What data infrastructure do agents need?

AI agents often fail not due to weak models but because of inadequate data infrastructure that was not designed for autonomous decision-making, especially as they transition from simple chatbots to more complex systems executing multi-step business processes. Traditional AI infrastructures, which are suitable for batch model training and human analytics, become bottlenecks in agentic workflows due to issues with data freshness, latency, and semantic consistency. Autonomous agents require an adaptive data infrastructure that integrates real-time data ingestion with centralized semantic layers and standardized interfaces to ensure accuracy and reliability in executing tasks. Key challenges in implementing such infrastructure include the need for sub-minute data freshness, diverse access patterns, and semantic grounding to prevent operational failures caused by context vacuums, lineage blind spots, metric drift, and integration overhead. To address these, a five-layer agent data architecture is proposed, consisting of an ingestion layer for real-time data capture, a processing layer for rapid data retrieval, a semantic layer for machine readability, a validation layer for automated quality checks, and an interface layer for standardized access control. The dbt platform is highlighted as a tool that can help build this infrastructure by providing a governed and semantically consistent data foundation, ensuring reliable agent operation in production environments.

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