arXiv:2604.19667v2 Announce Type: replace-cross Abstract: At present, executable visual workflows have emerged as a mainstream paradigm in real-world industrial deployments, offering strong reliability and controllability. However, in current practice, such workflows are almost entirely constructed through manual engineering: developers must carefully design workflows, write prompts for each step, and repeatedly revise the logic as requirements evolve -- making development costly, time-consuming, and error-prone. To study whether large language models can automate this multi-round interaction
The proliferation of advanced large language models (LLMs) has enabled new research into automating complex, multi-step tasks previously requiring significant human oversight, making 'Chat2Workflow' a timely benchmark.
This benchmark indicates a significant step towards automating elaborate software and operational workflows using natural language, potentially collapsing numerous white-collar tasks and increasing enterprise efficiency.
The development and deployment of complex visual workflows, previously a manual and time-consuming engineering effort, could become highly automated and accessible through natural language interfaces.
- · AI developers
- · Enterprise software companies
- · Businesses adopting AI agents
- · Knowledge workers capable of directing AI workflows
- · Routine workflow engineers
- · Consulting firms focused on manual workflow design
- · Companies slow to adopt AI automation
The immediate consequence is a reduction in the time and cost associated with building and maintaining executable visual workflows.
Plausible second-order consequences include accelerated digital transformation across industries and a shift in demand for highly skilled workflow automation specialists.
A speculative third-order consequence is the rise of highly adaptable, AI-driven 'meta-platforms' that can assemble and manage entire operational ecosystems with minimal human intervention.
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Read at arXiv cs.LG