This week, we’re joined by Alexis Gauba, Co-Founder of Raindrop, an AI native observability platform built for agents in production.

Alexis breaks down why operating agents is fundamentally different from monitoring traditional software. As systems shift from deterministic code to probabilistic behavior, dashboards alone are not enough. Teams need to detect unknown issues, track signals like forgetting, and understand long agent trajectories across millions of AI events.

We discuss why agent observability has become essential over the past year, what makes agent infrastructure distinct from prior platform shifts, and when internal tooling stops scaling. Alexis also explains Raindrop’s approach to production monitoring, combining explicit signals with automated detection to help teams not just find issues, but fix them.

Episode chapters:

2:05 - Founding Raindrop
3:54 - Building with Close Friends
5:44 - What Raindrop Actually Does
7:45 - The Reliability Challenge of Agents
9:55 - Monitoring Agents at Scale
14:45 - Experiencing the Pain Firsthand
18:00 - The Rise of Agent Infrastructure
22:17 - Internal AI Use Cases
24:07 - Hiring for Initiative and Ownership
28:30 - The Power of Multitasking
32:06 - Quick Fire Round

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E67: Monitoring the Probabilistic Stack with Alexis Gauba (Raindrop)