AI Docker/SSH ops: automate deployment and config

Deploy: AI pulls code, builds Docker image, deploys to servers

Agent automates deploy workflow: listens to git push, builds Docker image (or uses pre-built), tests locally in container, deploys to production SSH, runs migrations, checks health. Integrates with GitHub Actions, Kubernetes, SSH servers. $19/mo.

366k+⭐ OpenClaw on GitHub
<5minutes to launch

Sound familiar?

What's eating your time

Deploy slow and manual: ssh to server, pull code, docker build, docker run, check it works, too many steps

No automation: each time someone does same thing manually, risk of human error (forget migration, wrong port)

Rollback hard: if deploy breaks — how to rollback? No quick way

Staging differs from prod: tests pass in staging, break in prod, no pre-deploy checks

Capabilities

What your AI agent can do

Build Docker image and registry push

When git push to main → agent: clone repo, docker build (using Dockerfile), builds image, tags (with version/commit hash), pushes to Docker Hub/GHCR. Automated CI/CD.

Local container testing

Before prod deploy agent runs image locally: docker run -it, checks app starts, endpoints respond, health checks pass. All ok → continue deployment.

SSH deploy and run migrations

Agent SSH to prod server, pulls image, docker stop old container, docker run new, runs db migrations (if needed), updates configuration (env vars, configs). Sequential steps, rolling restart.

Health check and fallback

After deploy agent checks: app live? Metrics normal? If something wrong → can auto-rollback to old image. If ok → counts as successful.

Logging and deployment trail

Agent saves: what image deployed, when, which commit, result (success/rollback). History of all deployments for audit and quick analysis what broke.

Works with your tools

GitHub
GitHub Actions
Docker Hub
SSH/Linux servers
Kubernetes
Slack
How it works

Get started in a few steps

1

Git push and trigger

Developer pushes changes to main (or any watched branch). GitHub webhook → agent sees immediately, starts workflow.

2

Build Docker image

Agent: clone repo, docker build (using Dockerfile in repo), builds image, tags with commit hash (e.g. app:abc123def45), pushes to registry (Docker Hub or GHCR).

3

Local testing

Agent runs image locally: docker run -d --name test-app -p 3000:3000 app:abc123def45, checks: HTTP 200 on /health, logs without errors, container not crashed. If ok → continue.

4

Deploy to production

Agent SSH to prod server, pulls new image, runs: docker pull app:abc123def45, docker stop app-old, docker run -d --name app (new container), runs migrations (db migration script if needed).

5

Health check and notification

Agent monitors new container 60 seconds: HTTP 200, metrics, logs. If ok → posts to Slack 'Deploy successful (abc123, v1.2.3)'. If error → auto-rollback to old image + alert.

FAQ

Frequently asked questions

Yes. Instead of SSH + docker run can kubectl apply -f deployment.yaml (with new image tag). Agent can auto-update manifest and apply. If Helm used → can helm upgrade.

Want OpenClaw — without the DevOps?

OpenKlo is managed hosting for the original OpenClaw. Same agent, live in 3 minutes.

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