One-Command Automated Linux VPS Hardening & Multi-Agent Fleet Bootstrap

01 Cloud Infrastructure Without Manual Tedium

Deploying autonomous AI agents into production environments often deteriorates into a fragile mess of scattered Docker containers, exposed database ports, manual SSL configuration, and insecure SSH defaults. When an agent requires persistent vector storage, visual debugging, scheduled tasks, and webhook listeners, manually provisioning servers takes hours and inevitably introduces security holes.

To solve this, we created linux-vps-agent-provisioner: a zero-dependency Bash automation harness that transforms a bare Ubuntu 24.04 server into a hardened, production-ready multi-agent command station in under 60 seconds.

02 Hardened Perimeter & Container Topology

The architecture enforces strict isolation. External ingress is permitted only on port 22 (restricted SSH with key-only auth) and ports 80/443 (managed by Caddy with automated Let's Encrypt TLS). Internal agent services run inside an isolated Docker bridge network where containers communicate over cryptographically secure local hostnames:

03 The 60-Second Provisioning Script

The bootstrap script is idempotent, meaning it can be re-run safely at any time to verify system integrity and repair dropped services:

#!/usr/bin/env bash
set -euo pipefail

# 1. Update OS packages & install runtime dependencies
export DEBIAN_FRONTEND=noninteractive
apt-get update && apt-get upgrade -y
apt-get install -y ufw fail2ban curl git jq htop ca-certificates

# 2. Hardened UFW perimeter configuration
ufw default deny incoming
ufw default allow outgoing
ufw limit 22/tcp comment 'SSH brute-force rate limiting'
ufw allow 80/tcp comment 'HTTP automated ACME challenge'
ufw allow 443/tcp comment 'HTTPS edge ingress'
ufw --force enable

# 3. Deploy isolated Docker multi-agent compose stack
mkdir -p /opt/agent-station && cd /opt/agent-station
curl -fsSL https://get.docker.com | sh
docker compose up -d

04 Qdrant Vector DB & Persistent Memory

Autonomous workflows require high-throughput episodic memory. We configured Qdrant with HNSW graph indexing tuned specifically for dense embeddings (384-dim all-MiniLM-L6-v2 or 1536-dim text-embedding-3-small). All vector indexes persist to host NVMe volumes with WAL (Write-Ahead Logging) enabled.

HTTP/1.1 200 OK
Content-Type: application/json

{
  "result": {
    "status": "green",
    "vectors_count": 48210,
    "indexed_vectors_count": 48210,
    "points_count": 48210,
    "segments_count": 4,
    "config": {
      "params": {
        "vectors": { "size": 1536, "distance": "Cosine" }
      },
      "hnsw_config": { "m": 16, "ef_construct": 100 }
    }
  },
  "time": 0.0014
}
  

05 n8n Autonomous Workflow Execution

n8n provides a visual node-based execution DAG that interfaces directly with local models, webhooks, and GitHub APIs. We configure n8n with N8N_ENFORCE_SETTINGS_FILE_PERMISSIONS=true and bind its webhook listener behind Caddy with rate limiting and automated basic authentication.

06 Automated Fail2ban & UFW Defense

Every cloud host is bombarded with thousands of automated SSH and port scans daily. The provisioner installs custom Fail2ban jail profiles:

[sshd]
enabled = true
port = 22
filter = sshd
maxretry = 3
findtime = 600
bantime = 86400
action = iptables-multiport[name=SSH, port="22", protocol=tcp]

07 Deployment Latency & Resource Footprint

Empirical measurements recorded across 5 clean VPS deployments on Linode, Hetzner, and DigitalOcean:

08 Repository & Production Playbook

The complete bootstrap script, docker-compose template, and Caddyfile configurations are open source:

github.com/axe01010/linux-vps-agent-provisioner →