arXiv:2606.29116v1 Announce Type: new Abstract: 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 publicly available n8n workflows and examine four aspect
The proliferation of LLMs and the increasing demand for automation in business processes are converging, leading to the rapid adoption and development of agentic workflows in low-code/no-code platforms.
This study provides a foundational empirical understanding of how LLM agents are being deployed in real-world automation, offering insights into their current capabilities and future potential for transforming white-collar work.
The ability of non-expert users to design complex, autonomous workflows significantly lowers the barrier to entry for advanced automation, shifting software development and task execution paradigms.
- · Low-code/no-code platforms
- · Businesses adopting LLM agents
- · AI agent developers
- · Productivity software providers
- · Traditional enterprise software vendors
- · Routine white-collar job roles
- · Consulting firms specializing in manual process automation
Widespread integration of LLM agents will automate repetitive office tasks, increasing operational efficiency across many industries.
This efficiency gain will lead to significant changes in workforce composition, necessitating reskilling and upskilling for many roles, and creating demand for new types of human-AI collaboration.
The democratization of complex automation through LLM agents could lead to unforeseen business model innovations and potentially exacerbate existing digital divides if access remains uneven.
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Read at arXiv cs.AI