VIDEO: The Cursor Moment for Data Science, Context at the Core

Zerve's co-founders, Greg Michaelson and Jason Hillary, demonstrated the superiority of agentic workflows over generic coding assistants by showcasing how Zerve's agent effectively manages exploratory data analysis (EDA), ETL, and modeling tasks through live code execution. Unlike generic tools that struggle with context and rely on guesses, Zerve's agents execute real code in the cloud, track state, and update context with each step, ensuring accurate results without hallucinations. The platform emphasizes the importance of context in data science by connecting data, code, and results, thereby enhancing productivity and learning. Zerve also prioritizes user control and safety with sandboxed execution, read-only permissions, and evaluator checks. The use of a Directed Acyclic Graph (DAG) structure, scheduling, and versioning allows Zerve to transform experimental work into reliable, production-ready runs, particularly benefiting ETL and modeling pipelines.

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