Google Cloud Launches AI Agent Observability and Governance in BigQuery and Looker

As enterprises move AI agents from experimental prototypes into production, data and platform teams are discovering that deploying an agent is only half the battle—you also need to see how it reasons, which data it touches, and what it costs to run. On November 14, 2026, Google Cloud announced expanded AI agent observability and evaluation capabilities across its Google Data Cloud, providing unified visibility into first-party data agents in BigQuery and Looker, as well as custom third-party agents built with frameworks like Agent Development Kit (ADK) or LangGraph.

The core of this update is a unified telemetry framework designed to address three operational challenges: tracing multi-step reasoning while protecting sensitive data, controlling token consumption and query spend, and evaluating answer accuracy before prompt or schema changes reach users. For first-party agents, Conversational Analytics agents emit telemetry automatically; for custom agents, developers can stream structured telemetry into BigQuery via single-line SDK instrumentation. All data lands in customer-owned BigQuery tables, ensuring data sovereignty.

In terms of privacy protection, Google Cloud has introduced a 'fail-closed' mechanism. When a knowledge source uses row-level security, column-level policy tags, or dynamic data masking, the system automatically skips logging to prevent sensitive content leakage. Meanwhile, administrators can enable Cloud Monitoring and Cloud Trace to aggregate metrics and trace steps, but these operations exclude core content such as user prompts and model responses by default. If deeper auditing is required, agent owners can opt in to conversation logging on a per-agent basis, inheriting dataset IAM permissions and Customer-Managed Encryption Keys (CMEK).