OpenAI’s data productivity team built Kepler, an AI agent that understands their data platform context and writes iterative SQL to answer complex data questions. It handles table discovery, query refinement, follow-up prompts and even visualizations, cutting analysis time and reducing errors.

• OpenAI built Kepler, an internal AI agent that writes iterative SQL to answer data questions across 70,000 datasets and 600 petabytes daily, deployed via Slack, IDEs, or web UI
• It doesn't just query once—it discovers tables, refines CTEs, verifies numbers against dashboards/Notion, and proposes hypotheses (e.g., duplicate logging) for anomalies like a WAU spike
• Memory for self-learning plus rigorous evals let it improve over time without regressing
• Rolled out to 80% of the company, turning multi-engineer, multi-hour investigations into minutes

Hex built a suite of analytical evals to test data-analysis models and found Claude Fable 5 outperforms its Opus 4.x predecessors by 10–15%, nailing both semantically modeled and raw-data tasks with fewer mistakes. They’ve also designed a tougher “Frontier” benchmark for long-horizon, open-ended scenarios, where Fable 5’s careful assumptions and cross-checks boost its pass rate to around 58%.

• Claude Fable 5 beats Opus 4.7 by 10-15 points on Hex's core benchmarks, scoring 93%+ on Analytical Hard/Semantically Modeled tests and 65% on Semantically Unmodeled tasks, versus prior Opus versions' single-digit gains
• Fable's advantage comes from following a "golden workflow" (starting in the semantic layer, cross-checking raw SQL) and transparently stating assumptions, which lets it catch errors like a cents-for-dollars mistake that Opus misses
• On Hex's new "Frontier" benchmark for long-horizon, open-ended tasks, Fable at Max Effort hits 58% pass rate, notably outperforming other setups