Deploying an AI agent used to mean provisioning EC2 instances, configuring Dockerfiles, setting up load balancers, and hoping nothing falls over at 3am. With Divzero and Openclaw, that's gone. Here's how to go from zero to a live, persistent agent in under a minute.

What You'll Need

An Openclaw agent config file (openclaw.yaml)
A Divzero account (early access)
60 seconds

Step 1: Write Your Agent Config

Openclaw agents are defined in a simple YAML file. Here's a minimal example:

name: customer-support-agent
model: claude-sonnet-4-5
system_prompt: |
  You are a helpful customer support agent for Acme Corp.
  You have access to order history and can process refunds.
tools:
  - search_orders
  - process_refund
  - send_email
memory:
  type: persistent
  ttl: 30d

That's it. No Dockerfile. No Kubernetes manifest. No IAM roles.

Step 2: Connect Your Repo

In your Divzero dashboard, click New Agent and connect your GitHub repository. Divzero detects your openclaw.yaml automatically.

# Or push via CLI (coming soon)
divzero deploy --config openclaw.yaml

Step 3: Watch It Go Live

Divzero builds a container from your config, provisions the runtime, and gives you a live endpoint in under 60 seconds. You'll see the agent appear in your dashboard with:

Status: Active
Endpoint: https://agents.divzero.one/your-agent
Memory: Initialized and ready
Traces: Live tool call tracing

What Happens Under the Hood

When you deploy, Divzero:

Validates your openclaw.yaml against the Openclaw schema
Builds a minimal container with the Openclaw runtime
Provisions an EC2 instance from our auto-scaling pool
Mounts a persistent memory store scoped to your agent
Starts the agent and begins capturing traces

All of this happens without you touching a single cloud console.

Persistent Memory Out of the Box

Unlike raw Claude API calls that start fresh every conversation, Openclaw agents on Divzero get persistent memory by default. Your agent remembers:

Previous conversations with users
Tool call results it decided to cache
Any data it explicitly wrote to memory

This is what makes agents actually useful for production — they learn and retain context across sessions.

Full Tool Call Tracing

Every tool invocation your agent makes is logged and visualized in the Divzero dashboard. You can see exactly:

Which tools were called, in what order
What arguments were passed
What the tool returned
How long each call took
Total cost per run

No more digging through raw CloudWatch logs.

What's Next

Once your agent is live, you can:

Set up webhooks to trigger the agent from external events
Configure rate limits and cost caps per run
Add team members who can view traces without touching config
Scale to multiple instances with one slider

Get early access to Divzero →

Deploying Your First Openclaw Agent in 60 Seconds