How to Review AI Output Without Becoming the Bottleneck

When a team first starts using AI in operations, the review process is usually informal. Someone runs the AI, looks at the output, decides it is good enough, and sends it on. This works for one or two runs a day. It breaks when the volume increases.

Informal review has three failure modes at scale. The first is invisibility: nobody can see how many reviews are pending, which ones are overdue, or who is responsible for which decision. The second is inconsistency: different reviewers apply different standards, and there is no record of what was approved or why. The third is concentration: all the reviews flow to the same one or two people, who become the rate-limiting step for the entire operation.

Each of these failure modes is a structural problem, not a people problem. The reviewers are not too slow or too inconsistent: the process gives them no structure to be fast or consistent within. Adding more AI capability to an operation with informal review does not solve the problem. It makes it worse: more AI output means more review demand, and the informal process buckles faster.

The solution is to make review a first-class step in the workflow, not an afterthought. That means giving every review a defined assignee, a payload containing the relevant context, a set of possible decisions, a deadline, and a record of what was decided. DAVE's task system provides all five.

Every time a running workflow instance reaches a Human Review, User Interaction, Agent Review, or Safety Check node, it pauses and generates a task. That task appears in the Task Inbox of the assigned user. The workflow does not advance until the task is completed. This is the structural guarantee that no AI output bypasses human review: the workflow is literally blocked until the reviewer acts.

To access the Task Inbox, navigate to Tasks in the sidebar. The page displays the heading Task Inbox with a list of all tasks assigned to you or visible to your role. The task table shows five columns:

• Type. The task type: user_interaction, human_review, agent_review, or safety_check. Click the type link to open the task detail page.
• Status. A color-coded badge: amber for pending, blue for in_progress, green for completed, red for failed, and grey for cancelled.
• Instance. A truncated ID linking to the parent workflow instance, so you can trace any task back to the run that generated it.
• Assignee. The user ID of the person responsible, or a dash if the task is unassigned.
• Due. The task's deadline, or a dash if no deadline is set.

Use the status filter dropdown in the top-right corner to narrow the list. Filter to pending to see only the tasks that need your attention right now. Filter to completed to review past decisions. The filter options are: All Statuses, pending, in_progress, completed, failed, and cancelled.

The Task Inbox is available to users with the Admin or Use role. Users with the Create, Curate, or Reporting role do not have access to the Task Inbox and cannot complete tasks. If a team member who needs to review AI output cannot see the Tasks sidebar item, their role assignment needs to be updated by an Admin.

The task detail page is designed to give a reviewer everything they need to make a decision without leaving the platform or consulting a separate document. Click any task's Type link in the Task Inbox to open it.

At the top of the detail page, you see the task type as the page heading, the task ID in monospace text, and a status badge. Below the heading, three metadata cards show the parent instance ID, the assignee, and the due date. Then comes the most important section: the Payload.

The Payload is a JSON object passed from the workflow to the task. It contains the data the reviewer needs to make a decision: the AI-generated content, the original brief, the context from earlier steps in the workflow, or any other information the workflow designer chose to include. Reading the payload carefully before clicking a decision button is the single most important habit for a reviewer who wants to be consistent.

Once you have reviewed the payload, the decision panel at the bottom of the page presents three options:

• Approve (green button). The content meets requirements and the workflow should proceed on the approved path. Click this when the AI output is good enough to move forward.
• Reject (red button). The content is unacceptable and the workflow should not continue on the current path. Click this when the output needs to be discarded or the process needs to restart.
• Request Changes (outlined button). The content needs revision before it can be approved. Click this when the output is close but not ready, and the workflow should route back for revision.

Before clicking any decision button, you can add an optional Comment in the text area above the buttons. The comment is recorded alongside your decision in the task's Submitted Result. For Reject and Request Changes decisions, a comment is especially important: it tells the workflow owner and any downstream users what was wrong and what needs to change. A reviewer who leaves comments consistently is a reviewer who creates a self-documenting quality record, not just a series of approve/reject clicks.

After you click a decision button, the task moves to completed status and the workflow instance resumes from the node that generated the task, following the outgoing edge that corresponds to your decision. If the workflow has separate paths for Approve, Reject, and Request Changes, your choice determines which path the instance takes next.

The most common bottleneck pattern in AI review operations is the single expert reviewer. All the review tasks flow to one person because they are the only one trusted to make the call. When that person is unavailable, the queue backs up. When the volume grows, they cannot keep up. The operation scales to the capacity of one person.

DAVE's task system provides the structural tools to distribute review without losing quality control. Here is how to use them.

Assign tasks to the right role, not the right person. The workflow node configuration determines who receives a task. Assigning tasks to a specific user by name creates a single point of failure. Designing workflows so that tasks are assigned based on role or team membership means any qualified reviewer can pick up the work. When one reviewer is unavailable, another can step in without any workflow reconfiguration.

Use due dates to surface priority. The Due column in the Task Inbox is visible to every reviewer. Tasks approaching their deadline are visible without any manual chasing. Reviewers can filter to pending tasks and sort by due date to work in priority order. Tasks that time out move to failed status, which is visible to Admins and workflow owners as a signal that the review capacity is insufficient for the volume.

Use the status filter to manage the queue. A reviewer who filters the Task Inbox to pending sees only the tasks that need action. A reviewer who filters to in_progress sees tasks that have been opened but not yet completed. The filter is a lightweight queue management tool that requires no additional tooling.

Use comments to build a shared quality standard. When reviewers leave comments explaining their decisions, the comment record becomes a shared reference for what good looks like. New reviewers can read past decisions and comments to calibrate their own judgment. The quality standard becomes explicit and transferable rather than implicit and locked in one person's head.

Use Agent Review nodes to automate the first pass. For high-volume review scenarios, an Agent Review node can run an automated quality check before the task reaches a human reviewer. The agent evaluates the AI output against defined criteria and returns a pass or fail. Only outputs that fail the automated check, or that require a human judgment call, reach the human review queue. This reduces the volume of human review tasks without removing the human checkpoint for the cases that need it.

Every task completion in DAVE is logged in the audit trail: the decision made, the user who made it, and the submission data (with sensitive fields redacted). Assignment changes are also logged. This audit record serves two functions that are relevant to review quality at scale.

The first function is accountability. When a piece of AI output that was approved causes a problem downstream, the audit log shows who approved it and when. This is not primarily a blame mechanism: it is a feedback mechanism. If a particular reviewer is consistently approving outputs that cause downstream problems, the audit record makes that pattern visible. If a particular type of task is consistently being rejected, the audit record makes that pattern visible too. Both patterns are signals that something in the workflow or the agent configuration needs attention.

The second function is compliance. Task completion events support SOC 2 CC6.1 (logical access) and GDPR Article 30 (processing records) when those compliance modes are active on the tenant. For organisations operating under regulatory requirements, the task audit trail is part of the evidence base that demonstrates human oversight of AI-generated outputs. The record is created automatically, without any additional configuration, as a consequence of using the task system.

A review operation that runs through DAVE's task system is not just faster and more consistent than an informal review process. It is also auditable by design. The audit trail is not a bolt-on: it is a property of every task completion, on every workflow, in every tenant.