mlsanityData to 7 Oct 2026

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

arXiv 2508.02470 ↗First posted 4 Aug 2025cs.HC cs.AI cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 45 references that are in the map. First posted on arXiv 4 Aug 2025.

Builds on

11 of 45 references.

References · 11

  1. 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 357
  2. 2023Using an LLM to Help With Code Understandingcited by 222
  3. 2022Do Users Write More Insecure Code with AI Assistants?cited by 186
  4. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  5. 2024Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarioscited by 85
  6. 2024AutoFlow: Automated Workflow Generation for Large Language Model Agentscited by 76
  7. 2023Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfacescited by 71
  8. 2018The Challenge of Crafting Intelligible Intelligencecited by 46
  9. 2023Exploring the Responses of Large Language Models to Beginner Programmers' Help Requestscited by 44
  10. 2023Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chainscited by 24
  11. 2023Jigsaw: Supporting Designers to Prototype Multimodal Applications by Chaining AI Foundation Modelscited by 11

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mappingcited by 2