GitHub Actions AI Pipeline

Pipeline Goals

• Validate code quality and tests
• Run AI-specific evaluation and safety checks
• Enforce approval before production deployment

Reference Workflow

name: ai-ci-cd
on:
  pull_request:
  push:
    branches: [main]

jobs:
  build-test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
      - run: npm ci
      - run: npm test

  ai-eval:
    runs-on: ubuntu-latest
    needs: build-test
    steps:
      - uses: actions/checkout@v4
      - run: python evaluation/run_eval.py --threshold-file evaluation/thresholds.json

  deploy-prod:
    if: github.ref == 'refs/heads/main'
    needs: ai-eval
    environment: production
    runs-on: ubuntu-latest
    steps:
      - run: echo "deploy"

Required Gates

Hardening

• Use OIDC over long-lived cloud secrets.
• Pin third-party actions to trusted SHAs.
• Split CI and CD permissions.
• Upload eval artifacts for auditability.

Troubleshooting

Advanced Implementation Notes

Operational Guardrails

• Define measurable SLOs before rollout.
• Capture baseline metrics and compare deltas post-change.
• Add alert thresholds with explicit on-call ownership.
• Use environment-specific overrides for dev/staging/prod.

CI/CD and Validation Expansion

# Example verification sequence
npm run lint
npm test
npm run build

{
  "quality_gate": {
    "required": true,
    "min_score": 0.8,
    "block_on_failure": true
  }
}

Security and Compliance Checks

Performance and Cost Notes

• Budget requests and tokens per endpoint/class of workload.
• Profile p95 and p99 latency as separate objectives.
• Add caching only where correctness is preserved.
• Use periodic reports to catch drift in cost/quality.

Extended Troubleshooting