2026· Innovative Trends in Multidisciplinary Engineering· Vol 01, pp. 01-09· 0 citations
Abstract
This paper proposes a cloud-based artificial intelligence (AI) content and productivity platform that consolidates common language-assistance functions — drafting, summarization, rewriting, and planning — into a single Software-as-a-Service (SaaS) workflow. The key design element is a workflow orchestration layer that classifies user intent, applies task-specific prompt templates, preserves session context, and performs output post-processing to provide consistent, reusable results. A controlled evaluation comparing fragmented use of independent AI tools with use of the unified platform shows the unified workflow reduces average task completion time by 33% and reduces interaction count by 43%, while maintaining comparable output quality. The orchestration router achieves 0.92 intent-routing accuracy on a held-out request set.
Agentic artificial intelligence systems can carry out a broad range of tasks in software engineering and scientific research, from writing and translating code to running workflows for data analysis and visualization. In scientific computing, the difficulty is verifying that agent-generated code is both correct and und...
A. Dhruv, M. Knobbe, P. Machado et al.· 0 citations
This work presents Avatar, an actor-based architecture comprising an orchestrator, an executor, and a provenance monitor, and observes that Avatar's rule mode reproduces native execution, with a single unchanged core running all three.
Suman Raj, H. Nguyen, Haochen Pan et al.· 0 citations
This paper proposes an architecture for a Python-native orchestration platform that borrows the operational role of a traditional RPA "control tower" while adopting the engineering practices of modern workflow-orchestration systems such as Temporal and Prefect, and is presented in four layers — presentation, orchestrat...
Aryanjeet Singh, S. Rathod, Pradnya Suryawanshi· International Journal For Mu...· 0 citations
A controlled empirical evaluation of systems that translate natural-language intents into executable AI workflows across heterogeneous tools and modalities, which showed that dynamic orchestration achieves 80.63% structural similarity to expert workflows, 88% component selection accuracy, and 92% output quality relativ...
Ashraf Elnashar, Jules White, Douglas C. Schmidt et al.· 0 citations
An AI-assisted migration framework that combines the Reference Model of Open Distributed Processing-guided architectural analysis, Large Language Models (LLMs), and Design Structure Matrix (DSM) analysis is presented, demonstrating the feasibility of the proposed approach for modernizing legacy scientific software.
N. Soveizi, S. Tesselaar, Hero Robinson Brouwer et al.· 0 citations
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