DataFlow-Harness: A Platform for Editable LLM Data Pipelines
DataFlow-Harness is a platform that enables large language model (LLM) agents to construct editable, platform-native data pipelines as directed acyclic graphs (DAGs), rather than generating free-form scripts. On a 12-task data-engineering benchmark, it achieved a 93.3% end-to-end pass rate, while reducing monetary cost by 72.5% and latency by 49.9% compared to Vanilla Claude Code. The platform's approach maintains reliability close to script-generation baselines but with significantly improved efficiency.
Why it matters: This work demonstrates a practical advance in LLM-driven workflow automation, enabling persistent and editable pipeline artifacts with high reliability and substantially lower cost and latency.
Full story at: arXiv Software Engineering ↗