AI Agents' Confident Errors Stem from Bad Data Engineering, Not Context

Summary

AI agents often provide confidently wrong answers not due to poor context retrieval, but because of underlying data engineering issues where outdated or incomplete data is still considered authoritative. This problem, common in enterprise AI, stems from monitoring pipelines for job completion rather than data correctness, leading to invisible failures that require robust data observability solutions.