Measuring What Matters with Jules- Google Developers Blog

Introduces a new methodology for evaluating proactive AI coding agents by leveraging historical bug-fixing data to establish 'ground truth' for 'aspirational goals'. Details a preliminary benchmark using 705 bugs from internal Google codebases to cluster related bugs, set them as targets, and test an agent's ability to identify insights within an exploration budget. Presents preliminary results showing the agent's effectiveness in identifying relevant insights and the benefit of increased exploration budgets. Outlines plans to expand the evaluation to public GitHub data and ingest richer context streams.