Real‑Time Monitoring and Alerting for OpenClaw Support Agents: Building Live Dashboards on UBOS

2.1. Capture – Connect to OpenClaw

• Generate an API token in the OpenClaw admin console with read:metrics scope.
• Use UBOS’s Workflow automation studio to create a connector that opens a persistent WebSocket to wss://api.openclaw.io/metrics.
• Map incoming JSON payloads to a canonical schema (e.g., {agent_id, ticket_id, status, response_time, timestamp}).

2.2. Transform – Enrich & Normalize

UBOS’s built‑in transformation engine lets you:

• Convert timestamps to UTC for consistent cross‑region reporting.
• Calculate derived metrics such as average response time per agent and ticket backlog growth rate.
• Tag each event with a region label to support multi‑datacenter deployments.

2.3. Load – Persist to UBOS Data Lake

Persist the normalized stream into UBOS’s time‑series store. This enables:

• Historical trend analysis (e.g., 30‑day SLA compliance).
• Fast retrieval for dashboard widgets.
• Seamless integration with downstream alerting services.

3.1. Choose a Dashboard Template

Start with the UBOS templates for quick start. The “Support Operations” template includes pre‑configured widgets for:

• Active tickets per queue.
• Agent availability heatmap.
• Real‑time SLA breach indicator.

3.2. Bind Data Sources

For each widget, select the corresponding time‑series collection created in Section 2. Use UBOS’s query builder to:

• Aggregate response_time by agent_id (average, median).
• Filter tickets where status = "open" and queue = "priority".
• Set a refresh interval of 5 seconds for truly live updates.

3.3. Add Interactive Controls

Empower agents to slice data on the fly:

• Dropdown to select a specific support team.
• Date‑range picker for custom historical views.
• Toggle to switch between “Tickets” and “Chats” metrics.

4.1. Define Critical Metrics

Typical thresholds for OpenClaw support include:

4.2. Configure Alert Rules in UBOS

1. Navigate to Alerts → New Rule in the UBOS console.
2. Select the metric (e.g., avg_response_time) and choose “Greater than”.
3. Enter the threshold value (e.g., 30 seconds).
4. Choose the notification channel:
   • Telegram – use the Telegram integration on UBOS to push alerts directly to a support channel.
   • Email – configure SMTP settings in the UBOS admin panel.
5. Optionally, add a “cool‑down” period to avoid alert fatigue.

4.3. Test and Refine

Trigger a synthetic event (e.g., artificially inflate response time) to verify that the alert reaches the intended destination. Adjust thresholds based on historical data to balance sensitivity and noise.

5.1. Set Up a Webhook Endpoint in Moltbook

In Moltbook, create an incoming webhook URL (e.g., https://moltbook.example.com/api/webhooks/ubos). Note the secret token generated for authentication.

5.2. Connect UBOS Alerts to Moltbook

1. In the UBOS alert rule editor, add a new action “Webhook”.
2. Paste the Moltbook webhook URL and include the secret token in the header.
3. Map alert fields to Moltbook ticket fields:
   • Title: “SLA breach – Average response time 32 s”.
   • Description: Include a JSON payload with affected agents and ticket IDs.
   • Labels: “urgent”, “OpenClaw”.
4. Enable “Attach Dashboard Snapshot” – UBOS can render a PNG of the current dashboard view and send it as an attachment.

5.3. Collaborative Workflow

When an alert fires:

• Moltbook creates a ticket automatically.
• The ticket contains a live snapshot of the UBOS dashboard, giving responders immediate context.
• Support agents can comment, assign owners, and close the ticket directly from Moltbook, while the dashboard continues to update in real time.