8am☕Coffee

Raindrop introduces Experiments to determine whether updating AI agents will help or hurt performance, offering a structured way to compare versions so teams can see the net effect of changes before rolling them out broadly. The tool enables controlled evaluations against defined metrics and guardrails, surfaces regressions that might be masked by anecdotal wins, and supports incremental deployment decisions, giving organizations a repeatable method to improve agents without sacrificing reliability or user experience.