2026-02-273 min

Why Every Agent Needs Its Own Container

DockerInfrastructureBest Practices

The Temptation

When you start working with multiple AI agents, the first idea is often: "I'll just start multiple instances on the same server."

That works — until it doesn't.

One agent uses too much memory → all others slow down

An update for one agent → restart for all

A bug in one skill → can affect other agents

No clear separation of data and access

Each of our 6 agents runs in its own Docker container. That means:

✅ **Isolation** — if Peter crashes, Maya keeps working

✅ **Independent updates** — new skills for one agent without disturbing others

✅ **Resource limits** — each container has its own memory cap

✅ **Security** — agents can't access each other's data

Minimal. Docker Compose makes it simple. One docker-compose.yml, one docker compose up -d, done.

In our playbook, we show exactly what the setup looks like — including the Compose file we actually use.

Fully explained in German too. 🇩🇪

Want to learn more?

Our playbook contains 18 detailed chapters — available in English and German.