<![CDATA[Contemporary business process automation faces a critical anomaly: conventional Robotic Process Automation (RPA) frameworks often experience fatal runtime crashes when encountering minor structural variations in enterprise documents. This instability triggers severe operational bottlenecks and exponentially increases manual intervention costs. To resolve this fragility, this study proposes and evaluates a novel AI Agentic Workflow architecture based on Multi-Agent Reinforcement Learning. The dynamic system orchestrates specialized sub-agents, specifically Planning, Tool-Execution, and Self-Reflection agents, to enable autonomous fault tolerance and contextual reasoning without relying on rigid scripts. Experimental testing was conducted across standard, structural drift, and ambiguous input scenarios using 5,000 synthetic and real-world supply chain documents. The empirical results demonstrate that the multi-agent architecture drastically outperforms legacy RPA and single-prompt Large Language Models, achieving a 98.9% Task Success Rate under severe structural drift. Furthermore, the system recorded a 99.2% Error Self-Correction Efficiency, successfully resolving runtime faults autonomously without human triage. Although the cognitive reasoning loops introduced an increased mean latency of 19.8 seconds, the computational trade-off is justified by the elimination of technical debt. Ultimately, this framework ensures highly adaptive enterprise resource planning ecosystems, safeguarding business continuity against unpredictable data fluctuations and volatile external integrations.]]>
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