AI Agents and Assistants
What is an AI agent? A plain-English guide for business owners
What an AI agent is, how it works, where it helps a small business, the risks, and a 7-point check to see if you are ready for one.
Oversight
Agent Review Queue
One queue where every AI output waits, prioritized and assigned, until a reviewer clears it.
AI outputs waiting for a human tend to scatter across inboxes, chat threads and tools, and some wait far too long. Agent Review Queue brings every one into a single queue, ranked by the priority rule you set and assigned to the right reviewer. Response times are tracked against your target, and every decision is logged inside your own systems.
- Every AI output awaiting review collected in one place
- Items prioritized and assigned to the right reviewer automatically
- Response times tracked so nothing sits waiting unseen
$2,400
20–30 working days
Optional maintenance $249/mo
What it is
Agent Review Queue gives every output your AI agents produce one place to wait for a person: prioritized by the rule you set, assigned to the right reviewer, and tracked against a response-time target. It is the queue itself, not the approval logic layered on top.
Why businesses buy it
Human review only works if items actually get reviewed. When outputs are spread across email, Slack and several different tools, urgent ones get buried and reviewers waste time hunting for work. A single queue with clear priority and ownership means the right person sees the right item first, and you can see where reviews are slowing down. The written scope covers one workflow, up to three connected systems and the queue rules before you order, so you know exactly what the build includes.
Problems this solves
AI outputs waiting for review are scattered across inboxes and tools
The review queue gathers outputs from up to three connected systems into one place. Each item waits there until a person picks it up, so nothing is forgotten in a side channel.
Urgent items sit behind routine ones because nothing sets priority
Items are ordered by the priority rule you set, so the most important outputs rise to the top. A single background workflow handles the sorting and assignment for you.
Reviewers assume someone else has picked up an item
Each output is assigned to the right reviewer under your rules. With clear ownership, fewer items fall between people, and managers can see who is holding what.
No one knows how long outputs wait before a person looks
The queue tracks every item against a response-time target you choose. Twenty test runs on your sample data check that priority, assignment and timing work as planned before launch.
A good fit if
AI outputs from more than one workflow currently land in scattered inboxes, spreadsheets or chat threads instead of one place.
You want outputs assigned automatically, by workload, topic or reviewer skill, rather than whoever notices first.
You need a response-time target on each item, with what is overdue visible at a glance.
You want one queue that can later plug into whatever approval rules or escalation logic you add on top of it.
Not the right fit if
You need the AI to stop and wait for a yes before anything happens, not just a well-run queue behind it. See the already-published Human Approval Workflow, which is the approval gate itself.
The problem is really an AI hitting something it cannot resolve, not routine work waiting for review. See Agent Exception Handler, which routes exceptions, not the full output stream.
You want a rubric that scores completed work, not a queue of work still waiting. See Agent Quality Control.
You want to speed up the reviewer's decision itself, with a summary of what to check, not the queue that holds the item first. See AI Approval Assistant.
How it works
- Every output your AI agents produce, across the workflows you connect, lands in one queue instead of scattered inboxes or spreadsheets.
- Each item is prioritized automatically, by the rule you set: age, value, workflow type, or a flag your agents already attach to riskier cases.
- Items are assigned to the right reviewer, by team, topic or workload, so nothing sits waiting for someone who is already at capacity.
- A response-time target runs against every item, so what is overdue is visible on the queue view rather than discovered days later.
- Reviewers see the queue inside the tool they already use, such as Slack, Microsoft Teams, email or your helpdesk, not a separate login.
- Cleared items, and how long each one sat before a person reached it, are logged, giving you a real picture of reviewer load over time.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Your business information (company basics, hours and brand voice), shared during intake.
- The list of systems your outputs currently come from, with admin or API access to each.
- A written description or a screen recording of how review currently happens.
- Five to ten real sample outputs so the queue is built and tested against genuine cases.
- Who should receive alerts when an item is overdue or an error occurs.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- One decision-maker who can approve the queue rules before launch.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake verified and the queue's priority rules agreed with you.
Weeks 1–2
Systems connected and the workflow's twelve steps mapped into the queue.
Week 3
Assignment rules and response-time targets configured.
Weeks 3–4
Twenty test runs on your sample data.
Week 4
Your review and two rounds of changes.
