The data leader's primer for agentic AI

Agentic AI doesn't just answer questions, it takes action. That raises the stakes on your data. This primer lays out what it actually takes to deploy AI agents that work: fresh, centralized, governed data, and the infrastructure to support it.

Why this matters

Gartner projects more than 40% of agentic AI projects will be canceled by the end of 2027. The usual cause isn't the model. It's data that's stale, siloed, or missing the context an agent needs to act correctly. This guide shows how to close that gap.

What you'll learn

• Why agentic AI puts far greater demands on your data infrastructure than traditional analytics or reporting
• How lineage, semantic layers, and governance turn agentic AI into an auditable, reliable workflow
• Where agentic AI works best, and where it doesn't: the task characteristics that predict success or failure
• How Fivetran and dbt Labs use their own AI agents internally, including real examples from product ops and customer support
• A 7-step checklist for going from idea to a governed, production-ready agent

Download the guide and start building AI agents on data you trust.