aracli-deploy-management

SKILL.md

Deploying OpenClaw Agent Systems

Skill by ara.so — Daily 2026 Skills collection.

A practical guide to deploying and managing OpenClaw-compatible AI agent systems. Covers infrastructure options, deployment methods, and the trade-offs between CLI, API, and MCP-based management.

Infrastructure Options

1. Cloud VMs (AWS, GCP, Azure, Hetzner)

Spin up VMs and run agents as containerized services.

# Example: Docker Compose on a cloud VM
docker compose up -d agent-runtime

Pros:

Familiar ops tooling (Terraform, Ansible, etc.)

Easy to scale horizontally — just add more VMs

Pay-as-you-go pricing on most providers

Full control over networking and security

Cons:

You own the uptime — no managed restarts or healing

GPU instances get expensive fast

Cold start if you're spinning up on demand

Best for: Teams that already have cloud infrastructure and want full control.

2. Managed Container Platforms (Railway, Fly.io, Render)

Deploy agent containers without managing VMs directly.

# Example: Railway
railway up

# Example: Fly.io
fly deploy

Pros:

Zero server management — just push code

Built-in health checks, auto-restarts, and scaling

Easy preview environments for testing agent changes

Usually includes logging and metrics out of the box

Cons:

Less control over the underlying machine

Can get costly at scale compared to raw VMs

Cold starts on free/hobby tiers

GPU support is limited or nonexistent on most platforms

Best for: Small teams that want to move fast without an ops burden.

3. Bare Metal (Hetzner Dedicated, OVH, Colo)

Run agents directly on physical servers for maximum performance per dollar.

# Example: systemd service on bare metal
sudo systemctl start agent-runtime

Pros:

Best price-to-performance ratio, especially for GPU workloads

No noisy neighbors — predictable latency

Full control over hardware, kernel, drivers

No egress fees

Cons:

You manage everything: OS, networking, failover, monitoring

Scaling means ordering and provisioning new hardware

No managed load balancing — you build it yourself

Best for: Cost-sensitive workloads, GPU-heavy inference, or teams with strong ops skills.

4. Serverless / Edge (Lambda, Cloudflare Workers, Vercel Functions)

Run lightweight agent logic at the edge without persistent infrastructure.

# Example: deploy to Cloudflare Workers
wrangler deploy

Pros:

Zero idle cost — pay only for invocations

Global distribution with low latency

No servers to patch or maintain

Scales to zero and back automatically

Cons:

Execution time limits (often 30s–300s)

No persistent state between invocations

Not suitable for long-running agent sessions

Limited runtime environments (no arbitrary binaries)

Best for: Stateless agent endpoints, webhooks, or lightweight tool-calling proxies.

5. Hybrid

Combine approaches: use managed platforms for the API layer and bare metal for the agent runtime.

User → API (Railway/Vercel) → Agent Runtime (bare metal GPU)

Pros:

Each layer runs on the most cost-effective infra

API layer gets managed scaling, agent layer gets raw performance

Can migrate layers independently

Cons:

More moving parts to coordinate

Cross-network latency between layers

Multiple deployment pipelines to maintain

Best for: Production systems that need both cheap inference and a polished API layer.

Management Methods: CLI vs API vs MCP

Once your agents are deployed, you need a way to manage them — ship updates, check status, roll back. There are three main approaches.

CLI

A command-line tool that talks to your agent infrastructure over SSH or HTTP.

# Typical CLI workflow
mycli status
mycli deploy --service agent
mycli rollback
mycli logs agent --tail

Pros:

Fast for operators — one command, done

Easy to script and compose with other CLI tools

Works great in CI/CD pipelines

Low overhead, no server-side UI to maintain

Cons:

Requires terminal access and auth setup

Hard to share with non-technical team members

No real-time dashboard or visual overview

Each tool has its own CLI conventions to learn

Best for: Day-to-day operations by the team that built the system.

API

A REST or gRPC API that exposes deployment operations programmatically.

# Deploy via API
curl -X POST https://deploy.example.com/api/v1/deploy \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"service": "agent", "version": "v42"}'

# Check status
curl https://deploy.example.com/api/v1/status

Pros:

Language-agnostic — any HTTP client can use it

Easy to integrate with dashboards, Slack bots, or other systems

Can enforce auth, rate limiting, and audit logging at the API layer

Enables building custom UIs on top

Cons:

More infrastructure to build and maintain (the API itself)

Versioning and backwards compatibility become your problem

Latency overhead compared to direct CLI-to-server

Auth token management adds complexity

Best for: Teams building internal platforms or integrating deploys into larger systems.

MCP (Model Context Protocol)

Expose deployment operations as MCP tools so AI agents can manage infrastructure directly.

{
  "tool": "deploy",
  "input": {
    "service": "agent",
    "version": "latest",
    "strategy": "rolling"
  }
}

Pros:

Agents can self-manage — deploy, monitor, and rollback autonomously

Natural language interface for non-technical users ("deploy the latest agent")

Composable with other MCP tools (monitoring, alerting, etc.)

Fits naturally into agentic workflows

Cons:

Newer pattern — less battle-tested tooling

Requires careful permission scoping (you don't want an agent force-pushing to prod unsupervised)

Debugging is harder when the caller is an LLM

Needs guardrails: confirmation steps, dry-run modes, blast radius limits

Best for: Agentic DevOps workflows where AI agents participate in the deploy lifecycle.

Comparison Matrix

CLI
API
MCP

Speed to set up
Fast
Medium
Medium

Automation
Scripts/CI
Any HTTP client
Agent-native

Audience
Engineers
Engineers + systems
Engineers + agents

Observability
Terminal output
Structured responses
Tool call logs

Auth model
SSH keys / tokens
API tokens / OAuth
MCP auth scopes

Best paired with
Bare metal, VMs
Managed platforms
Agent orchestrators

Recommendations

Starting out? Use a managed platform (Railway, Fly.io) with their built-in CLI. Least ops burden.

Cost matters? Go bare metal with a simple CLI for deploys. Best bang for buck.

Building a platform? Invest in an API layer. It pays off as the team grows.

Agentic workflows? Add MCP tools on top of your existing API. Don't replace your API with MCP — wrap it.

GPU inference? Bare metal or reserved cloud instances. Serverless doesn't work for long-running inference.