2025/01/22 by Vaibhav Tupe, Tupe, Vaibhav, Shrinath Thube +1 · 4 citations
Business, Management and Accounting · Decision Sciences · #Artificial Intelligence (cs.AI) #Collaboration in agile enterprises #D.2.11 #D.2.12 #FOS: Computer and information sciences #I.2.0 #I.2.11 #K.6.5 #Scientific Computing and Data Management #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2502.17443
openalex publication_date 2025/01/22 · openalex created_date 2025/10/15 · openalex updated_date 2026/07/28
The rapid advancement of Generative AI has catalyzed the emergence of autonomous AI agents, presenting unprecedented challenges for enterprise computing infrastructures. Current enterprise API architectures are predominantly designed for human-driven, predefined interaction patterns, rendering them ill-equipped to support intelligent agents' dynamic, goal-oriented behaviors. This research systematically examines the architectural adaptations for enterprise APIs to support AI agentic workflows effectively. Through a comprehensive analysis of existing API design paradigms, agent interaction models, and emerging technological constraints, the paper develops a strategic framework for API transformation. The study employs a mixed-method approach, combining theoretical modeling, comparative analysis, and exploratory design principles to address critical challenges in standardization, performance, and intelligent interaction. The proposed research contributes a conceptual model for next-generation enterprise APIs that can seamlessly integrate with autonomous AI agent ecosystems, offering significant implications for future enterprise computing architectures.