Raindrop

Raindrop is a production monitoring and observability platform built specifically for AI agents. It watches every agent conversation in real time, surfaces issues automatically, and delivers alerts to Slack before users report problems. The company announced a $15M seed round in December 2025, backed by Lightspeed and leading AI companies, and has since shipped major features including Trajectories (agent trace visualization) and Agent Self Diagnostics.

What It Is

Raindrop sits in the observability layer for AI agent applications, filling a gap that traditional evals and logging tools leave open. While evals catch regressions you already know about, Raindrop is designed to surface the unexpected: hallucinations, infinite loops, tool failures, context loss, and refusal spikes that only appear in real production traffic. Teams integrate it with two lines of code and immediately gain visibility across every conversation their agent handles.

How Detection and Alerting Work

Raindrop monitors every agent interaction and groups problems into actionable alerts delivered to Slack. The platform automatically identifies patterns such as:

• Hallucinations — agent citing policies or facts that don't exist
• Infinite loops — agent repeatedly asking for the same information
• Tool failures — API calls failing silently while the agent reports success
• Latency spikes — tool response times deviating from baseline
• Context loss — agent forgetting user-provided details mid-conversation
• Unexpected refusals — valid requests being declined after prompt changes

Each alert links directly to a step-by-step trace showing the full conversation, every tool call, and every decision point.

Custom Monitoring Without Code Changes

Beyond automatic detection, Raindrop lets teams define custom behaviors to monitor using plain language descriptions — no code changes or redeployments required. Engineers can track things like users expressing frustration, agents using filler words, or conversations where users abandoned the session. The platform starts monitoring the described behavior instantly and surfaces trends over time.

Experimentation and Validation

Raindrop includes an experimentation platform that lets teams compare models, prompts, and configurations against real production traffic. The homepage shows an example experiment comparing "Prompt v2.4 vs Baseline" across 2,847 conversations, measuring dimensions like user frustration and deployment issues. This closes the loop between detecting a problem, shipping a fix, and confirming the fix actually worked in production.

Security and Compliance

Raindrop is SOC 2 Type II certified. It includes a PII Guard feature that uses AI models to automatically strip personally identifiable information from data the moment it reaches Raindrop's servers, enabling visibility into agent behavior without exposing sensitive customer data. Custom PII redaction rules and edge-PII redaction are available for enterprise deployments.

Integrations and Audience

Raindrop publishes SDKs for TypeScript, Python, and Go, plus native integrations with Vercel AI SDK, LangChain, OpenAI Agents, Claude Agent SDK, AWS Bedrock, Vertex AI, Pydantic AI, Mastra, and an HTTP API. The platform is positioned for AI engineers, product managers, QA and support teams, engineering leads, founders, and DevOps teams. The homepage attributes endorsements from named individuals at Replit, Framer, Clay, AngelList, and Tolan, among others.

Update: Trajectories and Agent Self Diagnostics (2026)

The blog lists two major feature launches in early 2026: Trajectories (March 2026), a purpose-built agent trace visualization and search tool, and Agent Self Diagnostics (February 2026), which surfaces what the agent itself identifies as going wrong. These releases signal a product direction toward deeper debugging and autonomous issue reporting beyond passive monitoring.