The Chase-Catch Gap in Enterprise AI Agents

This article examines the 'chase-catch gap' in enterprise AI agents—the significant distance between a quick, impressive demo and a reliable production system. It argues that the primary barrier isn't model capability but data and knowledge problems: production agents require reliable data access, consistent business definitions, governance, observability, cost controls, and human oversight. The article defines the gap, explains why pilot projects stall, introduces a 'knowledge layer' bridging LLM reasoning and enterprise data, offers a scorecard for agent-readiness, and shows how lakehouse architecture can help narrow the gap. It emphasizes that no shortcut exists for organizational readiness.