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.
Citations
IRE Journals:
Ayush Maurya "Contextual Aware Wired Robotic Process Automation Agentic AI System: TenseAI CAW-RP1 Model" Iconic Research And Engineering Journals Volume 8 Issue 12 2025 Page 1192-1195
IEEE:
Ayush Maurya
"Contextual Aware Wired Robotic Process Automation Agentic AI System: TenseAI CAW-RP1 Model" Iconic Research And Engineering Journals, 8(12)
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