Go-live
Live, recorded handoff session, support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $2,400 |
| Express | 15–25 working days | $2,900 |
| Rush | 10–20 working days | $3,350 |
What you receive at handover
- A review queue running in the background across up to three connected systems
- Priority, assignment and response-time rules built into one workflow of up to twelve steps
- A test report from twenty runs using your own sample data
- An admin guide and the recorded handoff session
- 30 days of support after go-live
Expected benefits and how to measure them
- Reviewers can work from one list instead of hunting across tools.
- Helps urgent outputs get seen ahead of routine ones.
- Makes waiting times visible, so you can staff reviews to match demand.
- Time waiting for review
- Compare how long outputs waited before launch, using message timestamps, with the queue's tracked times after.
- Items missing the target
- Count items that went past the response-time target each week, straight from the queue.
- Outputs never reviewed
- Count outputs that were never looked at in the month before launch and in the month after.
Before you start: Record where AI outputs wait for review today and roughly how long they sit. Note any items that were missed entirely.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is one-time. Running Agent Review Queue afterwards costs roughly $20 to $100 a month in your own accounts, covering the automation platform and AI model usage behind the queue. AI usage during the build and testing is billed the same way, directly to your accounts, so you keep every provider relationship.
Ask about running costsCustom requirements
If your queue rules go past the Scope Card, add a note at checkout. You get a written Change Quote, and that work only starts once you accept it. Your original order runs on its normal timeline either way. Larger or bespoke projects go through the Custom AI request form instead.
Request a custom versionKeep it running
We recommend monthly maintenance, with a monthly allowance of change hours to adjust a priority rule or an assignment as your team changes. It starts after the 30 days of support and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A workflow tool bundles a queue behind a per-seat fee | An agency quotes queue-building after discovery calls | Ours is one fixed price, shown on this page before you buy |
| Scope | A generic queue leaves prioritization and assignment for you to configure | An agency agrees scope during the project | Ours ships with your priority and assignment rules already built in |
| Delivery | A workflow tool still needs real configuration before reviewers trust it | An agency build usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Visibility | A shared inbox does not show what is overdue at a glance | An agency deliverable depends on what was agreed | Ours tracks a response-time target against every item |
Questions about this product
How much does Agent Review Queue cost?
The build is one fixed price, and the panel on this page shows it for each delivery speed. That price covers the full Scope Card: one review workflow of up to twelve steps, up to three connected systems feeding the queue, the priority and assignment rules, and response-time tracking. The automation platform and AI usage behind the queue run in your own accounts afterwards.
How is Agent Review Queue different from Human Approval Workflow?
Agent Review Queue is the queue itself: prioritization, assignment and response-time tracking for every AI output waiting on a person. The already-published Human Approval Workflow is the approval gate layered on top, deciding what the AI may do alone and what always needs sign-off. Many teams eventually run both, but this build ships the queue mechanics on their own.
What does Agent Review Queue cost to run each month?
Running it afterwards costs roughly $20 to $100 a month in your own accounts, covering the automation platform and AI model usage behind the queue. There is no separate license fee on top of that figure. The exact number depends on how many outputs pass through the queue and how often reviewers act on them.
What access does Agent Review Queue need to my systems?
We need admin or API access to each system your outputs currently come from, shared by adding our team as a user or creating a key you control, never by email. A written description or screen recording of your current manual review process, plus five to ten real sample outputs, lets us build assignment and priority rules that reflect genuine cases, not guesses.
Can we change the priority or assignment rules later?
Yes. Adjusting an existing priority rule, or changing who an item type gets assigned to, usually fits inside the monthly change hours that come with maintenance. Adding a whole new workflow beyond the one in scope, or a fourth connected system beyond the three included, goes through a Change Quote agreed before that work starts.
Can we cancel the Agent Review Queue maintenance later?
Yes. Maintenance has no lock-in, so you can cancel any month once the 30 days of included support has ended. Unused hours do not roll over. If your review process changes a lot, bigger changes can go through a Change Quote.
Who owns the queue and its data once it is built?
You do. Agent Review Queue runs inside your own systems and accounts, so the queue, its rules and every logged decision belong to you from day one. If you ever move providers or stop maintenance, the configuration stays in your environment and keeps working; nothing is licensed back to us.
Guides on this topic
All guides Stop losing AI outputs in scattered inboxes.
Order Agent Review Queue today and it goes live on a fixed delivery date. The scope above is exactly what you get, in writing, before you pay.
Works well with
Oversight
Agent Exception Handler
Catches technical failures in AI runs, such as errors, timeouts and unusual runs, and routes them for resolution.
Oversight
Agent Quality Control
Samples and scores output from any of your AI agents against one quality standard.