[GitHub Actions](https://github.com/features/actions)

GitHub Actions is GitHub’s built-in continuous integration and continuous delivery (CI/CD) platform. It lets you automate your build, test, and deployment pipeline directly from the same repository where your code already lives, with no separate server to run or maintain. You define a workflow once, and GitHub runs it automatically every time the event you specify happens: a push, a pull request, a scheduled time, or a manual trigger. For AI developers shipping production systems, that means every model update, agent change, or API integration gets tested and deployed the same way, every time, without a human remembering to run the checks by hand.

How GitHub Actions works

A workflow is an automated process described in a YAML file, a plain-text format for structured configuration, saved inside a .github/workflows folder in your repository.

• Trigger: an event you define, like a code push, a pull request, or a cron schedule, starts the workflow.
• Job: each workflow contains one or more jobs, and by default they run in parallel on separate virtual machines called runners.
• Runner: a runner is the machine, GitHub-hosted or self-hosted, that actually executes your job. GitHub provides Linux, macOS, and Windows runners out of the box.
• Step: each job runs a sequence of steps, and a step either executes a shell command directly or calls an action, a reusable, packaged unit of automation you can pull from the GitHub Marketplace or write yourself.
• Secrets: sensitive values like API keys and deployment credentials are stored encrypted at the repository or organization level and injected into the workflow only when it runs.

Because runners spin up fresh for every job, workflows stay consistent and reproducible whether they run on your laptop’s exact setup or not.

What you can build with GitHub Actions

• Automated test suites: run your unit and integration tests on every pull request across multiple language versions and operating systems at once using a matrix build, catching bugs before they merge.
• Model and agent deployment pipelines: package an LLM application or agent, run evaluation scripts against it, and push it to staging or production automatically whenever the main branch changes.
• Docker image builds: build a container image on every commit and push it straight to a registry like GitHub Container Registry or Docker Hub, keeping your deployable artifact always current.
• Scheduled jobs: run nightly data refreshes, dependency updates, security scans, or backups on a cron schedule without a separate task scheduler.
• Documentation and site deployment: rebuild and publish a docs site or static frontend to GitHub Pages automatically the moment content changes.
• Cross-cloud deployment: trigger a deploy to AWS, Google Cloud, or Azure directly from a workflow step, using official actions built by each cloud provider.