CI/CD

CI/CD stands for Continuous Integration and Continuous Delivery (or Continuous Deployment). It is a set of practices and automated pipelines that build, test, and deploy code changes reliably and frequently. CI focuses on merging and validating code, while CD automates the release process to staging or production environments.

Why It Matters

Manually deploying an AI assistant is error-prone and slow. A CI/CD pipeline ensures that every code change is automatically tested for regressions, built into a container image, and deployed to your hosting platform. This means faster iteration cycles, fewer broken deployments, and the confidence that what works in your test environment will work in production. For teams maintaining AI assistant wrappers, CI/CD catches issues like missing dependencies or broken health checks before they reach users.

How It Works

A typical CI/CD pipeline has several stages. The build stage compiles source code and creates artifacts like Docker images. The test stage runs linting, unit tests, and integration tests, including smoke tests that verify the container starts correctly. The deploy stage pushes the validated image to a registry and triggers a deployment on your platform.

Platforms like GitHub Actions, GitLab CI, and Railway's built-in builder each provide pipeline configuration through YAML files or automatic detection. Railway, for example, detects a Dockerfile in your repository and automatically builds and deploys on every push to the main branch.

In Practice

For AI assistant deployments, include health check verification in your pipeline so a failed deployment automatically rolls back. Use environment-specific variables to separate staging from production credentials. Pin dependency versions in your Dockerfile and lock files to ensure reproducible builds, and run security scans on your container images to catch vulnerabilities early.