{
  "id": 481,
  "url": "https://arxiv.org/abs/2606.29116v2",
  "title": "Characterizing Large Language Model Agentic Workflows: A Study on N8n Ecosystem",
  "summary": "Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs. LLM agents are LLM systems that use LLMs as a core \"brain\" to reason, plan, and autonomously execute complex, multi-step tasks. In this paper, we present the first large-scale empirical study of LLM agentic workflows in low-code automation platforms. We analyze more than 6,000 publ",
  "authors": "Yutian Tang, Yuming Zhou, Huaming Chen",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-27T23:57:01.000Z",
  "fetched_at": "2026-07-14T14:14:32.649Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/481",
  "original_url": "https://arxiv.org/abs/2606.29116v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}