Social media has amplified public discourse in India while perpetuating caste-based hierarchies. Despite legal protections, caste-based hate speech continues to propagate across digital platforms through culturally embedded expressions that conventional classifiers often struggle to interpret. We propose GYAAN-SAHIT, a knowledge-driven multi-agent framework that addresses this problem through structured debate-based classification. Each agent adopts a distinct ideological and socio-cultural persona, engaging in multi-turn argumentation to reason over context, subtext, and intent. A critic agent then evaluates the coherence of the debate before producing the final classification. The framework further integrates Hindi hate lexicons to ground its reasoning in linguistic and cultural specificity. Experiments show that GYAAN-SAHIT achieves improvement in performance while generating culturally grounded explanations, demonstrating the effectiveness of persona-based multi-agent reasoning for hate speech detection in low-resource and socially complex environments.