The monitoring platform for AI agents. It traces every production run and catches the failures that never throw an error - hallucinations, loops, refusals, broken tools.
Nothing is on fire. The server is up, the latency graph is flat, the error rate reads a comfortable zero. By every dashboard you own, the night is uneventful. And yet somewhere in production an AI agent just invented a refund policy that does not exist, looped on a tool call forty times, and sent a user away confused. No exception was thrown. No pager went off. The customer simply left, and took the story with them.
This is the failure mode nobody built dashboards for. Traditional monitoring was designed for software that breaks loudly - a 500, a stack trace, a crash you can grep. AI agents break politely. They hallucinate, they refuse, they drift off-script, and they do all of it while returning a perfectly valid HTTP 200. Raindrop exists to make those silent failures loud.
Based in San Francisco and often described in one tidy phrase - "Sentry for AI agents" - Raindrop is an applied AI research company building the observability layer for a generation of software that is non-deterministic by design. It records what your agents actually did, decides what went wrong, and hands your engineers the specific run that broke.
“Raindrop is doing for AI what Sentry did for web apps - except the stakes now include hallucinations, refusals, and misaligned intent.”
Raindrop turns the fog of production AI into a working loop: capture every run, surface the issues automatically, then test a fix against live traffic before you trust it.
Capture every message, tool call, retry, and error from production, then replay the exact run that misbehaved instead of guessing from logs.
Automatically flags silent failures - hallucinations, infinite loops, broken tools, refusals - so you learn about them before your users do.
Small custom models tuned to the shape of your product. Watch "User Frustration" or define your own, like "Agent Stuck in a Loop," across millions of events.
Semantic search over massive production datasets - find the one pattern that matters, not just the log line that happened to match.
Agent-native A/B testing. Run a candidate fix against real traffic and show, with data, that it actually worked.
Server-side redaction keeps sensitive data out of view, while a Triage Agent lets your team investigate incidents straight from Slack.
The point of a custom signal is simple: the metrics that matter for your agent are ones only you can name. Below is an illustrative view of incident rates a team might track. Figures are indicative, for explanation only.
Previously co-founder and CEO of Opyn, an early DeFi options platform later acquired by Coinbase.
Also a co-founder of Opyn. A second-time founder building monitoring for software that behaves probabilistically.
Worked on visionOS at Apple and avionics software at SpaceX - places where silent failure was never an option.
The founders' argument is blunt: the tools most teams use to measure AI were built for chatbots. A chatbot answers a question and stops. A modern agent picks up thousands of tools, runs for minutes or hours, and makes a long chain of decisions where any single link can quietly bend the outcome. You cannot score that with a one-shot benchmark.
Raindrop's answer is to train small, custom models to the exact shape of each customer's product rather than lean on one generic classifier. That specialization is the whole idea - it is why the platform can see a behavior as specific as "UI Aesthetic Complaints" and track how often it happens across a river of events. Specialization beats scale when the terrain keeps changing.
Investors noticed. The $15M seed was led by Lightspeed, with checks from Figma Ventures, Vercel Ventures, Y Combinator, and a roster of operators who run AI products themselves: the founders of Replit, Notion, Framer, Cognition, and Speak. When the people shipping agents put money into the company that watches agents, it reads less like a bet and more like buying insurance.
“The intelligence behind intelligence.”
Raindrop watches production agents for a growing list of AI-first companies - more than 50 teams in all.
Return to that quiet 2 a.m. The graphs are still flat and the error rate still reads zero. But now, in a Slack channel, a message arrives before the customer ever hits send on their complaint: an agent invented a refund policy, looped on a tool, and left a user frustrated - here is the exact run, here is the signal that caught it, here is the incident rate over the last thousand conversations.
The failure did not get quieter. The room got a way to hear it. That is the whole shift Raindrop is chasing: not fewer bugs in a demo, but a feedback loop where production behavior surfaces the problem, an engineer ships a fix, and an experiment proves it worked against real traffic. The machines still run without watching. Raindrop just makes sure someone is.