In a landscape where AI agents are becoming autonomous operators rather than passive tools, Raindrop has secured $15 million in seed financing, a raise that signals one thing clearly: observability for AI agents is no longer optional—it is a new infrastructure layer. The round was led by Lightspeed Venture Partners, with participation from several frontier AI companies and founders shaping the current generation of AI tooling.
Founded by Ben Hylak, Alexis Gauba, and Zubin Koticha, CEO of Raindrop, the company emerged from a problem the team faced firsthand: AI agents were becoming more capable, yet failures were becoming harder to detect. Public incidents—from chatbots mishandling medical information to incorrect airline reimbursements—only underscored the stakes.
“Traditional testing methods aren’t designed for long, autonomous agent trajectories,” said Koticha. “Engineering teams are operating in the dark. Raindrop is the first platform that monitors complex agent behavior at scale.”
The missing layer in AI engineering
Legacy monitoring tools track latency or token usage—metrics that barely touch the underlying failure patterns hidden in multi-step reasoning or toolchain orchestration. Raindrop’s thesis is that AI agents require a different kind of lens: custom adaptive models that examine behavioral signals across millions of events.
This approach allows engineering teams to define issues with real specificity—from “agent stuck in loop” to “UI aesthetic complaints.” When such anomalies spike, Raindrop issues Sentry-style alerts, enabling rapid triage. The platform’s background AI agents then analyze failure patterns and summarize what went wrong, step by step.
Lightspeed’s Bucky Moore framed the investment with unusual clarity: “They staked an early claim that monitoring would be the most critical part of building reliable agents—and they were right.”
A fast-growing customer base—and a problem that isn’t going away
Today, Raindrop works with AI organizations processing millions of events daily. Many use Raindrop Experiments—an A/B testing framework for AI agents—to verify whether changes to tools, models, or pipelines actually fix the observed issues.
Evan Goldschmidt, CTO of Tolan, described the platform’s impact: “It’s like seeing an iOS crash report in Sentry—but for our AI capabilities.”
The founding DNA behind the platform
The founding team reflects a blend of applied AI research and consumer-scale product thinking. Koticha and Gauba are second-time founders whose previous company was acquired by Coinbase, while Hylak spent four years on Apple’s Human Interface Design team—experience that shaped Raindrop’s emphasis on interpretability and signal design.
Editorial Perspective: Why this raise matters for the future of AI infrastructure
The AI ecosystem has moved from model-building to agent-building, and that shift has exposed an uncomfortable truth: agents can fail in ways that are invisible, silent, and costly. The industry’s sprint toward autonomy has outpaced the development of diagnostics. What logging was to cloud systems, monitoring will become for AI agents.
This is why Raindrop’s positioning is significant. The company isn’t trying to replace evals or improve embeddings—it is building the missing observability fabric for AI workflows, an area that will grow in relevance as agents integrate with financial systems, medical workflows, and enterprise operations.
Second, the caliber of participants—from Figma Ventures to founders of Replit, Notion, Framer, and Cognition—suggests alignment from builders who encounter agent failures daily. These are not speculative investors; they are operators who see the problem up close. Their participation indicates the problem’s urgency rather than market hype.
Lastly, the nature of failures emerging in the AI ecosystem—multi-step hallucinations, tool misuse, silent reasoning breakdowns—requires a type of monitoring that did not exist before. Raindrop’s raise reflects a broader industry shift: reliability, not model size, will define the next competitive frontier for AI products.
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