Structured AI data pipelines score 10.9 points below free-form code — DataFlow-Harness closes the gap

If you ask an AI coding agent to write a standalone Python script to parse a single JSON file, it will likely give you a perfect answer in seconds. But the same agent often breaks if you ask it to build a systematic data processing pipeline, like ingesting thousands of messy documents, chunking text, scoring quality, and filtering noise for a Retrieval-Augmented Generation (RAG) system that fits your specific enterprise stack. While large language models (LLMs) excel at one-off code generation, their outputs for complex data-processing tasks are typically free-form, disposable scripts. These s