Simon Couch
Simon Couch builds tools that make the work of data science more joyful and effective. As an engineer on the AI Core Team at Posit, his work spans coding assistants, model evaluations, and next-edit-suggestion systems. Drawing on his background in statistics, Simon spent several years authoring and maintaining core packages in the open-source tidymodels framework—like stacks, broom, and infer—before shifting his focus to LLMs. He blogs about his work at simonpcouch.com.
Session
How do we build competent data analysis agents? Data analysis requires a willingness to pause, question conclusions, and dig into subtleties. Frontier LLMs, however, are optimized to push tasks toward completion, not to slow down when something seems off. This tendency works well for coding agents, where success is often verifiable. But for data analysis, verification is more complicated, and autonomous work by the agent can be at odds with the spirit of the discipline. Drawing on our experience building data analysis agents, we'll share evaluations that expose where LLM-driven analysis goes wrong and design patterns that keep analyses correct, transparent, and reproducible.