AI coding tools produce wildly different outputs based on their harness configuration, but few teams have time to benchmark thoroughly.
Create a standardized test suite that runs coding agents through realistic scenarios with different harness settings. Generate comparative metrics and recommendations.
Engineering managers at tech companies would pay for actionable insights to optimize their setups.
Start with Python/Rust/JavaScript test cases and basic metrics before expanding.
The challenge is keeping benchmarks relevant as models rapidly evolve.