> For the complete documentation index, see [llms.txt](https://docs.unitlab.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.unitlab.ai/documentation/queues/queue-failures-and-recovery.md).

# Queue failures and recovery

Queue recovery starts by identifying the layer that failed: permission, workflow stage, assignment, annotation validation, source processing, model inference, batch processing, or downstream release creation.

### The operating model

```mermaid
flowchart TB
  A["Symptom"]
  B["Role + stage"]
  C["Assignment + task state"]
  D["Source + validation"]
  E["Batch/model processing"]
  F["Owned recovery"]
  A --> B
  B --> C
  C --> D
  D --> E
  E --> F
```

### Use this in production

* A missing button usually starts with role, stage, selection, or resource-state checks.
* Invalid annotations remain saved but require property or value correction before acceptance.
* Rejected work needs a corrective route; failed processing needs operational recovery.
* For partial batches, preserve succeeded items and retry only after remote-state inspection.
* Escalate systemic failure patterns with affected IDs, counts, timestamps, versions, and redacted errors.