# How to Implement Human-in-the-Loop for AI Emails

> Source: <https://promptcube3.com/en/threads/2466/>
> Published: 2026-07-23 18:01:33+00:00

# How to Implement Human-in-the-Loop for AI Emails

`approved`

status.This approach transforms the AI workflow from a risky "fire and forget" system into a controlled pipeline: the agent drafts, a human audits/edits, and only then does the code execute the send command.

## The Logic Flow

Instead of a direct call to an SMTP server, the agent follows this sequence:

1. **Draft Generation:** The agent produces the content.

2. **External Holding:** The draft is pushed to a review system (like Impri) via API.

3. **Human Intervention:** A reviewer reads the draft, makes necessary edits to the body or subject, and hits "Approve."

4. **Execution:** The system polls for the decision; if approved, it sends the *final edited version*, not the original AI draft.

## Real-world Implementation: Node.js & TypeScript

Here is a practical tutorial on how to build this approval gate using `nodemailer`

and a review API.

``` python
import nodemailer from "nodemailer";

const IMPRI_KEY = process.env.IMPRI_API_KEY!;
const IMPRI_BASE = "https://api.impri.dev";

async function sendWithApproval(opts: {
 to: string;
 subject: string;
 body: string;
 expiresIn?: number; // seconds; default 3600
}): Promise<"approved" | "rejected" | "expired"> {
 // 1. Push the draft to review
 const push = await fetch(`${IMPRI_BASE}/v1/actions`, {
 method: "POST",
 headers: {
 Authorization: `Bearer ${IMPRI_KEY}`,
 "Content-Type": "application/json",
 },
 body: JSON.stringify({
 kind: "email.send",
 title: `Email to ${opts.to}: ${opts.subject}`,
 preview: {
 format: "markdown",
 body: `**To:** ${opts.to}\n**Subject:** ${opts.subject}\n\n---\n\n${opts.body}`,
 },
 editable: ["preview.body"], 
 expires_in: opts.expiresIn ?? 3600,
 }),
 });

 if (!push.ok) throw new Error(`Push failed: ${push.status}`);
 const { id: actionId } = await push.json();

 // 2. Poll until human decision
 let result: { status: string; decision?: { final_preview?: { body: string } } };
 for (;;) {
 const poll = await fetch(`${IMPRI_BASE}/v1/actions/${actionId}`, {
 headers: { Authorization: `Bearer ${IMPRI_KEY}` },
 });
 result = await poll.json();
 if (result.status !== "pending") break;
 await new Promise((r) => setTimeout(r, 10_000));
 }

 if (result.status !== "approved") {
 return result.status as "rejected" | "expired";
 }

 // 3. Send the human-approved version
 const approvedBody = result.decision!.final_preview!.body;

 const transporter = nodemailer.createTransport({
 host: process.env.SMTP_HOST,
 port: 587,
 auth: { user: process.env.SMTP_USER, pass: process.env.SMTP_PASS },
 });

 await transporter.sendMail({
 from: process.env.SMTP_FROM,
 to: opts.to,
 subject: opts.subject,
 text: approvedBody,
 });

 // 4. Update audit log
 await fetch(`${IMPRI_BASE}/v1/actions/${actionId}/result`, {
 method: "POST",
 headers: {
 Authorization: `Bearer ${IMPRI_KEY}`,
 "Content-Type": "application/json",
 },
 body: JSON.stringify({ status: "success" }),
 });

 return "approved";
}
```

By treating human approval as a mandatory API response rather than a prompt suggestion, you create a fail-safe LLM agent deployment that is actually production-ready.

[Next Mage-Flow: 4B Params vs 32B Giants →](/en/threads/2450/)