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Agents with {\\em malicious intent} may contribute harmful content that appears benign to evade content-based moderation, while compromising the system through exploitative and malicious behavior manifested across their overall interaction patterns within the community. To address this, we introduce \\textsc{\\textbf{Bot-Mod}} (\\textsc{\\textbf{Bot-Mod}}eration), a moderation framework that grounds detection in agent intent rather than traditional content level signals. \\method{} identifies the underlying intent by engaging with the target agent in a multi-turn exchange guided by Gibbs-based sampling over candidate intent hypotheses. This progressively narrows the space of plausible agent objectives to identify the underlying behavior. 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