Cursor fights agent decision flapping

Solving non-deterministic behavior is the biggest hurdle for production-grade coding agents. By treating classifier evaluations as a statistical consistency problem rather than a single-pass test, Cursor is showing how to systematically tame LLM entropy.

• –**Quantifying the gray zone**: A classifier that allows an action 6 times but blocks it 4 times highlights prompt ambiguity rather than a model failure.
• –**Deterministic guardrails**: Setting up structured rules and input validation is proving far more effective than relying on larger models or complex reasoning paths for safety gates.
• –**Flapping as a design signal**: If a policy cannot achieve 100% consensus across repeated runs, it is a clear indicator that human intervention or a stricter sandbox boundary is required.
• –**Structured verification**: Verifying agent states against structured data schemas helps anchor probabilistic LLM outputs into reliable execution logs.