Delx
OpenClaw / OpenClaw for Customer Support

OpenClaw for Customer Support

OpenClaw helps support teams move from manual queue handling to controlled, tool-driven workflows with clear escalation paths. Instead of relying on rigid rule trees, your agent classifies tickets by intent, drafts first responses, and routes to the right specialist queue -- all with full session traceability.

Workflow example: ticket triage agent

  1. A new ticket arrives in your helpdesk (Zendesk, Freshdesk, Intercom, or a custom queue).
  2. Your integration sends the ticket subject and body to OpenClaw via quick_session.
  3. OpenClaw classifies the intent (billing, bug report, feature request, account access) and assigns a priority score.
  4. The DELX_META response includes a suggested_category and confidence field.
  5. If confidence is above your threshold (e.g., 0.85), the ticket is auto-routed to the matching queue and a draft response is generated.
  6. If confidence is below threshold, the ticket is flagged for human review with the agent's reasoning attached.
  7. Session metrics are logged so you can track accuracy and routing quality over time.

Code example

Send a ticket to OpenClaw for triage using quick_session:

curl -X POST https://api.delx.ai/v1/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "quick_session",
      "arguments": {
        "agent_id": "support-triage-01",
        "message": "User reports they were charged twice for order #4821. Wants a refund. Tone: frustrated.",
        "context": "ticket_classification"
      }
    }
  }'

Where it helps most

Ticket classification, intent detection, first-response drafting, and smart routing to the right specialist queue. OpenClaw is especially effective for high-volume teams where even a 10% reduction in misrouted tickets saves hours of agent time per day.

Metrics to track

Rollout model

Start with one queue, run in shadow mode for two weeks, compare outcomes with human baseline, then expand gradually by ticket category. Shadow mode means the agent classifies every ticket but does not act -- you compare its decisions against what your human agents did. This gives you a clean accuracy baseline before going live.

FAQ

Can OpenClaw handle live chat?

Yes. OpenClaw agents can participate in live chat workflows by receiving messages through A2A or MCP, classifying intent in real time, drafting responses, and escalating to a human agent when confidence is below your configured threshold. The session stays open so context is preserved across the conversation.

How does OpenClaw route tickets?

OpenClaw uses a quick_session call to classify the ticket's intent and urgency. Based on the DELX_META response, your integration layer reads the suggested category and priority, then pushes the ticket to the matching specialist queue in your helpdesk system.

What's the accuracy of AI ticket classification?

Teams typically see 85-95% correct classification after a two-week shadow-mode calibration period. OpenClaw's session metrics endpoint lets you track classification accuracy and tune confidence thresholds over time.

Related

Back to OpenClaw hubTroubleshooting guide