Core Role and Functional Details of Agent Inbox
As LLManager's human-computer interaction hub, Agent Inbox takes on three main roles:
1. Authorization Decision Centre
- Visualization: Presenting AI-generated approval recommendations and complete reasoning chain
- operation panel: Provide three-state operation buttons for approval/modification/rejection
- historical retrospect:: Correlation shows the disposition of similar historical cases
2. Model training interface
- learning with fewer samples: Manual modification results are automatically deposited into the training sample library
- Reflection Trigger: Activate the plan_reflection or full_reflection process with different modifications.
- Evaluation Kanban: Demonstrate the analysis of differences between model predictions and manual decision making
3. Process configuration terminals
- Assistant management: Supports binding/switching of helper instances for different approval scenarios
- Rule adjustments: ApprovalCriteria and rejectionCriteria can be updated in real time.
- Model switching: change the underlying LLM without restarting the service
Typical interaction process: users access the service via dev.agentinbox.ai → view AI suggestions → refer to similar cases → select the type of operation (fill in the reason for change when modification is required) → the system automatically triggers the subsequent learning mechanism.
This answer comes from the articleLLManager: a management tool that combines intelligent automated process approvals with human reviewsThe
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