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Drafting quality email replies is one of the most time-consuming tasks in customer service. Each response requires reading the incoming message, understanding the issue, checking customer history, referencing the right policy, and writing in a consistent brand voice. Even experienced agents spend 5-10 minutes per reply. Multiply that across hundreds of daily emails and you have a team that spends more time writing than solving. This use case gives your support team an agent that reads incoming emails, pulls relevant customer history and policy information, and drafts a reply ready for human review. The agent follows your brand voice guidelines from the knowledge space and flags any email that requires escalation instead of a standard response.

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What you need

Data sources

  • Helpdesk system (Zendesk, Freshdesk, or similar) — Ticket records with email threads, status, and assigned agent
  • CRM system — Customer account details, contract status, and purchase history

Knowledge spaces

  • Brand voice guidelines — Upload your tone guide, approved phrases, and language to avoid
  • Email templates — Standard response templates for common issue types (billing, shipping, returns, technical)
  • Refund and credit policies — Upload your current policies so the agent can reference them accurately
Semantic layer: Define these in your ontology before setting up the agent.
ComponentNameDefinition
ObjectTicketMaps to the helpdesk ticket table. Represents a customer issue with status, category, and full conversation thread
ObjectCustomerMaps to the CRM account table. Represents a customer with contact info, account tier, and history
MetricFirst Response TimeMinutes between ticket creation and the first agent reply
MetricCustomer Lifetime ValueTotal revenue from a customer’s account, used to prioritize high-value customers
DimensionIssue TypeCategorizes tickets as billing, technical, shipping, returns, account, or general inquiry
DimensionCustomer TierSegments customers by account level: enterprise, business, standard
See building a semantic layer for a step-by-step guide.

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Agent setup

1

Create the agent

Go to Agent Space > New agent.
FieldValue
NameSupport Rep
RoleCustomer service specialist
GoalResolve customer issues quickly and empathetically
2

Set the description

You are a customer support specialist. Always greet the customer by name when available. Use a warm, professional tone. Acknowledge the customer’s frustration before jumping to a solution. Keep responses concise — no more than three paragraphs. If you cannot resolve an issue, escalate clearly and explain what happens next. Never promise refunds or credits without confirming the policy first.
3

Scope data access

Grant access to:
  • Helpdesk system data source (ticket records and email threads)
  • CRM system data source (customer accounts and purchase history)
  • Brand voice guidelines knowledge space
  • Email templates knowledge space
  • Refund and credit policies knowledge space
  • Ticket and Customer objects in the semantic layer
4

Add skills

Trigger: User asks to draft a reply or a new support email arrives
  1. Read the incoming email and identify the issue type (billing, technical, shipping, returns, account, general inquiry).
  2. Check the customer’s history in the CRM for prior interactions, open tickets, and account tier.
  3. Pull the relevant email template from the knowledge space based on the issue type.
  4. Draft a reply that acknowledges the issue, provides a clear next step, and sets expectations for resolution time.
  5. Match the company’s tone guidelines from the brand voice knowledge space: professional, empathetic, concise.
  6. If the issue requires internal action (refund, escalation, engineering ticket), note it separately as an internal comment.
  7. Present the draft for review before sending.
Trigger: Customer requests a refund or credit
  1. Identify the customer and order from the email or ticket context.
  2. Pull the order details from the CRM, including purchase date, amount, and item.
  3. Check the refund policy from the knowledge space for the applicable time window and conditions.
  4. Determine eligibility: within policy (approve), outside policy (deny with reason), or edge case (escalate).
  5. Draft the appropriate response, citing the specific policy clause.

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Automation

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Playbook: Auto-draft for new emails

1

Set the trigger

Set the playbook to trigger on a new inbound email event from the helpdesk system.
2

Build the workflow

The playbook reads the incoming email, classifies it, pulls context, and generates a draft reply saved back to the ticket.
  1. Query step — Pull the email content and any existing ticket context from the helpdesk.
  2. Query step — Look up the customer in the CRM by email address to get account details and history.
  3. AI step — Classify the issue type and generate a draft reply following the brand voice guidelines.
  4. Condition step — If the issue type is “legal” or “data privacy,” skip the draft and route directly to the legal queue.
  5. Action step — Save the draft reply as an internal note on the ticket for agent review.
3

Configure delivery

The draft is saved directly to the helpdesk ticket. Send a Slack notification to the assigned agent’s channel letting them know a draft is ready for review.
4

Test and activate

Click Run now to test with live data, then toggle to Active.

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What’s next

Ticket classification

Automatically categorize and prioritize tickets the moment they arrive.

All customer service use cases

See the full list.