Vector-only RAG architectures break down when AI systems move from retrieval assistants to operational participants. Production-ready agentic AI requires a 'Live Contextual Data Layer' — a persistent, governed, multimodel foundation combining graph, vector, document, key-value, and search indexes. Six architectural requirements are identified: semantic clarity, entity resolution and relationships, temporal awareness, auditability and governance, LLM/agentic integration, and a unified persistence layer. Real-world examples include a SaaS company processing 40,000 support tickets/day and PSI CRO reducing clinical trial site identification from six weeks to minutes. McKinsey data shows fewer than 10% of enterprises have successfully scaled agents, with 80% citing data limitations as the primary blocker.
