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Contextual Aware Wired Robotic Process Automation Agentic AI System: TenseAI CAW-RP1 Model
Subject area: Science,Engineering and Technology · Area of research: Artificial intelligence
Abstract
The Contextual Aware Wired Robotic Process Automation (CAW-RPA) agentic AI system, embodied in the TenseAI CAW-RP1 model, fuses deterministic RPA ?doer? capabilities with AI ?thinker? functions?namely NLP, ML, and LLMs?to create an autonomous, context-sensitive workflow engine. CAW-RP1 interprets user intent via an LLM/NLP front end, formulates multi-step plans with a reinforcement-learning agent, and executes tasks through RPA bots. A closed-loop feedback mechanism enables continual learning and adaptation. In tests on 100 representative tasks, CAW-RP1 achieved a 98% accuracy rate, 90% task-completion rate, 60% error-recovery rate, and 90% output-quality rating. We compare CAW-RP1 against traditional rule-based RPA and cognitive RPA, highlighting its superior flexibility, autonomy, and ability to handle unstructured data. Finally, we outline future enhancements?multi-agent grouping, advanced learning strategies, and governance features?that will drive the next generation of agentic automation.
References
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How to cite this paper
@article{1709296,
author = {Ayush Maurya},
title = {Contextual Aware Wired Robotic Process Automation Agentic AI System: TenseAI CAW-RP1 Model},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {12},
pages = {1192-1195},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709296.pdf},
abstract = {The Contextual Aware Wired Robotic Process Automation (CAW-RPA) agentic AI system, embodied in the TenseAI CAW-RP1 model, fuses deterministic RPA ?doer? capabilities with AI ?thinker? functions?namely NLP, ML, and LLMs?to create an autonomous, context-sensitive workflow engine. CAW-RP1 interprets user intent via an LLM/NLP front end, formulates multi-step plans with a reinforcement-learning agent, and executes tasks through RPA bots. A closed-loop feedback mechanism enables continual learning and adaptation. In tests on 100 representative tasks, CAW-RP1 achieved a 98% accuracy rate, 90% task-completion rate, 60% error-recovery rate, and 90% output-quality rating. We compare CAW-RP1 against traditional rule-based RPA and cognitive RPA, highlighting its superior flexibility, autonomy, and ability to handle unstructured data. Finally, we outline future enhancements?multi-agent grouping, advanced learning strategies, and governance features?that will drive the next generation of agentic automation.},
month = {June},
}