The new Agentic Data Cloud helps make sense of your data.

It’s a massive shift in how we think about storage. Historically, data clouds were essentially “passive libraries”—you put data in, and humans (or static dashboards) went in to find it.

The Agentic Data Cloud, specifically the one Google Cloud just unveiled at Next ’26, flips that. It’s designed to be a “System of Action” rather than just a system of record. Instead of waiting for a person to run a query, the architecture is built so AI agents can autonomously reason over, navigate, and act on that data.

Here are the three biggest pillars making that happen:

1. The Knowledge Catalog

This is the evolution of the old Dataplex. It doesn’t just list where your tables are; it uses a semantic layer to understand business meaning.

• Context for Agents: If an agent is tasked with “reducing churn,” it needs to know exactly which column defines a “customer” and what “churn” means in your specific business logic.

• Continuous Enrichment: It tags unstructured data (like PDFs and images) and infers missing structures, so agents aren’t “flying blind” when they hit messy datasets.

2. Model Context Protocol (MCP) Support

Google and Snowflake have both leaned heavily into MCP. This is a secure, universal interface that lets an agent “talk” to different data engines (BigQuery, Spanner, etc.) without needing a custom integration for every single one. It treats your data assets as tools that the agent can discover and use on the fly.

3. The “Borderless” Lakehouse

One of the coolest technical hurdles they’ve cleared is the cross-cloud latency problem. The Agentic Data Cloud is built to be a borderless lakehouse using things like the Apache Iceberg REST Catalog. This allows for “zero-copy” access—meaning an agent can work across Google Cloud, AWS, and Snowflake simultaneously without the data ever being physically moved or siloed.

The big takeaway: We’re moving from “Data as a Resource” to “Data as Context.” If you’re building agents, the quality of their work is now entirely dependent on how well this data layer can explain itself to them.

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