AI agents fail at data engineering, not the context layer

AI agents fail in production not because of bad context but because of stale, unvalidated data upstream. Teams chasing better retrieval architectures or context-layer vendor products treat the symptom while missing the underlying data engineering problem. Retrieval pipelines score relevance or availability, not correctness, so stale or missing data passes through unnoticed. The fix involves correctness, freshness, consistency, and lineage—core data engineering practices exemplified by Uber's Unified Data Quality platform and Netflix's lineage system.