Efficient resource allocation represents a fundamental challenge in modern agricultural operations, where competing demands for water, machinery, labor, and nutrients must be optimized across spatial and temporal dimensions. This research presents a novel multi-agent system (MAS) framework for collaborative farm resource allocation, employing autonomous intelligent agents representing individual resource types, field zones, and operational constraints. The system integrates distributed decision-making algorithms, negotiation protocols, and real-time sensor data to achieve Pareto-optimal resource distribution. Implementation across four commercial mixed-farming operations (total area 1,847 hectares) over two growing seasons demonstrated 23.7% improvement in overall resource utilization efficiency compared to centralized planning approaches. Water consumption decreased by 31.2% through dynamic allocation based on soil moisture gradients and crop phenological stages. Machinery utilization rates improved from 62.4% to 84.7% baseline through intelligent scheduling and conflict resolution mechanisms. The MAS architecture achieved computational scalability with linear complexity O(n) relative to agent population, enabling real-time optimization for farms with 50+ management zones. Economic analysis revealed net benefit of $127 per hectare annually through reduced input costs and improved yields. This research establishes multi-agent systems as viable frameworks for decentralized agricultural management, offering enhanced adaptability, robustness, and efficiency compared to traditional centralized optimization approaches.