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An approval queue is a deliberate workflow state. An automated process, including an AI agent, reaches a predefined point where a specific proposed action needs a person’s judgment or authority. It pauses there, keeps the pending work intact, shows the reviewer enough to decide, and then continues, rejects, or routes the item under an explicit rule. Everything outside that point runs without stopping. The design problem is less about whether to add a human and more about where the pause belongs, what the reviewer sees, and what happens to the work while it waits.
What an approval queue is, and what it is not
A human checkpoint is a workflow pause with a defined continuation path. The system stops at a known step, sends the decision to a human-facing surface, and resumes or routes the work based on the response. That is different from a modal confirmation bolted onto an agent that otherwise acts freely. A modal appears wherever a developer remembered to add one, has no record of what happened, and usually cannot survive a closed browser tab or a reviewer who is away for a day.
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Google Cloud’s Architecture Center describes the pattern in these terms: “The human-in-the-loop pattern integrates points for human intervention directly into an agent’s workflow.” (Google Cloud Architecture Center, “Choose a design pattern for your agentic AI system.”) The phrase that matters is directly into the workflow. The checkpoint is a state the process can be in, with inputs, outputs, and a next step.
Set the gate by consequence, not by agent
The gate belongs to the action, not to the agent as a whole. Two actions performed by the same agent can deserve different treatment: an internal note that can be edited tomorrow and a message that a customer will read immediately. Start by identifying what the automation proposes to do and what happens if it is wrong, then choose the gate strength.
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A practical spectrum, described in Microsoft’s AI agent runbooks for workflow design, runs from light to heavy:
| Gate strength | Typical fit | What the reviewer does | What happens on the workflow side |
|---|---|---|---|
| Notify | Low-consequence, reversible work | Reads a summary after the fact | Action proceeds; a record is kept for audit |
| Confirm | Moderate-impact actions with a clear yes or no | Approves or rejects one proposed step | Action waits for the answer, then runs or stops |
| Draft and commit | Work that carries organizational voice or numbers | Edits the draft, then commits it | Output stays marked as draft until committed |
| Mandatory qualified review | Regulated or safety-related actions | A named, qualified role reviews and signs off | Nothing proceeds without that role’s recorded decision |
This is a design pattern, not a legal rule. Where regulation applies, the required reviewer and evidence come from the regulation and your compliance function, not from a workflow diagram.
Vendor documentation points the same way. OpenAI’s guidance on guardrails and human review gives examples of sensitive side effects, such as cancellations, edits, shell commands, and other sensitive tool actions, as places where human approval can pause execution. Google Cloud identifies critical actions and subjective judgments as good candidates for a checkpoint. Neither source says every action should be gated. Both treat the gate as something to place deliberately.
Avoid one approval policy for everything. An internal, reversible write and an irreversible, customer-facing action carry different consequences and should not share a rule. Route uncertain cases to review when uncertainty is a real risk signal, and let established low-risk work proceed under monitoring. A gate that fires on every trivial step trains reviewers to click through without reading, which defeats the purpose.
Make each review unit small and inspectable
A reviewer can only exercise judgment on what they can see. A prompt that asks “Does this look right?” is a weak review surface when the output is consequential. A usable review item contains:
- The exact proposed action, stated in the terms the system will execute, such as the record to update, the field to change, or the message to send.
- The evidence it relies on, meaning the relevant source passage, record, or prior case, not a summary of it.
- Confidence, where it is meaningful. Show it only if the score has been validated for this task. An uncalibrated number can mislead more than it helps.
- A legible change, such as tracked edits, a diff, or a before-and-after view.
- The status of the destination record. If the agent writes to a CRM, ticket tracker, or document store, the draft or pending-review state should travel with the record. A label that exists only in a chat thread gets lost, and downstream users may treat an unapproved record as final.
Keep each review unit small enough to check in a minute or two. A bundle of forty unrelated changes invites a single approval of all of them, which transfers responsibility without enabling oversight.
Keep pending work durable while the review waits
The queue has to survive waiting. Human review is often asynchronous, so the process should not hold a live connection or lose its context while someone is in a meeting. OpenAI’s guidance states the requirement directly: “If the review might take time, serialize state, store it, and resume later.”
