DataFlow-Harness addresses the NL2Pipeline gap by having an LLM agent construct platform-native, editable DAGs through typed incremental mutations instead of generating standalone scripts. It combines procedural DataFlow-Skills, an MCP layer exposing the live operator registry and pipeline state, and a WebUI that synchronizes conversational authoring with visual DAG editing. On a 12-task data-engineering benchmark, it reports a 93.3% observed end-to-end pass rate, 72.5% lower measured monetary cost, and 49.9% lower generation latency than Vanilla Claude Code. Its pass rate was within 0.9 percentage points of a context-aware Claude Code baseline, at 42.8% lower cost.
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