MultiMail's inbound webhooks and gated_send oversight let AI agents draft contextual replies that a human reviews before anything reaches the customer.
Generic auto-responders erode trust. A customer emailing about a billing dispute doesn't want 'Thanks for contacting us — we'll respond within 2 business days.' They want an answer. But fully autonomous AI replies carry real risk: hallucinated policy details, wrong account information, or a tone-deaf response to an already-frustrated customer. Most teams are stuck between 'useless template' and 'fully autonomous AI' with no intermediate option that's actually safe to deploy.
MultiMail's inbound webhook delivers the full email to your agent the moment it arrives. The agent loads the complete conversation history via GET /v1/mailboxes/{mailbox_id}/threads/{thread_id}, drafts a reply using your LLM of choice, then queues it via the reply endpoint (POST /v1/mailboxes/{mailbox_id}/reply/{email_id}). When the mailbox is in gated_send mode, the draft is held for review. A human reviewer sees the draft alongside the original thread, approves or rejects it, and MultiMail delivers. The agent handles reading and drafting; the human handles last-mile judgment. As you build confidence in specific email categories, you can shift the mailbox to monitored or autonomous mode without changing your agent's code.
Configure your MultiMail mailbox to POST to your webhook endpoint on every inbound message. The payload includes email_id, thread_id, mailbox_id, sender, subject, and body text. Your agent receives this event and begins processing immediately — no polling required.
The agent calls GET /v1/mailboxes/{mailbox_id}/threads/{thread_id} with the thread_id from the webhook payload. This returns the complete conversation history — all prior messages, timestamps, and directions — so the draft accounts for everything that's already been said and avoids repeating questions already answered.
The agent passes the thread history and incoming message to your LLM with a system prompt that includes your product knowledge and any verified account data you've fetched from your own database. The model generates a reply scoped to what it actually knows.
The agent calls the reply endpoint (POST /v1/mailboxes/{mailbox_id}/reply/{email_id}) with the drafted response. With the mailbox in gated_send mode, the message is held in the pending queue rather than delivered. Reviewers access the queue via GET /v1/oversight/pending and see the original email, full thread, and proposed reply side by side.
The reviewer calls POST /v1/oversight/decide with {email_id, action} where action is approve or reject. Approved messages deliver immediately. Rejected drafts can trigger a revision loop in your agent, which drafts again and queues a fresh reply.
MultiMail fires a delivery event when the message sends. Your agent logs the outcome, sets tags via PUT /v1/mailboxes/{mailbox_id}/emails/{email_id}/tags to mark the thread resolved, and optionally updates your CRM or support system with the resolution details.
Pick your platform, copy the prompt, and paste it to your AI agent — it sets up MultiMail and builds the whole flow. Nothing to fill in.
The agent reads the full conversation thread via GET /v1/mailboxes/{mailbox_id}/threads/{thread_id} before drafting. Replies reference what the customer actually said, not a generic acknowledgment that ignores the question entirely.
gated_send mode holds every draft in the pending queue. Your team reviews the agent's work and approves, edits, or rejects before anything reaches the customer. You get drafting speed without losing control over what goes out.
Start with the mailbox in gated_send. As you build confidence in specific email types — shipping status, password reset instructions, plan upgrade confirmations — route those categories to a mailbox set to monitored or autonomous. Oversight mode is a per-mailbox setting, so dedicating a mailbox per trust tier lets you mix modes without changing your agent's code.
Every draft, every approval decision, and every delivery event is logged. You can query the full history of any thread, see exactly what the agent drafted, and review what a human changed before approving. This matters for support quality audits.
MultiMail handles inbound routing, thread stitching, and delivery. Your agent can use any model for the drafting step — Claude, GPT-4o, Gemini, or a fine-tuned model trained on your historical support data. The API does not care which LLM you use.
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