Feedback-Driven Improvement is OVADARE’s mechanism for adapting and enhancing its conflict detection and resolution capabilities. By learning from past conflicts and their resolutions, OVADARE continuously evolves to minimize future issues and optimize agent interactions.
Conflict Analysis: Every resolved conflict is logged with details like:
The agents involved.
The type of conflict.
The resolution applied.
Policy Refinement: OVADARE uses logged data to suggest updates to existing policies or the creation of new ones.
Agent Behavior Adjustment: Patterns in conflicts can lead to agent-specific recommendations, such as tweaking their capabilities or communication protocols.
System-Wide Optimization: Feedback is aggregated to improve OVADARE’s detection and resolution algorithms, making the system more efficient.
OVADARE will provide dashboards and tools to visualize logged conflicts and their resolutions. These insights empower developers to take informed actions.