Google is shaking up the enterprise data landscape with its newly announced Agentic Data Cloud, a move that could redefine how businesses interact with their data. At Cloud Next, Google unveiled this new architecture designed for AI agents to take action, not just humans to ask questions. This shift from reactive intelligence to systems of action is crucial as AI becomes more autonomous in business operations.
What Google’s Agentic Data Cloud Does
The Agentic Data Cloud introduces three key components: the Knowledge Catalog, Cross-cloud Lakehouse, and Data Agent Kit. The Knowledge Catalog automates the semantic metadata curation process, eliminating the need for manual data steward intervention. This means data engineering teams can scale their operations across the entire data estate, covering platforms like BigQuery and Cloud SQL, and federate with third-party catalogs such as Collibra and Atlan.
The Cross-cloud Lakehouse enables BigQuery to query Iceberg tables on AWS S3 without egress fees, leveraging a private network. This approach allows businesses to access and utilize AI capabilities across third-party datasets seamlessly. The Data Agent Kit transforms how data engineers work by allowing them to describe outcomes rather than write pipelines. This means less time coding and more time focusing on results.
Competitive Context and Market Landscape
Google’s move is part of a broader industry trend where semantic context is becoming critical infrastructure. Competitors like Databricks and Snowflake are also emphasizing semantic layers with their Unity Catalog and Cortex offerings, respectively. Microsoft Fabric is in the mix too, focusing on business intelligence and agent grounding.
The market is aligned on the importance of semantics, but there’s a divergence in how companies approach building and maintaining these models. Google is banking on openness and federation with third-party semantic models, setting itself apart by not requiring customers to rebuild from scratch.
Implications for Founders, Engineers, and the Industry
For enterprises, the implications are clear. If your data catalog is still manually curated, it won’t scale to meet the demands of agent workloads. The shift to storage-based federation, using open standards like Iceberg, is crucial to avoid hidden egress costs that can become a tax on agentic AI.
Data engineers should prepare for a transition from writing pipelines to focusing on outcome-based orchestration. Those who adapt early will likely have a competitive advantage as the industry moves in this direction.
What Happens Next
As Google and its competitors push forward, enterprises must evaluate their current data infrastructure and prepare for an agent-driven future. This evolution is not just about staying current; it’s about ensuring that your data strategy can support the next wave of AI-driven business operations. The landscape is shifting, and those who adapt will lead the charge into this new paradigm. For more details, visit Google Cloud’s official page.
