How a Workflow Library Fixes Claude Code's Data Science Reliability Gaps

AI coding agents like Claude Code can generate data science pipelines quickly, but they struggle with reproducibility and state management across long sessions. Key failure modes include training models on stale cached data, redundant recomputation of expensive steps, and mismanaged file paths — all rooted in the agent's limited context memory rather than coding ability. A dependency-aware workflow library called oryxflow addresses these issues by letting developers declare each pipeline step as a task with explicit dependencies, shifting execution control to the engine. This means the agent no longer needs to mentally track what has been computed or whether intermediate outputs are still valid, as the graph structure handles that automatically. The result is a more reliable run-observe-edit loop where completed steps load from cache and stale intermediates are detected structurally rather than silently ignored.