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For most enterprises, autonomous AI agents still feel like a risk waiting to happen. Andi Gutmans, Vice President and General Manager for Data Cloud at Google, joins Cindi Howson to explain what it takes to build the data foundation that trustworthy agentic AI depends on. He breaks down how organizations can finally activate the 90 percent of enterprise data that's unstructured, why tokenmaxxing is the wrong way to measure AI value, and how open standards like Apache Iceberg are helping leaders tear down fragmented, multi-cloud data silos and unify data across clouds.

Silicon Valley has been measuring AI success by token consumption. The more tokens, the more AI transformation. Right? Wrong. Andi Gutmans, Vice President and General Manager for Data Cloud at Google, joins Cindi Howson on the podcast to share that the best context is the context that drives the outcomes you need with the least amount of tokens and processing. Efficiency, not volume, is where the real value is.

New AI tools are coming out faster than ever, and your employees are working hard to stay at the cutting edge of new technology. Where does governance come into play? Explore how to govern AI without slowing down innovation in the latest episode of The AI Forecast, sponsored by Cloudera.

Ready to eliminate data delays and fuel your AI models with trusted, real-time insights? In this video, we break down how Cloudera Data in Motion allows organizations to unlock the full potential of their data fabric. Whether your corporate datasets are scattered across diverse storage systems, multiple cloud vendors, or on-premises data centers, Cloudera provides the scalable, engine-agnostic data services required to stream and process information instantly—without needing to redesign or refactor your existing pipelines.

This is a comprehensive walkthrough of the metadata extraction process for Cloudera Data Lineage. Learn how to utilize the harvesting agent to set up a new metadata source, such as Informatica Oracle, and perform a local extraction. The video demonstrates how the agent securely reads metadata from databases, ETL tools, and reporting systems, staging it as local XML files to ensure data does not leave the network without explicit action.

In this video, Cloudera’s Dipankar demonstrates how to build an AI agent in Cloudera Agent Studio powered by an open-source Apache Iceberg MCP Server. As a real-world use case, the agent monitors Apache Iceberg table health by analyzing metadata for issues such as small files, partition skew, snapshot history, and other operational signals. Subscribe to stay ahead of the curve with the latest in data strategy, open architectures, and enterprise AI innovations.