> 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/6.-build-wire-your-code/03-cicd-gates.md).

# Tutorial 3: Wire CI/CD quality gates

Tutorial 3 — Wire Stratix evaluations into GitHub Actions to gate AI changes.

**Time:** \~30 minutes **Level:** Intermediate **You'll build:** A GitHub Actions workflow that runs a Stratix evaluation on every PR and blocks merge on regression.

## What you'll learn

* How to call Stratix from a CI runner
* How to compare a run against a baseline
* How to fail a build on regression

## Prerequisites

* [ ] Completed [Tutorials 1 and 2](/2.-get-started/all-tutorials.md)
* [ ] A GitHub repo with an AI feature you can iterate on
* [ ] A Stratix evaluation space you want to gate against
* [ ] A GitHub Actions runner

## Step 1: Get an API key

In Stratix Premium → **Settings → API keys** → **New key**. Scope it to evaluation read/write. Copy the key.

In GitHub: **Settings → Secrets and variables → Actions → New repository secret**. Name: `LAYERLENS_STRATIX_API_KEY`.

## Step 2: Create the CI script

`scripts/ci-eval.py`:

```python
import os
import sys
from layerlens import Stratix

client = Stratix(api_key=os.environ["LAYERLENS_STRATIX_API_KEY"])

# Re-run the gating space
space_id = "your-space-id"
run = client.spaces.run(space_id=space_id)
result = client.spaces.wait_for_completion(run.id, timeout=600)

# Fetch baseline (most recent main-branch run)
baseline = client.spaces.latest_run(space_id=space_id, branch="main")

# Compare
delta = result.score - baseline.score
tolerance = 0.01
print(f"Score: {result.score}")
print(f"Baseline: {baseline.score}")
print(f"Delta: {delta:.4f}")

if delta < -tolerance:
 print("REGRESSION beyond tolerance — failing the build.")
 sys.exit(1)
else:
 print("OK")
```

## Step 3: Add the workflow

`.github/workflows/eval.yml`:

```yaml
name: AI Eval Gate

on:
 pull_request:
 paths:
 - 'src/prompts/**'
 - 'src/agents/**'

jobs:
 eval:
 runs-on: ubuntu-latest
 steps:
 - uses: actions/checkout@v4
 - uses: actions/setup-python@v5
 with:
 python-version: '3.11'
 - name: Install
 run: |
 pip install layerlens --extra-index-url https://sdk.layerlens.ai/package
 - name: Run eval
 run: python scripts/ci-eval.py
 env:
 LAYERLENS_STRATIX_API_KEY: ${{ secrets.LAYERLENS_STRATIX_API_KEY }}
```

## Step 4: Open a test PR

Make a small change to a prompt under `src/prompts/`. Push. Watch the GitHub Action run. Verify the eval kicks off.

## Step 5: Tune the tolerance

Start strict (0.01). Loosen as you learn the natural variance of the evaluation. If you get false-positives on noise, you have two choices:

* Increase tolerance
* Make the eval less noisy (more rows, or a determined seed)

## What's next

* [Tutorial 4: Score live traces](/8.-evaluate-score-the-outputs/04-score-traces.md)
* [Use case: AI quality gates in CI/CD](/4.1-general-use-cases/ai-quality-gates-cicd.md)
* [Workflow: Govern](/6.-build-wire-your-code/workflow.md)
* [Integrations: GitHub Actions](/6.-build-wire-your-code/migration.md)