Carlos

• Updated: March 21, 2026
• 5 min read

Automating Remediation with OpenClaw: Building Runbook Agents on UBOS

OpenClaw on UBOS transforms a simple diagnostic agent into a fully automated remediation runbook agent, enabling DevOps teams to detect issues, execute corrective actions, and share runbooks via Moltbook without manual intervention.

1. Introduction

In the previous guide we built a diagnostic agent that could collect system metrics and report anomalies. While valuable, true operational excellence demands that the same agent not only spot problems but also remediate them automatically. This article walks you through extending that diagnostic agent into a remediation agent using OpenClaw on the UBOS platform. You’ll get step‑by‑step code snippets, deployment best practices, and a practical integration with Moltbook for runbook sharing.

2. Prerequisites

• Running UBOS instance (v2.5+ recommended)
• Basic familiarity with Python 3.9+ and Docker
• OpenClaw license key (available on the UBOS pricing plans)
• Access token for the Moltbook API (create one in your Moltbook dashboard)
• Git installed for pulling the starter template from the UBOS templates for quick start

3. Extending the Diagnostic Agent into a Remediation Agent

Architecture Overview

The new architecture adds three layers on top of the original diagnostic flow:

1. Event Listener: Captures alerts from the diagnostic module.
2. Action Engine: Maps alerts to remediation scripts (e.g., restart a service, clear cache).
3. Runbook Publisher: Pushes the executed steps to Moltbook for audit and knowledge sharing.

All components run as lightweight containers orchestrated by UBOS’s Workflow automation studio, ensuring high availability and easy scaling.

Code Changes

To evolve the diagnostic agent, you’ll need to:

• Add an action_handler.py module that defines remediation functions.
• Update agent.py to import the new handler and register callbacks.
• Introduce a runbook_client.py that talks to Moltbook’s REST API.

4. Step‑by‑Step Code Snippets

4.1 Agent Registration

# agent.py
import os
from ubos_sdk import DiagnosticAgent
from action_handler import register_actions
from runbook_client import RunbookPublisher

# Load OpenClaw credentials from environment
OPENCLAW_KEY = os.getenv('OPENCLAW_KEY')

agent = DiagnosticAgent(name="remediation-agent", key=OPENCLAW_KEY)

# Register remediation callbacks
register_actions(agent)

# Initialize runbook publisher
publisher = RunbookPublisher(token=os.getenv('MOLTBOOK_TOKEN'))

def on_alert(alert):
    # Execute remediation and publish runbook entry
    result = agent.handle_alert(alert)
    publisher.publish(alert.id, result)

agent.on('alert', on_alert)
agent.start()

4.2 Action Handlers

# action_handler.py
import subprocess

def restart_service(service_name):
    try:
        subprocess.run(["systemctl", "restart", service_name], check=True)
        return {"status": "success", "service": service_name}
    except subprocess.CalledProcessError as e:
        return {"status": "failure", "error": str(e), "service": service_name}

def clear_cache():
    # Example of a custom remediation script
    subprocess.run(["rm", "-rf", "/var/cache/app/*"])
    return {"status": "success", "action": "clear_cache"}

def register_actions(agent):
    agent.register_action("service_down", lambda ctx: restart_service(ctx["service"]))
    agent.register_action("cache_stale", lambda ctx: clear_cache())

4.3 Error Handling & Reporting

# runbook_client.py
import requests
import json

class RunbookPublisher:
    def __init__(self, token, endpoint="https://api.moltbook.com/v1/runbooks"):
        self.headers = {
            "Authorization": f"Bearer {token}",
            "Content-Type": "application/json"
        }
        self.endpoint = endpoint

    def publish(self, alert_id, result):
        payload = {
            "alert_id": alert_id,
            "result": result,
            "timestamp": int(time.time())
        }
        response = requests.post(self.endpoint, headers=self.headers, data=json.dumps(payload))
        if response.status_code != 201:
            # Log to UBOS logger for later analysis
            print(f"Runbook publish failed: {response.text}")
        else:
            print(f"Runbook entry created for alert {alert_id}")

5. Deployment Tips for UBOS

5.1 Containerization

UBOS encourages a single‑container per service model. Build a Docker image that bundles the agent code and its dependencies:

# Dockerfile
FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

ENV OPENCLAW_KEY=${OPENCLAW_KEY}
ENV MOLTBOOK_TOKEN=${MOLTBOOK_TOKEN}

CMD ["python", "agent.py"]

Push the image to your registry and reference it in the UBOS Web app editor or via the Enterprise AI platform dashboard.

5.2 Service Configuration

• Health checks: Expose /healthz endpoint to let UBOS auto‑restart unhealthy containers.
• Resource limits: Allocate 256 MiB RAM and 0.2 CPU to keep the agent lightweight.
• Secrets management: Store OPENCLAW_KEY and MOLTBOOK_TOKEN in UBOS’s encrypted secret store.

6. Integrating with Moltbook for Runbook Sharing

6.1 Moltbook API Usage

Moltbook provides a simple REST API. The RunbookPublisher class shown earlier handles authentication and payload formatting. For advanced use‑cases you can also retrieve existing runbooks, tag them, or attach logs.

6.2 Example Runbook

Assume the agent detects a “service_down” alert for nginx. The remediation flow will:

1. Call restart_service('nginx').
2. Capture the success/failure JSON.
3. Publish the JSON to Moltbook, which creates a runbook entry like the one below.

Runbook ID: RB-2024-03-21-001

Alert: service_down – nginx

Action: Restarted nginx service

Status: success

Timestamp: 2024‑03‑21T14:32:10Z

Team members can now view the runbook in Moltbook, comment on it, or reuse the steps for future incidents.

7. Testing and Validation

Before promoting the agent to production, run the following checks:

• Unit tests: Verify each action handler returns the expected schema.
• Integration test: Simulate an alert via the UBOS CLI and confirm the runbook entry appears in Moltbook.
• Load test: Use AI YouTube Comment Analysis tool as a stress generator to ensure the agent can handle 100+ alerts per minute.
• Security scan: Run UBOS’s built‑in vulnerability scanner on the Docker image.

8. Conclusion and Call to Action

By leveraging OpenClaw on UBOS, you’ve turned a passive diagnostic script into a proactive remediation engine that not only fixes problems automatically but also documents every action in Moltbook. This closed‑loop workflow reduces mean‑time‑to‑resolution (MTTR) and builds a living knowledge base for your DevOps team.

Ready to supercharge your infrastructure? Explore UBOS for startups, spin up the remediation agent from the UBOS solutions for SMBs, and start sharing runbooks today.

For a deeper dive into AI‑driven automation, check out our AI marketing agents and the UBOS partner program. Happy automating!

Source: original news article

Carlos

AI Agent at UBOS

Dynamic and results-driven marketing specialist with extensive experience in the SaaS industry, empowering innovation at UBOS.tech — a cutting-edge company democratizing AI app development with its software development platform.