Cursor tunes agent harness, trims token burn

This is the real moat in AI coding tools now: not just which model you ship, but how aggressively you shape the harness around it. Cursor is basically saying the editor is becoming an operating system for agent behavior.

• –Cursor combines public benchmarks with real-world experiments, which is the right answer to eval gaming and benchmark overfitting
• –Per-model tool formats and prompts matter more than most teams admit; matching the model’s training style saves reasoning tokens and reduces mistakes
• –The log-monitoring automation loop is a practical self-healing system for agent regressions, not just a metrics dashboard
• –Mid-chat model switching remains expensive and messy, which is why the article quietly recommends sticking with one model unless you need to switch
• –The multi-agent framing matters: harness orchestration is becoming the layer that determines how useful frontier models actually are in production