Most organizations run on data operations in the background, continuously producing critical data. Waiting for engineers to build and maintain pipelines doesn't scale and creates brittle dependencies and inconsistencies. Naively deploying AI to take over processing isn't the solution, as it can result in hallucinated insights, untraceable lineage and pipelines that cost 10x more to operate.
In this session, Brian Frutchey, CTO of Clear Fracture, shows how appropriately designed agentic data engineering changes the equation. You'll see how autonomous AI agents can design, build, validate and govern production-grade data pipelines while keeping every decision auditable, every transformation explainable and every cost dramatically lower than chatbot-style AI or manual approaches.
What we'll cover
- Why chatbot-style AI fails at scale
- How agentic data engineering acts as a force multiplier, with real-world metrics showing a 5-10x reduction in pipeline development effort and time
- Maintaining compliance with full auditability and oversight
- A live demonstration of an agent building a production data pipeline