In practice, the lifecycle of one approval looks like this:
- The workflow reaches a predefined checkpoint and stops before executing the gated action. For an agent tool call, this means returning an approval interruption instead of running the tool.
- The system serializes the run state, including the proposed action, the inputs and evidence, and the position in the workflow, and stores it under a stable identifier.
- The review item is sent to the reviewer through the surface your organization uses, with the artifacts described above.
- The application records the decision: approve, reject, or edit and approve, with the reviewer’s identity and timestamp.
- The system resumes the same run from the saved state, executes or discards the action, and writes the outcome back to the destination record.
Resume the same run. Starting a new user turn with a summary of what was pending loses the exact state the reviewer approved, and a later re-run may propose something different. If a nested agent requests approval, the interruption may surface at the outer run; resolve it there so that the decision attaches to the top-level process that will act on it.
Workflow platforms implement this as a wait step. Elastic documents approval and input wait steps that pause execution and make the response available to later steps. Microsoft Learn describes asynchronous approval workflows built with Power Automate and Microsoft Teams. The mechanics differ by product, but the design questions are the same.
Route to the right reviewer structure
Routing is a choice about authority and independence, not convenience. Two models cover most cases:
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|---|---|---|---|
| Tiered (sequential) | Each level receives the request only after the previous level approves | Authority must pass through successive levels, such as a manager then a finance owner | Added wait time at every level; a stalled first approver blocks everyone after |
| Parallel | Several independent reviewers decide concurrently | Independent judgment is valuable and reviewers can decide without seeing each other’s answers | Partial decisions must be defined; agreement rules need to be explicit |
A confidence threshold can help decide which items reach review. Microsoft Learn’s training on asynchronous approval workflows describes confidence-based escalation as one technique. Treat it as a routing input, not a substitute for consequence analysis. A high-impact action should require review even when the model reports high confidence, because the cost of a wrong action is set by the action, not by the score.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Define rejection, timeout, and partial decisions before launch
Rejection is a workflow outcome, not a dead end. Decide, for each gated action, what happens next:
- Return for edits: the item goes back to the requester or agent with the reviewer’s comments, and the run resumes from the revised proposal.
- Reroute: the item goes to another reviewer or a higher tier.
- Discard: the proposed action is dropped and the record is left unchanged, with the reason logged.
- Notify: a requester or owner is told the action did not proceed.
Timeouts need an explicit policy too. When nobody responds, the options are expiry, escalation, cancellation, or continued waiting. Product defaults are not uniform: Elastic’s documented wait-step defaults differ between steps, and its documentation states that behavior depends on the Elastic Stack version. Verify the timeout behavior of the version you run, then set your own value rather than inheriting one. Batched or multi-approver items also need a rule for partial decisions, such as whether one rejection blocks the whole batch or only the items it names.
Interaction design matters as well. Simple approve-or-reject buttons work for confirmations. A structured form that lets the reviewer correct a field or add context is better for drafts and data changes, because a rejection with no reason gives the system nothing to learn from.
Measure throughput and control together
A queue can fail in two directions. It can slow work until people route around it, or it can let errors through while looking busy. Track both sides:
- Straight-through rate: the share of items that proceed without review, which shows whether the gate is firing only where it should.
- Time in queue: how long items wait, which shows whether reviewers are a bottleneck.
- Reviewer time per item: whether review is cheaper than the manual baseline it replaces.
- Corrections by field or action: what reviewers change, which shows where the agent’s proposals are weakest.
- Rejection rate: how often proposals are refused, and for which reasons.
- Defects found after approval: the cases the gate missed, which is the direct test of whether it works.
Record who changed what and why. Recurring correction types are often the best input for adjusting the gate. Relax a gate only after demonstrated performance over a meaningful period and a deliberate business decision. High-consequence actions should stay gated when the risk warrants it, even if the queue is slow.
What the sources do and do not establish
The guidance above comes from official vendor and cloud documentation and a Microsoft technical runbook that is maintained in a public repository and may change. It is design guidance, not a fixed standard. No source reviewed establishes a benchmark for acceptable queue times, approval rates, or reviewer workload, so set those targets from your own baseline. The product-specific details, including timeout defaults and API behavior, change between releases, so confirm them against the version you deploy before building on them.
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