How to Auto-Respond to Customer Emails Using AI

Use AI to draft and send helpful responses to common customer questions.

What This Integration Does

Most support inboxes are full of repeat questions: shipping status, password resets, returns policy, opening hours. A human reply is friendly but slow, and the answer is usually already written down somewhere in your help docs. This workflow reads the incoming email, looks up the answer in your Knowledge Base, drafts a reply with a tool-augmented LLM, and (once you trust it) sends the reply automatically.

The workflow is triggered per inbound email. Each run pulls the email body, retrieves the top matching documents from a Knowledge collection, asks the AI Agent to write a reply grounded in those documents, and either sends the reply through front or routes it to a human for approval first. State left behind: an outbound message in Front and a log entry on the conversation so you can audit what the AI replied.

Prerequisites

A Front connection (or another inbox connector) with permission to read conversations and send messages.
A Knowledge collection populated with your FAQ, returns policy, shipping info, and product docs.
An AI provider configured (Vertex AI, OpenAI, Anthropic, etc.) for the Agent node.
A decision on which categories you trust to auto-send vs. require human approval.

Step 1: Trigger on Inbound Email

Drop a Trigger node onto the canvas and set its type to Webhook. Configure Front to POST inbound messages to this webhook URL (Front Rules can fire on a specific inbox or tag). The webhook payload gives you conversationId, messageId, the sender, subject, and body.

Step 2: Knowledge - Retrieve Relevant Docs

Add a Knowledge node in query mode. Point it at the collection you populated and pass {{ trigger.body }} as the query. Limit results to the top 4-6 chunks so the prompt stays focused. The node returns ranked snippets plus source references you can cite in the reply.

Step 3: AI Agent - Draft the Reply

Add a Connector node configured as an AI Agent (tool-augmented LLM). Give it a system prompt like:

You are a customer support agent for ACME. Reply to the customer using ONLY
the knowledge snippets provided. If the snippets do not cover the question,
say "I'll loop in a teammate" and do not invent an answer.

Customer email:
{{ trigger.body }}

Knowledge snippets:
{{ knowledge.results }}

Return a structured object with reply (the message text) and confidence (high/medium/low) so the next step can branch on it.

Step 4: Condition - Auto-Send or Human Review

Add a Condition node that checks {{ agent.confidence }} == "high". On the true branch, route straight to sending. On the false branch, route to a Human node so a teammate can edit the draft before it goes out.

Step 5: Send the Reply via Front

Add a Connector node pointing at the front connector and pick the send-message tool. Configure:

conversationId: {{ trigger.conversationId }}
body: {{ agent.reply }}
type: reply

For approved drafts coming out of the Human branch, send the edited text the reviewer returned instead of the raw agent output.

Step 6: Tag and Notify

After sending, call front add-tag with a tag like ai-auto-replied so you can audit later. For low-confidence handoffs that the human had to rewrite, also call slack send-message into your support channel so the team can spot patterns the AI is missing.

Tips

Start every new category with the Human branch only. Watch a week of drafts, then flip categories you trust to auto-send.
Keep your Knowledge collection trimmed - stale or contradictory docs are the number one cause of wrong replies.
Log the prompt, retrieved snippets, and final reply for every run. When a customer complains, you need the exact context the model saw.

Common Pitfalls

Letting the AI answer outside its knowledge. Without the "do not invent" instruction it will happily make up policy. Keep that guardrail in the prompt.
Forgetting threading. Always include conversationId on the send-message call or the reply lands as a brand new conversation.
Replying to bounces and out-of-office auto-replies. Filter those out at the trigger or with a Condition before the Agent runs.

Testing

Send three real-looking test emails to the inbox: one obviously covered by your docs, one borderline, one totally out of scope. Confirm the in-scope one auto-sends, the borderline one routes to Human, and the out-of-scope one says "I'll loop in a teammate" rather than guessing.