> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sovara-labs.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Recommendation algorithm
> How Sovara decides what should be reviewed.
Sovara's recommendation algorithm keeps annotation focused. It does not try to
replace the reviewer. It tries to decide which runs are worth a reviewer's time.
## What it optimizes for
The queue favors runs that may add new information. A run is more useful when it
shows behavior not already covered by nearby annotated examples, exposes a
partial gap in the agent's capability, or contains a failure that is hard to
judge from the final output alone.
The algorithm also reduces repeat work. Runs that look similar to already
reviewed successes should not keep coming back unless they reveal a new pattern.
## Priority labels
Sovara groups surfaced runs by why they deserve attention. The UI shows five
labels:
* **Repeated failure**: the run matches a failure pattern already seen in
related traces.
* **Failure risk**: the run is likely wrong, but it is not yet established as a
repeated failure pattern.
* **Novel behavior**: the run is meaningfully different from reviewed examples.
* **Partially covered**: related examples exist, but the run still tests a gap.
* **Covered**: successful references already cover the behavior, so review is
optional and lower priority.
The label is a starting point, not a verdict. The reviewer still decides whether
the run should be marked as success or failure.
## Reviewer guidance
When Sovara surfaces a run, it includes a short explanation and links to the
trace steps worth checking first. Start there, then open the full run when the
case needs more context.
Click Inspect run, then open
Run chat and
start with:
```text theme={"theme":{"light":"github-light","dark":"github-dark"}}
What should I look at first?
```
Run chat can point you to the final answer, retrieved evidence, related
failed behavior, and the steps cited by the recommendation. Use that as the
starting point for the label and ground truth.
Good annotations make future recommendations better. They tell Sovara which
behaviors are already covered, which failures matter, and which domain lessons
should become lessons.