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AI Agentic workflows and Enterprise APIs: Adapting API architectures for the age of AI agents

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arxiv 2502.17443 v1 pith:GP6LKU2M submitted 2025-01-22 cs.SE cs.AI

Vaibhav Tupe , Shrinath Thube This is my paper · ORCID
classification cs.SEcs.AI
keywords enterpriseagentsapisarchitecturesinteractionagentagenticanalysis
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Agentic AI for a New Paradigm in Business Process Development

    cs.AI 2025-07 reject novelty 4.0 of 10

    Business processes can be modeled as goal-driven agent teams where goals, objects, and agents replace fixed task sequences, and workflows emerge from trigger objects.

  2. Leveraging Large Language Model for Intelligent Log Processing and Autonomous Debugging in Cloud AI Platforms

    cs.AI 2025-06 reject novelty 4.0 of 10

    A cloud log debugging framework combining log clustering, LLM reasoning, and reinforcement-learning recovery planning is claimed to improve fault location accuracy by 16.2 percent, but the supporting accuracy experime...

  3. Agentic Web: Weaving the Next Web with AI Agents

    cs.AI 2025-07 conditional novelty 3.0 of 10

    A position paper defines the Agentic Web as the next web era and proposes a three-dimensional conceptual framework for understanding and building it.

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