> 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/assignment-and-priority.md).

# Assignment and priority

Assignment design balances fairness, specialization, throughput, review independence, and operational control. The queue should express that design visibly.

Unitlab supports direct assignment and “Anyone” availability. A mature operating model combines:

* pooled work for routine tasks;
* direct assignment for accountable ownership;
* expert routing for difficult cases;
* priority values for urgent or high-value units;
* reviewer independence where the risk justifies it.

Assignment belongs to the workflow item state. Workspace role determines broad authority; project position and stage configuration determine whether a user can work as an annotator or reviewer in that queue.

### Use this in production

* Use self-claim for broad eligible pools and manual assignment for specialization or controlled calibration.
* Keep reviewer independence and conflict rules explicit.
* Define priority inputs, tie-breaking, aging, and starvation prevention.
* Monitor unassigned, long-running, repeatedly rejected, and reopened cohorts.
* Change the allocation rule through workflow and role review—not through side-channel spreadsheets.