Designing Agent-Assisted Abuse Detection With Classifier Routing and Feedback Loops
AI agents can investigate abuse, but reliable detection requires smart routing, classifier feedback loops, audit trails, and strict tool permissions.
Designing agent-assisted abuse detection systems involves careful routing of suspicious entities to the agent and structuring the agent's output for effective use. The first step is routing entities based on classifier scores and novelty scores, rather than sending every suspicious entity directly to the agent. Known abusive entities can be handled by fast classifiers, while novel entities are sent to the agent for investigation.
The agent's output should be structured and contain useful features for training a classifier, rather than just a plain text report. This allows the classifier to learn from the agent's findings and improve its performance over time.
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