AIAP: A No-Code Workflow Builder for Non-Experts with Natural Language and Multi-Agent Collaboration

August 04, 2025 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Hyunjn An, Yongwon Kim, Wonduk Seo, Joonil Park, Daye Kang, Changhoon Oh, Dokyun Kim, Seunghyun Lee arXiv ID 2508.02470 Category cs.HC: Human-Computer Interaction Cross-listed cs.AI, cs.CL, cs.MA, cs.SE Citations 0 Venue arXiv.org Last Checked 4 months ago
Abstract
While many tools are available for designing AI, non-experts still face challenges in clearly expressing their intent and managing system complexity. We introduce AIAP, a no-code platform that integrates natural language input with visual workflows. AIAP leverages a coordinated multi-agent system to decompose ambiguous user instructions into modular, actionable steps, hidden from users behind a unified interface. A user study involving 32 participants showed that AIAP's AI-generated suggestions, modular workflows, and automatic identification of data, actions, and context significantly improved participants' ability to develop services intuitively. These findings highlight that natural language-based visual programming significantly reduces barriers and enhances user experience in AI service design.
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