Efficient Implementation of AI Agents in Enterprise Application Integration (EAI) and Electronic Data Interchange (EDI)
DOI:
https://doi.org/10.32628/CSEIT251112397Keywords:
Agentic AI, AI Agents, Multi-Agent Architecture, AI Orchestrator, Explainable AI, Enterprise Application Integration, EDI, Micro language model, Large language model, Data lakeAbstract
Contemporary enterprises increasingly contend with the multifaceted challenge of integrating diverse applications and managing large-scale data flows. Although foundational, traditional Enterprise Application Integration (EAI) and Electronic Data Interchange (EDI) solutions often fail to address the real-time processing, intelligent automation, and dynamic adaptation demanded by today’s digital landscape. The study investigates the incorporation of Artificial Intelligence agents into EAI and EDI ecosystems to surmount these limitations. We posit that the autonomous, reactive, proactive, and social attributes intrinsic to AI agents render them particularly adept at advancing integration capabilities. This research introduces an innovative agent-based architectural framework focusing on sophisticated routing, dynamic data transformation, and automated workflow orchestration. The proposed system leverages machine learning algorithms for adaptive data mapping and employs reinforcement learning to optimize agent collaboration and resource allocation in the existing EAI/EDI infrastructure. Empirical evaluations, comprising simulations and case analyses, reveal pronounced improvements in key performance metrics such as transaction throughput, latency reduction, and error minimization relative to traditional integration methodologies. This whitepaper expounds on the strategies and methodologies integral to the effective deployment and operationalization of Agentic AI within EAI and EDI contexts. It delineates the architectural refinements crucial for AI-driven transformation and proposes deployment frameworks designed to maximize scalability and resilience through AI orchestrators. By addressing the inherent complexities and offering tailored solutions, this study provides actionable insights for enterprises seeking to harness autonomous AI to bolster efficiency, curb operational costs, and unlock new pathways for innovation.
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