Data Fabric: Querying agent traces in BigQuery

Arize's Data Fabric, integrated with Google BigQuery, enables organizations running AI agents in production to seamlessly query and analyze agent traces alongside billing, infrastructure, and customer data by synchronizing these traces to open Apache Iceberg tables. This integration addresses the challenge of disjointed data systems by allowing engineering and data teams to treat AI agent telemetry as structured warehouse data, facilitating SQL-based analysis without the need for custom pipelines or data export. Arize's approach leverages open standards, ensuring no vendor lock-in, and supports efficient querying through schema fidelity and partitioning. The system combines Arize's purpose-built adb OLAP engine with Data Fabric to provide low-latency debugging and a continuously updated record in the warehouse, enabling insights into cost drivers, performance issues, and resource allocation. By joining agent decision data with operational and business datasets, organizations can derive actionable insights that inform both immediate engineering decisions and broader business strategies, though challenges such as data modeling and attribution remain.

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