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.
Sara Altman | Simon Couch
Posit | Posit
Agents for Correct, Transparent, and Reproducible Data Analysis
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.