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Enterprise data processing environments face increasing operational complexity that exceeds traditional manual management capabilities. Current Big Data platforms rely on reactive operational models that respond to system issues after they impact performance and user experience. This article introduces autonomous agent architectures that transform data platforms into intelligent systems capable of independent perception, reasoning, and action execution. The proposed framework integrates perception layers for comprehensive system monitoring, decision models that balance multiple competing objectives, and action orchestration mechanisms that implement optimizations automatically. Autonomous capabilities enable continuous performance tuning, intelligent failure recovery, dynamic cost optimization, and automated policy enforcement without human intervention. The architecture maintains scalability and fault tolerance characteristics while adding sophisticated reasoning capabilities that adapt to changing operational conditions. Implementation strategies offer practical deployment methods that reduce disruption while gradually adding autonomous features. The operational transformation enables proactive optimization that predicts and prevents issues before they affect system performance. Human-agent collaboration frameworks define effective interaction models that balance oversight with system autonomy. Risk mitigation strategies ensure safe autonomous operation through bounded decision-making and comprehensive safeguards. Performance evaluation metrics demonstrate significant improvements in operational efficiency, cost reduction, and system reliability through autonomous operation.