Raindrop provides real-time monitoring and error tracking for AI agents in production. The platform detects silent failures, tool errors, refusals, and abnormal agent behavior - issues that evaluation frameworks and offline testing often miss. Teams receive alerts when agents fail without signaling, when integrated tools begin erroring, or when agents refuse valid requests, enabling faster detection-to-resolution cycles.
The system emphasizes production visibility across conversation streams rather than post-hoc analysis. Raindrop surfaces abnormal trajectories through visualization and anomaly detection, and supports feature-flag monitoring to verify whether production changes resolve observed issues. This production-first orientation addresses a core operational challenge: engineers often lack visibility into failure modes that don't appear until agents encounter real-world usage patterns.
Raindrop is used by fast-growing AI companies building conversational agents. The team operates from San Francisco as a small, high-velocity group focused on the observability infrastructure required as AI agents scale in production environments.