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Build an AI Agent for Support Inbox Triage with Human Approval

A practical OpenClaw or Hermes workflow for sorting support messages, drafting replies, tagging issues, and escalating urgent cases without letting the agent send blindly.

May 3, 2026 · ClawMama Team

Reader persona: a founder, support lead, customer success operator, or small team handling support messages across email, chat, Telegram, Slack, or helpdesk tools.

Job to be done: use an AI agent to triage support messages, draft replies, tag issues, and escalate urgent cases while keeping human approval before customer-facing sends.

Support inboxes become painful when every message looks equally urgent.

A good support triage agent does not need to solve every ticket.

It needs to answer four questions quickly:

  1. What is this message about?
  2. How urgent is it?
  3. Who should handle it?
  4. What draft reply or next step should a human review?

That is a good workflow for OpenClaw or Hermes.

The workflow

The safe workflow is:

inbound message → classify → extract facts → draft response → route/escalate → human approval → send/update

The important part is the approval layer.

The agent may draft, tag, and prepare. It should not send customer-facing messages or change critical records without approval.

Step 1: Define triage categories

Ask your agent to create a triage taxonomy:

# Support Triage Categories

## Billing
Questions about payments, invoices, credits, refunds, or plan changes.

## Login / access
Account access, authentication, password, Telegram bot access, or permissions.

## Bug report
Something appears broken. Needs reproduction details.

## Feature request
Customer asks for a new feature or improvement.

## How-to
Customer needs help using an existing feature.

## Urgent / incident
Service down, data loss, security concern, payment failure, or high-value customer blocked.

## Spam / low priority
Irrelevant, promotional, or no clear user request.

Keep the taxonomy short at first.

Too many categories make triage slower and less reliable.

Step 2: Define urgency levels

Use a simple urgency scale:

# Urgency

## P0 — Critical
Security issue, data loss, production outage, billing/payment breakage for many users.

## P1 — High
A paying customer is blocked, a core workflow is broken, or there is an urgent launch blocker.

## P2 — Normal
Standard product question, bug report, or feature request.

## P3 — Low
Nice-to-have feedback, unclear issue, duplicate, spam, or low-context request.

Ask the agent to include a reason for every urgency label.

Step 3: Create the triage prompt

Use a prompt like this:

You are my support inbox triage agent.

For each inbound message, produce:
1. category;
2. urgency;
3. short summary;
4. facts extracted from the message;
5. missing information to ask for;
6. suggested assignee or route;
7. draft reply;
8. whether human approval is required.

Rules:
- Do not send replies without approval.
- Do not promise refunds, discounts, timelines, or legal/security conclusions.
- Do not ask for sensitive credentials.
- Escalate security, billing, data loss, and outage reports.
- If uncertain, label confidence as low.

This turns support triage into a repeatable workflow.

Step 4: Use a structured output format

Ask for this output:

# Support Triage

## Category
Billing / Login / Bug / Feature / How-to / Urgent / Spam

## Urgency
P0 / P1 / P2 / P3

## Summary
One or two sentences.

## Extracted facts
-

## Missing information
-

## Suggested route
-

## Draft reply
Customer-facing draft, not sent.

## Approval required?
Yes / No

## Confidence
High / Medium / Low

The structure matters because it lets the human review quickly.

Step 5: Draft replies without sending

A useful support reply should be short and precise.

Ask:

Draft a customer reply.

Rules:
- be concise and calm;
- acknowledge the issue;
- ask for only the missing information needed;
- do not overpromise;
- do not mention internal routing;
- do not send without approval.

Example output:

Hi Jane — thanks for the report. I understand the Telegram bot is not responding after you created a new agent.

Could you send:
1. the approximate time this happened;
2. the agent name;
3. any error message you saw?

We’ll use that to check the run logs and narrow it down.

That is useful draft work.

It still leaves the send decision to a human.

Step 6: Add escalation rules

Some messages should never be handled as normal support.

Add escalation rules:

# Escalate immediately if the message mentions:
- security vulnerability;
- data leak;
- unauthorized access;
- payment charged incorrectly;
- production outage;
- user cannot access a paid service;
- legal complaint;
- refund demand from a high-value customer;
- public complaint going viral.

The agent should flag these clearly:

Escalation: yes
Reason: possible billing error affecting a paid user
Suggested route: human operator / billing owner

Step 7: Save support learnings

Ask the agent to maintain a support learning log:

# Support Learning Log

## Common issues
-

## Repeated questions
-

## Missing docs
-

## Product feedback
-

## Suggested help center articles
-

This turns support messages into product and content input.

For example:

Repeated question:
Users ask whether the agent can send messages without approval.

Suggested doc:
Write a short trust model page explaining human approval, revoke, and logs.

That is how support becomes marketing and product intelligence.

Step 8: Use ClawMama to run it

If you already run OpenClaw or Hermes, build this in your existing runtime.

If you want a ready-to-use agent, create one with ClawMama:

https://t.me/clawmamarun_bot

ClawMama gives you a managed OpenClaw or Hermes agent. New users get $2 of AI credits and access to the latest ChatGPT model, so you can test this support triage workflow before maintaining your own infrastructure.

A good first message is:

Help me build a support inbox triage workflow.

Create:
1. a triage taxonomy;
2. urgency levels;
3. a structured output template;
4. escalation rules;
5. a support learning log.

Then wait for me to paste the first support message.
Do not send replies or update external systems without approval.

This gives the agent a safe working shape.

When BrowserMan helps

Support often lives inside browser-based tools.

BrowserMan can help when the agent needs to inspect an approved support dashboard, CRM, or customer account page in a real browser session.

Use it for:

  • reading ticket context;
  • checking account status;
  • preparing a draft reply;
  • filling fields for review;
  • collecting screenshots for internal escalation.

Keep high-risk actions behind approval:

The agent may read approved pages and prepare drafts.
The agent may not click Send, refund, delete, suspend, invite, or change billing without explicit approval.

That is the difference between useful support automation and risky automation.

What not to automate

Do not let the agent blindly:

  • send customer replies;
  • issue refunds;
  • change billing plans;
  • delete accounts;
  • reset credentials;
  • promise timelines;
  • diagnose security incidents as resolved;
  • expose private customer data.

The agent should reduce sorting and drafting work.

The human should own customer trust.

Final takeaway

A support triage agent should not try to replace support judgment.

It should make the first pass reliable:

classify → prioritize → extract facts → draft → escalate → wait for approval

OpenClaw and Hermes provide the runtime. ClawMama makes the agent easy to start. BrowserMan can add controlled browser access when support work happens in web tools.

The result is faster support without blind sends.

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