> 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/8.-evaluate-score-the-outputs/04-score-traces.md).

# Tutorial 4: Score live traces

Tutorial 4 — Score production traces continuously with Stratix trace evaluations.

**Time:** \~30 minutes **Level:** Intermediate **You'll build:** A continuous trace evaluation running on real production traffic.

## What you'll learn

* How to ingest traces from your application
* How to build a trace evaluation
* How to schedule continuous runs
* How to alert on score drift

## Prerequisites

* [ ] Completed [Tutorials 1 and 2](/2.-get-started/all-tutorials.md)
* [ ] An application emitting AI calls (real or simulated)
* [ ] A scoring config (scorers + judges) you want to apply

## Step 1: Ingest traces

In your application, after each AI call:

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

client.traces.create(
 id=correlation_id,
 name="answer-question",
 inputs={"prompt": prompt_text},
 outputs={"response": response_text},
 spans=[
 {"name": "retrieval", "kind": "tool", "inputs": {...}, "outputs": {...}},
 {"name": "llm", "kind": "llm", "model": "claude-opus-4-7", "inputs": {...}, "outputs": {...}}
 ],
 tags={"env": "production", "feature": "support-bot"}
)
```

For batch ingestion:

```python
client.traces.upload("./traces.jsonl")
```

## Step 2: Build the trace evaluation

```python
trace_eval = client.trace_evaluations.create(
 trace_set={"tags": {"env": "production", "feature": "support-bot"}},
 scorers=["scorer-id-1"],
 judges=["judge-id-1"],
 schedule="hourly",
)
print(trace_eval.id)
```

## Step 3: Watch a few runs

After an hour, scores start populating. Open **Premium → Agent Evaluation → Trace evaluations** → your trace eval. The score-over-time chart fills in.

## Step 4: Configure thresholds

```python
client.trace_evaluations.set_alert(
 trace_eval_id=trace_eval.id,
 metric="judge.helpfulness.pass_rate",
 threshold=0.85,
 direction="below",
)
```

When pass rate drops below 85%, you get notified.

## Step 5: Watch the trend

Over a week, the score-over-time chart tells you whether quality is steady or drifting. Drift is the early-warning signal you wanted.

## What's next

* [Tutorial 5: GEPA-optimize a judge](/9.-improve-tune-the-system/05-gepa-optimize.md)
* [Concept: Continuous evaluation](/7.-observe-see-whats-happening/continuous-evaluation.md)
* [Workflow: Observe](/8.-evaluate-score-the-outputs/workflow.md)
* [Stratix Premium — Trace evaluations](/8.-evaluate-score-the-outputs/trace-evaluations.md)