> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/7.-observe-see-whats-happening/postrelease-continuous.md).

# Post-release continuous evaluation

After release, score recent production traces on a recurring cadence (hourly / daily). Stratix's SDK doesn't ship a scheduler — wire the per-trace `client.trace_evaluations.create(...)` call into your own scheduled job.

## Per-run pattern

```python
from layerlens import Stratix
client = Stratix()

# Pull a sample of recent production traces
recent = client.traces.get_many(
 page=1,
 page_size=50,
 sort_by="created_at",
 sort_order="desc",
 # plus any source / judge_id / search filters relevant to your feature
)

# Score each with your continuous-eval judge
import asyncio
from layerlens import AsyncStratix

async def score_batch():
 aclient = AsyncStratix()
 creates = [aclient.trace_evaluations.create(trace_id=t.id, judge_id=CONTINUOUS_JUDGE_ID)
 for t in recent.traces]
 evals = await asyncio.gather(*creates)
 return await asyncio.gather(*[aclient.trace_evaluations.wait_for_completion(e.id) for e in evals])

results = asyncio.run(score_batch())
fails = [r for r in results if not r.passed]
if len(fails) / max(len(results), 1) > 0.05:
 alert_oncall(fails)
```

## Wire to a scheduler

Run this script from a GitHub Actions schedule, AWS EventBridge → Lambda, k8s CronJob, or any scheduler — same pattern as [daily smoke eval](/9.-improve-tune-the-system/daily-smoke-eval.md).

## See also

* [Concept: Continuous evaluation](/7.-observe-see-whats-happening/continuous-evaluation.md)
* [Tutorial 4: Score live traces](/8.-evaluate-score-the-outputs/04-score-traces.md)


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