datasette-agent-charts 0.1a2

The meaningful detail the summary gestures at but doesn't fully land is that exposing SQL beneath AI-generated charts is less about user convenience and more about a structural problem in agentic pipelines: when an LLM constructs a query autonomously, small errors in table joins or filter logic can produce charts that look plausible but are silently wrong. Showing the SQL is a forcing function for catching those errors before they propagate into decisions.

This sits squarely in the same conversation as OpenAI's Codex goal mode feature covered here on May 21, which highlighted the shift toward agents executing multi-step tasks without continuous human oversight. That story raised the implicit question of how humans stay in the loop when agents work autonomously. Willison's approach in datasette-agent-charts is a concrete, small-scale answer to that question: surface the intermediate reasoning (in this case, SQL) so users can audit what the agent actually did. The two stories are not directly connected by any shared platform or organization, but they are working on the same underlying tension between agent autonomy and human verifiability.

Watch whether Willison extends this transparency pattern to other agent outputs in Datasette, such as exposing intermediate reasoning steps or data transformations beyond SQL. If that happens within the next few months, it suggests a deliberate design philosophy rather than a one-off debugging convenience.