From data for humans to data for agents

For the past decade, the modern data stack was designed for a single customer: the human analyst. Automated pipelines and cloud warehouses made it easier to move and analyze data, enabling teams to query information and guide decisions more efficiently.

But this model was never designed for systems that operate in milliseconds, execute transactions autonomously, and depend on a real-time, high-fidelity view of the business.

As AI agents emerge as the new class of data consumer, the traditional foundation is cracking. Unlike an analyst who can wait for a two-hour sync delay or tolerate a slightly inconsistent naming convention, AI agents will be deployed at scale and embedded in time-sensitive, automated business operations that depend on speed and clear context. If your CRM data is out of sync with your ERP, the agent makes the wrong decision.

This is because an agent’s intelligence is only as good as the freshness of its data. Where we once optimized for 24-hour batch cycles to fuel dashboards, agents must take action in real time. While a human can use intuition to "fill in the gaps" of inconsistent data, an agent has little tolerance for ambiguity; without clear context, it fails. 

Consider a retail assistant agent managing a dynamic shopping experience. It cannot rely on inventory levels from four hours ago and needs a live view of shipping delays to keep a customer accurately informed. 

We are moving away from static tables designed for historical "look-backs" and toward live context designed for autonomous action. For agents, data consistency isn't a "nice-to-have" metric; it is the boundary of their reasoning capability.

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A new foundation for data in the AI era

“AI agents are fundamentally changing how businesses operate, but their intelligence is entirely dependent on the data that feeds them. By pairing Fivetran’s real-time ingestion with Google Cloud's open data ecosystem, we are giving agents the live, trusted context they need to move beyond historical analytics and start driving autonomous business actions with confidence.”
— Naveen Punjabi, Director, Analytics & AI ISV Partnerships, Google Cloud

This is why Fivetran’s collaboration with Google Cloud has evolved to deliver Open Data Infrastructure (ODI) as the foundation for AI. ODI is an architecture grounded in open standards that gives organizations control over their data, costs, and shared context. Instead of duplicating data across different architectures or moving it into systems that tightly couple storage and compute, ODI creates a unified foundation that remains open, consistent, and usable wherever it is needed.

By leveraging Fivetran to land data directly into Apache Iceberg via Google Cloud’s Lakehouse and BigQuery, joint customers can make full use of all data, providing a unified, interoperable layer where Fivetran manages the automated, high-frequency CDC and Google Cloud provides the intelligence. This approach effectively future-proofs the data estate and enables downstream modifications, without the need to re-platform.

The real value comes from how this foundation integrates with Gemini and the Gemini Enterprise Agent Platform. When Fivetran ingests data into open formats, it becomes immediately accessible for Retrieval-Augmented Generation (RAG). Instead of manually building complex pipelines to update vector databases, data flows directly from operational systems like Salesforce, SAP, or PostgreSQL into the environment where Vector Search on Gemini Enterprise Agent Platform can index it. This creates a Live Context for your agents, grounding Gemini’s reasoning in the most current enterprise data without the latency of traditional batch processing.

ODI also addresses one of the primary challenges in scaling AI: governance. As usage grows from hundreds of queries to thousands of automated decisions, the risk of a fragile data stack increases. This is where Knowledge Catalog becomes essential, providing the structure and guardrails agents need to operate reliably. 

By using Knowledge Catalog to organize Fivetran-ingested assets into governed data products, you ensure that agents operate with full context. They can access business logic, sensitive data labels, and quality guarantees directly, rather than relying on raw rows alone. Knowledge Catalog provides the semantic guardrails that prevent an agent from pulling data from a deprecated sandbox instead of a certified financial mart. 

By centralizing this flow through Google Cloud’s governed data layer, you can maintain a single source of truth that is both open and secure. This creates a shared, real-time memory for your AI agents, helping to ensure that every automated decision is based on governed, fresh, and high-fidelity information.

This marks a shift from reactive data pipelines to an always-on data environment. When your infrastructure is built on open standards and automated ingestion, the gap between raw data and autonomous action disappears. We are moving toward a world where the data stack empowers agents to decide what happens next.

Ready to see how this works in practice? Book a demo.

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