Senior SWE-Bench: New benchmark tests AI agents as senior engineers with realistic tasks

Senior SWE-Bench redefines how AI coding agents are assessed by treating them like senior engineers. Unlike traditional benchmarks that provide over-specified requirements, Senior SWE-Bench uses feature tasks with realistic instructions resembling natural language messages. To evaluate these tasks reliably, it introduces a validation agent that writes behavioral tests adapting to submitted solutions. The benchmark also includes bug tasks sourced from PRs that required significant runtime investigation—using logs, profiling data, and reproduction steps—forcing agents to diagnose subtle issues rather than just writing code.

Additionally, Senior SWE-Bench scores agents on tasteful solves by combining runtime correctness tests with multiple quality metrics based on observed codebase practices. It even tests against load-bearing practices that go unstated in instructions. With 6,008 characters and ~39 code symbols in a sample instruction for adding Google Books as a metadata source, the benchmark demonstrates a shift toward realistic, open-ended engineering challenges. This approach promises to better measure an agent's ability to handle ambiguity, debug production-like issues, and ship high-quality code—closing the gap between current evaluation and real-world software engineering expectations.