Capture the informal channel first
A text message AI assistant catches buyer asks, founder DMs, iMessage notes, and urgent customer details before they become invisible context.
Use-case guide
Build a revenue review queue with AI agents.
A revenue review queue is a daily surface where personal AI agents prepare source-backed actions for a human to approve: proof replies, renewal escalations, support follow-ups, and account research. The queue works because it puts evidence and action on the same card.
Message signalsource trailbrowser checkproof roomownerreviewsendresolveMessage signalsource trail
Start with inputs that already create follow-up pain.
Do not begin with every revenue metric. Begin with the signals that operators already chase manually: customer asks in messages, meeting follow-ups, proof requests, renewal concerns, support blockers, and browser research that gets repeated every week.
Review starts with source evidence.
Cache repeated checks
Computer-use cache helps the agent remember account context and browser research.
Package proof
AI agent website building turns approved evidence into a shareable proof page.
Keep execution close
Super keeps drafting, packaging, and review near the operator.
The six-step build
A good first version is simple: one queue, a few trusted inputs, explicit owners, and a review rule that keeps the agent from sending customer-facing claims without approval.
1. Define eligible signals
Use messages, meetings, support tickets, CRM notes, proof requests, usage flags, and browser checks.
2. Require source evidence
Every queue item needs a quote, note, link, screenshot, or browser artifact.
3. Classify the action
Proof reply, renewal risk, support escalation, pricing answer, implementation follow-up, or internal handoff.
4. Draft the response
The agent prepares an editable message, proof page, or account note.
5. Route to an owner
One person should approve, edit, send, or close the item.
6. Expire stale work
Items should resolve, refresh, or downgrade before the queue becomes noise.
Queue schema checklist
| Field | Purpose | Default |
|---|---|---|
| Source | Trust and review speed. | Quote, message, ticket, meeting note, or browser artifact. |
| Action type | Routing. | Proof, renewal, support, pricing, implementation, handoff. |
| Draft output | Operator leverage. | Email, SMS, proof room, internal note, escalation. |
| Status | Lifecycle. | Draft, review, approved, sent, stale, resolved. |
Sources and assumptions
This guide is based on recurring workflows in revenue operations, customer success, sales follow-up, founder-led selling, customer proof requests, and personal AI agent execution. Relevant Super workflows include Super, message-native AI assistance, computer-use cache, and agent-generated web pages.
FAQ
What should go into the first queue?
Start with proof requests and renewal risks because both need evidence, owner review, and timely follow-up.
Should the agent send automatically?
Keep human review for customer-facing claims. Let the agent gather, draft, and package.
How often should teams review it?
Daily for small teams and twice daily for high-touch sales or renewal motion.
What makes it better than a task list?
The source evidence and draft output are already attached, so review is faster than starting from scratch.