid-run | budget-gate.sh blocks next tool call; autopilot notices launcher refuses next item |
| Ntfy server outage | notify.sh records NULL ntfy_id in notifications table; autopilot keeps running; humans lose realtime signal until back online |
| Clock drift / timestamp confusion | all hooks use datetime('now') SQLite-local; no dependence on wallclock math across machines |
When to disable autopilot
- You're actively developing the launcher.
- You're exploring a new research direction (low queue signal).
- A fundamental harness bug is suspected (manual mode is faster to diagnose).
- You're on a budget near its ceiling and want to stop the line.
