HiveReview quality and handle exceptions
Reduce supervision through evidence, not optimism.
Make review concrete
A reviewer should be able to explain why the output is correct. Check the source information, required format, missing facts, and any action suggested by the AI employee.
For maintenance triage, that means checking urgency, the proposed next step, and whether the AI employee missed a safety escalation. For a report, check calculations and trace the claims to their sources.
Correct the process
When the AI employee makes an error, describe the rule it should have followed and show an example. Determine whether the problem was missing context, stale data, an unclear task, or inadequate access.
Do not keep repeating a prompt that cannot solve a missing connection or contradictory procedure.
Keep important decisions with people
Customer commitments, financial changes, sensitive disclosures, and irreversible actions need a deliberate authorization process. Use provider restrictions and supported approval controls. A sentence in a prompt is not a complete security boundary.
Reduce review gradually
The intended Hive model lets AI employees earn more autonomy as their work becomes dependable. Automatic changes to supervision based on quality scores are not assumed to be implemented in the prototype.
For now, record the team's decision about what can run without review, what still needs approval, and what must stop for help. Revisit that decision when inputs, tools, or responsibilities change.
When the result is uncertain
Stop the affected action, preserve the evidence, and ask the responsible person. “Needs attention” should explain the decision needed rather than merely report that the AI employee is stuck.