Abstract

Nowadays, the use of AI has become essential for enhancing adaptability and efficiency. This study contributes to this objective by proposing an agentic AI system based on a Low Code and No Code (LCNC) approach for personalised intelligent workflow management. The developed agentic AI integrates Large Language Models (LLMs) with personalised diary tools using a low-code development platform, enabling intelligent workflow management and task creation through visual interfaces. The system consolidates user tasks from multiple platforms into a single interface. Leveraging LLM-based reasoning capabilities, it detects scheduling conflicts, evaluates contextual factors such as urgency and priority, and autonomously selects appropriate actions without requiring manual intervention. The use of a LCNC platform simplifies system development and improves accessibility for non-technical users, bridging the gap between personalised user needs and technical execution. Preliminary results indicate that the proposed system can accurately handle diverse scheduling scenarios and make human-like decisions in real-world contexts.

Download

Share

COinS