{"id":1994,"date":"2026-09-23T19:45:29","date_gmt":"2026-09-23T19:45:29","guid":{"rendered":"https:\/\/datadecoded.com\/mcr\/?post_type=seminar&p=1994"},"modified":"2026-09-30T22:57:07","modified_gmt":"2026-09-30T22:57:07","slug":"who-operates-the-data-stack-when-the-agent-builds-it","status":"publish","type":"seminar","link":"https:\/\/datadecoded.com\/mcr\/seminars\/who-operates-the-data-stack-when-the-agent-builds-it\/","title":{"rendered":"Who Operates the Data Stack When the Agent Builds It?"},"content":{"rendered":"

What if your next data platform is a conversation instead of a pile of dashboards and config screens? I’m not talking about a simple agent that does SQL queries.<\/p>\n

Today agents can do more than that: look at raw files, design a schema, deploy infrastructure, ingest the data, write transformations, run quality checks, answer questions, and publish results and even discard infrastructure at the end. So what are we actually building: a data agent sitting on top of an existing stack, or a complete data stack?<\/p>\n

And who operates that data stack?\u00a0 Data teams have aspired to give their business stakeholders self-serve analytics for a decade.\u00a0 Historically, this has been through dashboards and metrics pre-built by the data team.\u00a0 How is the agentic analytics different?<\/p>\n

In this talk, we\u2019ll build a working analytics stack from scratch and explore how we can empower new audiences: both people and machines.<\/b><\/p>\n

Key Takeaways:<\/b><\/p>\n