How to Use AI to Draft Customer Response Emails

Have AI prepare response drafts for your support team to review and send.

What This Integration Does

This is the "human-in-the-loop" cousin of full auto-reply. Instead of letting AI send messages directly, this workflow watches your support inbox, drafts a reply grounded in your Knowledge Base, and posts the draft somewhere a human can review, edit, and send. Agents go from cold-typing every reply to polishing a near-final draft, which typically halves handle time.

The workflow runs per inbound conversation. For each new message it retrieves Knowledge snippets, drafts a reply with a tool-augmented LLM, and either creates a draft message on the Front conversation or drops a preview into Slack with an "approve" affordance. The original conversation stays unread until a human acts on it, so nothing goes out without review.

Prerequisites

A Front connection (or equivalent inbox connector) with permission to read conversations and post messages.
A Slack connection if you want drafts surfaced in a channel for review.
A Knowledge collection containing your support docs, FAQs, and policy pages.
An AI provider configured for the Agent node.

Step 1: Trigger on New Inbound Message

Drop a Trigger node onto the canvas and set its type to Webhook. Wire Front Rules to POST inbound messages from your support inbox to the webhook. The payload carries conversationId, messageId, sender details, subject, and body.

Step 2: Knowledge - Pull Supporting Docs

Add a Knowledge node in query mode. Point it at your support-docs collection and pass {{ trigger.body }} as the query. Cap results at 5 snippets so the LLM stays focused on the most relevant material.

Step 3: AI Agent - Draft the Response

Add a Connector node configured as an AI Agent. Use a prompt like:

You are a helpful Spojit support agent. Draft a reply to the customer
below using ONLY the knowledge snippets. Be friendly, concise, and
accurate. If the snippets do not answer the question, draft a short
holding reply that says we'll get back to them and flag the gap.

Customer email:
{{ trigger.body }}

Knowledge snippets:
{{ knowledge.results }}

Have the agent return draft (the body) and gap (true if it couldn't answer from the snippets) so reviewers can prioritize the tough ones.

Step 4: Post the Draft on the Conversation

Add a Connector node pointing at the front connector and pick the send-message tool with draft: true (or use your inbox's equivalent draft mode). Set:

conversationId: {{ trigger.conversationId }}
body: {{ agent.draft }}

The draft appears on the conversation alongside the customer's message. An agent can edit and click send when they're ready.

Step 5: Notify Reviewers in Slack

Add a Connector node pointing at the slack connector and call send-message into your support channel. Include the customer's subject, a short preview of the draft, and a link back to the Front conversation. If {{ agent.gap }} == true, prepend a "needs human research" tag so it jumps the queue.

Step 6: Tag the Conversation

Call front add-tag with ai-draft-ready so dashboards and SLA reporting can split "AI-assisted" from "fully manual" replies. This is how you'll measure whether the workflow is actually saving time.

Tips

Always have the AI return short drafts. Reviewers edit ten-line drafts faster than they edit thirty-line ones.
Include the source snippets the agent used in the Slack notification. Reviewers can spot-check accuracy at a glance.
Track edit distance between the AI draft and what actually got sent. If it's huge, your Knowledge collection is underpowered.

Common Pitfalls

Drafts that contradict policy because the Knowledge collection has duplicate or outdated docs. Curate it aggressively.
Letting drafts sit. Add an SLA escalation: if a draft is older than 30 minutes, ping a backup reviewer.
Drafting on noise. Filter out auto-responders and notifications at the trigger so reviewers don't waste cycles on junk drafts.

Testing

Pick three real recent tickets your team already answered. Run them through the workflow and compare the AI draft to what your team actually sent. Adjust the prompt and Knowledge collection until the drafts feel like a 70% starting point. Then enable it on one inbox.