AI agents read incoming emails, apply structured tags, and route them to the right handler — before urgent requests get buried in a general queue.
At high inbound volume, manual classification stops working. Support queues mix billing complaints with outage reports. Sales inboxes conflate renewals with churn signals. Urgent requests sit for hours because no one triaged them. The problem isn't that your team is slow — it's that classification is a repetitive, high-frequency task that humans aren't built to do at scale. A single missed priority email can cost more than the entire classification system.
MultiMail's inbound processing pipeline delivers each arriving email to your agent via webhook before it enters any human queue. The agent calls check_inbox or receives the push event, reads the full content with read_email, runs its classification logic, and writes structured tags back with set_tags. Downstream systems — ticket routers, Slack integrations, on-call pagers — subscribe to those tags via webhook and act immediately. The agent never sends email in this flow, so no approval gates slow it down. Classification latency is bounded by your inference time, not by human availability.
MultiMail delivers a POST to your configured webhook endpoint the moment an email arrives at your mailbox. The payload includes the email ID, sender, subject, and a preview. Your agent service handles this event rather than polling.
Your agent calls read_email with the email ID from the webhook payload to retrieve the full body, headers, and any attachments. This gives the classifier access to the complete signal — not just the subject line.
Pass the email content to your classification model or LLM. Extract structured fields: intent (support, sales, billing, abuse), urgency (critical, high, normal, low), sentiment (negative, neutral, positive), and topic tags. This step is entirely within your agent — MultiMail places no constraints on how you classify.
Call set_tags with the email ID and your classification results as a tags object. Tags are queryable and filterable across the MultiMail API, making them the source of truth for downstream routing decisions.
Webhook listeners subscribed to tag events receive the classification immediately. Route critical outages to PagerDuty, billing disputes to your finance queue, and positive responses to your CRM — all without human intervention in the classification step.
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.
Classification runs the moment email arrives, not when a human opens the inbox. Critical issues get tagged within seconds of delivery regardless of time zone or staffing level.
An LLM classifier applies the same intent and urgency taxonomy to the ten-thousandth email as it does to the first. Human classifiers drift over time; agents don't.
MultiMail tags are queryable via API and filterable in webhook subscriptions. Every downstream system — ticketing, alerting, CRM — can subscribe to exactly the classification signals it needs without coupling to your agent's internal logic.
Classification is a read-and-tag operation. The monitored oversight mode lets the agent act immediately without waiting for human approval, while still giving your team full visibility into every classification decision via the audit log.
Email volume spikes — product launches, outages, billing cycles — hit classification agents the same as steady state. Queue depth is the only constraint, not human bandwidth.
Email infrastructure built for AI agents. Verifiable identity, graduated oversight, and a hosted MCP server. Formally verified in Lean 4.