Capture message-originated tasks
A text message AI assistant can turn SMS, iMessage, customer asks, and founder DMs into queue cards with source evidence attached.
Use-case guide
Build an operator queue for a personal AI assistant.
A useful personal AI assistant should not bury completed work in chat. It should prepare reviewable cards: source, interpretation, draft, owner, and status. That queue becomes the place where humans approve agent work before it touches customers or important decisions.
Sourcedraftbrowser checkproof pagereviewownersendresolveSourcedraft
Start with the work that chat loses.
Operator queues are strongest when the assistant touches real-world context: customer messages, browser research, proof requests, renewal notes, personal reminders, and follow-up drafts. The queue gives each item a review state instead of leaving it as another transcript.
Chat asks. Queues review.
Attach browser memory
Computer-use cache keeps repeated account checks and browser sources close to the work.
Generate shareable outputs
AI agent website building turns approved evidence into focused pages.
Keep the execution surface
Super is useful when a personal assistant needs to draft, package, and route work from the same place.
The six-step queue build
The first version should be simple enough to review daily and strict enough that the assistant cannot hide source context.
1. Define eligible inputs
Messages, meeting notes, browser checks, proof requests, support blockers, and follow-up reminders.
2. Require source evidence
Each card needs the strongest quote, link, message, note, or artifact that explains why it exists.
3. Add an interpretation
The assistant should explain the work type: reply, proof, research, escalation, scheduling, or reminder.
4. Draft the output
Prepare the reply, proof page, note, or browser summary before the human opens the queue.
5. Route review
Assign one owner and one clear next action.
6. Expire stale cards
Old work should resolve, refresh, or downgrade so the queue stays trusted.
Queue schema checklist
| Field | Purpose | Minimum default |
|---|---|---|
| Source | Trust. | Message, note, link, screenshot, browser artifact. |
| Interpretation | Review speed. | One sentence explaining what the assistant thinks matters. |
| Draft | Leverage. | Editable reply, proof page, note, or escalation. |
| Status | Lifecycle. | Draft, review, approved, sent, stale, resolved. |
Sources and assumptions
This guide is based on recurring workflows in personal AI agents, message-based assistance, browser-based work, revenue follow-up, proof requests, and human-in-the-loop review. Relevant Super workflows include Super, message-native AI assistance, computer-use cache, and agent-generated web pages.
FAQ
Why not keep everything in chat?
Chat is hard to audit across multiple tasks. A queue gives each item a state, owner, and source trail.
What should the assistant never omit?
The source. If the reviewer cannot see why a card exists, the queue loses trust.
Should the queue send automatically?
For customer-facing or sensitive work, no. Let the assistant draft and let the human approve.
What is the easiest first workflow?
Start with message-originated follow-up because the source is obvious and the value is immediate.