## TL;DR
Cloud APIs throttle per account, per service and per region, and a restore sweep competes with production automation for the same quota. Map which quotas the sweep consumes, add service-aware backoff, spread the sweep across a longer window or a dedicated account where the cloud allows it, and coordinate with other heavy consumers so the sweep stops being the quota tragedy of the commons.

```text
agent hit the cloud API rate limit mid-restore-sweep and the sweep failed
```

## Use this when
- Restore sweeps fail with throttling errors from compute or storage control APIs.
- Production automation also reports throttling during sweep windows.
- Throttling eases when the sweep is paused.

## Not for this skill when
- Bucket listing alone is throttled. Listing-specific pacing may suffice.
- Requests fail with permission errors misread as throttling. Check error codes carefully.

## Steps
1. Collect throttling errors by service, operation and time from sweep logs. Verify: the consumed quotas are mapped
2. Check the cloud quota console for those services during sweep windows and note other consumers. Verify: quota contention is evidenced
3. Implement per-service backoff queues so one hot service does not stall the whole sweep. Verify: a throttled service pauses while others proceed in tests
4. Reschedule or stretch the sweep to spread calls, and where supported run it under its own project or account quota. Verify: the new schedule and quota home are in place
5. Run a full sweep in the new pattern. Verify: it completes without throttling failures or starving production automation

## Variant phrasings
### cloud api throttling sweep quota contention
The contention phrasing.
### restore sweep rate limit per service backoff
The backoff variant.
### agent sweep scheduled around quota limits
The scheduling wording.

Compatibility: Agents orchestrating restores across AWS, Azure and GCP APIs with per-service quotas and shared account limits.

## Why it happens
Quota is a shared commons: every team automation sees only its own retries, while the cloud sees the sum. Restore sweeps are unusually greedy because they enumerate everything, then act on much of it, so they collide with the very production jobs they protect. Quota mapping and scheduling turn an invisible commons problem into an engineering plan.

## Edge cases / pitfalls
- Request quota increases for genuinely steady needs. Clouds grant them when usage evidence is attached.
- Retries count against quota on many services. Backoff is not just politeness, it is quota conservation.
- Tag sweep traffic where the cloud allows it, so quota dashboards can attribute consumption.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_wug5-t_1IV_H8-dGHUp2yg