> 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/4.1-general-use-cases/continuous-evaluation.md).

# Continuous evaluation

Score live production traces on a recurring schedule — surface drift before customers do.

Pre- and post-deployment evaluations catch regressions before merge. **Continuous evaluation** catches them after merge — when something slips through, when production traffic shifts, when the model provider quietly updates their backend.

## The shape of the work

1. **Ingest live traces.** Your application emits trace JSON; Stratix ingests via API or SDK.
2. **Define a trace evaluation.** Pick the scorers and judges you want applied. The same scorers and judges from your pre- and post-deployment evaluations work here.
3. **Schedule it.** Run the trace evaluation on a recurring cadence — daily, hourly, per-batch.
4. **Watch the trend.** Stratix shows score-over-time per scorer per dimension.
5. **Get notified on drift.** When a score crosses a threshold, surface a notification (Premium) or a Slack alert.

## Why it works on Stratix

* **Same engine across pre-deploy and continuous.** The scorers and judges you built for CI gates are reusable.
* **Trace-first design.** Stratix's trace pipeline is shaped for continuous workflows; ingestion is rate-limited and persistent.
* **GEPA-optimized judges.** Your judge agreement with humans stays tight as live traffic shifts.
* **Org-scoped and tenant-isolated.** One tenant's burst doesn't affect another.

## Tools you'll use

* [Stratix Premium — Trace evaluations](/8.-evaluate-score-the-outputs/trace-evaluations.md)
* [Stratix Premium — Traces](/7.-observe-see-whats-happening/traces.md)
* [SDK: trace ingestion](/4.1-general-use-cases/general.md)
* [Notifications](https://github.com/LayerLens/gitbook-full/blob/main/13-reference/sdk-python/notifications.md)

## Outcomes you should see

You'll know this is working when:

* **Mean time to detect a quality regression drops to <1 hour** (versus days/weeks of customer-driven discovery).
* **Score-over-time charts are reviewed weekly** by the team that owns the AI feature.
* **Threshold alerts fire and resolve** — silence is suspicious; chronic firing means the threshold needs tuning.
* **Sample bias is measured**, not assumed — you can show the sample distribution matches production.

## Anti-patterns

* **Only scoring sampled traces.** Sampling is fine, but watch sample bias. Production distribution drifts; sample drifts with it.
* **Mixing pre-deploy and continuous in one config.** They have different cadence and different cost profiles. Keep them separate.
* **No alerting.** A continuously-evaluated trace pipeline that nobody reads is decoration.

## Where to next

* [Concept: Continuous evaluation](/4.1-general-use-cases/continuous-evaluation.md)
* [Tutorial: Score live traces](/8.-evaluate-score-the-outputs/04-score-traces.md)
* [Workflow: Observe](/9.-improve-tune-the-system/workflow.md)
* [Cookbook: continuous-eval recipes](/2.-get-started/all-cookbook-recipes.md)