Automatic learning sounds useful until it isn't. 

A tool that updates its own understanding without anyone signing off can drift in ways that are hard to catch until the wrong answer has already informed a real decision.

Teela's Admin Learning Review Queue puts a human in the loop before anything changes. 

Every learning candidate: whether surfaced by user feedback or triggered by usage signals, lands in the queue for an admin to approve or dismiss. 

Nothing takes effect until someone with the right access says it should.

The queue is split into two views: improvements pending review and those that have already been auto-approved based on strong positive signal thresholds. 

Admins can see the full history of what was approved, what was dismissed, and when, giving complete visibility into how Teela's understanding has evolved.

What this means for your team:

• No silent updates: every change to how Teela understands your data is logged
• Admins control what gets approved and what gets dismissed
• Full audit trail on all signals, approvals, and dismissals
• Auto-approval available for high-confidence improvements, with admin override always available
• Trust in your answers stays intact as the system grows

A system that learns on its own is only useful if you can trust what it learned. 

The Review Queue is how Teela keeps that trust intact.

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