Google Cloud has expanded its Conversational Analytics (CA) offering across more data sources, surfaces, and enterprise controls. BigQuery Conversational Analytics and the Conversational Analytics API are now generally available, following the GA of CA in Looker last year. Conversational Analytics in Databases (AlloyDB, Cloud SQL, Spanner) entered preview. Agents can natively query data in BigQuery, Looker, AlloyDB, Cloud SQL, Spanner, and also Lakehouse Managed Service tables, Apache Iceberg REST catalogs, and federated AWS S3 Unity Catalogs, enabling multi-cloud and multi-database analysis.

Enterprise governance features include Customer Managed Encryption Keys (CMEK), Private IP, VPC controls, Data Residency (DRZ) within the EU and US, and HIPAA compliance. Role-based access controls with parameterized secure views enforce row- and column-level permissions. Administrators can configure native cost controls (max query size in bytes) and monitor fleet health, active users, and query volumes via BigQuery labels, Looker system activity logs, and OpenTelemetry metric exports. Integrated feedback loops allow review of agent traces and user feedback for continuous improvement.

To reduce hallucination, CA agents are co-designed with the underlying data platforms. They leverage Knowledge Catalog for data discovery and automated context enrichment (table joins, descriptions). BigQuery Graphs and Spanner Graphs enable querying structured and unstructured data across multi-hop relationships. Looker’s semantic layer (LookML) grounds responses in centrally governed metric definitions, using ‘Golden Queries’ instead of guessing SQL joins. Agents also use built-in multimodal functions (object tables, ai.search, ai.classify, ai.forecast, ai.detect_anomalies) and ai.key_drivers for automated contribution analysis.

Agentic Workflows, now in preview, shift analytics from reactive question-answering to proactive intelligence. Agents can run multidimensional deep dives analyzing 10–20 contributing factors behind metric changes, schedule automated reporting routines, and trigger streaming anomaly detection when key metrics deviate from thresholds.

Developers can integrate CA via APIs with native SDKs (Node.js, Java, Go, Python, PHP, Ruby, .NET), publish agents to Gemini Enterprise, or embed into custom apps and multi-agent systems using the Agent Development Kit (ADK) and Model Context Protocol (MCP). The post positions CA as unifying data estate, security controls, and developer APIs to deliver proactive insights across enterprise teams.