Agentic Workflows

Scientific visualization has long been caught in a bind: the more complex the dataset, the more domain-specific the visualization, and the harder it is to automate. From MRI scans to hurricane simulations, modern scientific data is massive, high-dimensional, and notoriously messy. While dashboards and 2D plots have benefitted from LLM-driven automation, 3D volumetric visualization—especially in high-performance computing (HPC) settings—has remained stubbornly manual. VizGenie changes that. Developed at Los Alamos National Laboratory, VizGenie is a hybrid agentic system that doesn’t just automate visualization tasks—it refines itself through them. It blends traditional visualization tools (like VTK) with dynamically generated Python modules and augments this with vision-language models fine-tuned on domain-specific images. The result: a system that can answer questions like “highlight the tissue boundaries” and actually improve its answers over time. ...

“Every technical revolution rewires the old system—but does it fry the whole board or just swap out the chips?” The enterprise tech stack is bracing for another seismic shift. At the heart of it lies a crucial question: Can today’s emerging AI models—agentic, modular, stream-driven—peacefully integrate with yesterday’s deterministic data flows, or will they inevitably upend them? The Legacy Backbone: Rigid Yet Reliable Enterprise data architecture is built on linear pipelines: extract, transform, load (ETL); batch jobs; pre-defined triggers. These pipelines are optimized for reliability, auditability, and control. Every data flow is modeled like a supply chain: predictable, slow-moving, and deeply interconnected with compliance and governance layers. ...