{"ID":2886965,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.02470","arxiv_id":"2508.02470","title":"AIAP: A No-Code Workflow Builder for Non-Experts with Natural Language and Multi-Agent Collaboration","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.02470","url_pdf":"https://arxiv.org/pdf/2508.02470v1","authors":"[\"Hyunjn An\",\"Yongwon Kim\",\"Wonduk Seo\",\"Joonil Park\",\"Daye Kang\",\"Changhoon Oh\",\"Dokyun Kim\",\"Seunghyun Lee\"]","published":"2025-08-04T14:36:31Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.AI\",\"cs.CL\",\"cs.MA\",\"cs.SE\"]","methods":"[]","has_code":false}