Your agents can only act with confidence when the data behind them is current, governed, and traceable. However, AI agents do not stop working to reconcile conflicting metrics.
They do not question stale data, fill gaps in business context, or wait for a pipeline to recover. They act, and that makes the data foundation behind them a production requirement.
DBTA’s recent webinar, How To Build the Data Foundation Your AI Agents Can Trust, with Eric Carr, lead partner sales engineer at Fivetran + dbt Labs and Alex Hauer, principal product marketing manager at Fivetran + dbt Labs, who discussed how to build a trusted foundation that serves analytics, operations, and AI from the same current, governed data.
According to Carr and Hauer, your data stack was built for humans, but agents are the new primary data consumers. You must ask yourself this series of questions: Do you trust your data infrastructure? Is data governed, accurate, and traceable? Is it portable, and built on open standards? Can it handle agent costs, at scale?
Today’s data stack breaks at scale, which results in unreliable agents, no ROI, and runaway costs.
Carr and Hauer said, a trusted data infrastructure consists of the following:
- Flexible by design: Embrace Iceberg and Delta Lake formats for portability across any compute, cloud, LLM.
- Fresh and accurate: Access all your data across any source, in any format, at any volume—easily.
- Trusted by default: Agents inherit documented metrics, end-to-end lineage, data contracts, and auditable history.
- Efficient at scale: Unit costs deflate as volume scales.
Fivetran + dbt Labs offers this kind of trusted infrastructure, they explained. The company provides flexible storage plus compute, reliable data movement, rich metadata, and scalable infrastructure.
Fivetran helps you turn unstructured data into rich context for agentic AI, Carr and Hauer noted.
For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.