How to Deploy and Use Mission Control for AI Agents - A Step-by-Step Tutorial

How to Deploy and Use Mission Control for AI Agent Operations

In this tutorial, we'll walk through setting up Mission Control from scratch, connecting it to agent runtimes, and using its dashboard to dispatch tasks and monitor costs. By the end, you'll have a fully operational AI agent control plane running on your own infrastructure.

Prerequisites

• A server with Docker installed (Ubuntu 22.04+ or similar)
• At least 2 CPU cores and 4GB RAM (8GB recommended)
• Docker Compose (optional but recommended)
• One or more AI agent runtimes to connect (e.g., OpenClaw, Claude Code, Codex)

Step 1: Deploy with Docker Compose

Create a docker-compose.yml file:

services:
  mission-control:
    image: ghcr.io/builderz-labs/mission-control:latest
    restart: unless-stopped
    ports:
      - "8080:8080"
    volumes:
      - ./data/mission-control:/data

Start the service:

docker compose up -d

Step 2: Initial Setup

Open your browser and navigate to http://your-server-ip:8080. You'll be greeted by the setup wizard:

1. Create an admin account — set your email and password
2. Configure your first agent runtime — choose from OpenClaw, Claude Code, Codex, or add a custom runtime
3. Set spending limits — define budgets per agent and per task to prevent cost overruns
4. Invite team members — optionally add collaborators with role-based permissions

Step 3: Dispatching Your First Task

Once your agent runtime is connected:

1. Click "New Task" in the top-right corner
2. Select the target agent runtime from the dropdown
3. Enter your task description or prompt
4. Set priority (Low, Normal, High, Critical)
5. Click "Dispatch"

You'll see the task appear in the dashboard with a status indicator. Click on it to view the real-time execution log.

Step 4: Monitoring and Cost Tracking

The real power of Mission Control becomes apparent when you have multiple agents running. The dashboard provides:

Step 5: Reviewing Run Logs

Click any completed task to open the detailed view. Here you'll find:

• Full execution transcript
• Token count and cost breakdown
• Duration and performance metrics
• Error logs (if any)
• Export option for compliance records

Conclusion

Mission Control transforms how teams operate AI agents. Instead of juggling multiple terminal sessions and spreadsheets for tracking costs, you get a professional operations dashboard that scales with your needs. With its straightforward Docker deployment and intuitive interface, you can be up and running in under 10 minutes.