Raindrop is building the safety layer for AI agents, detecting and preventing production failures like hallucinations and failed tool calls. The company addresses the paradox that as agents become more capable, they introduce greater complexity and more ways for things to go wrong. Their new Raindrop Simulations product catches critical failures in CI before they ever reach production, democratizing a capability previously limited to large AI labs.
Roberto Milev and Uday Kanagala from Navan discuss the challenges and emerging best practices in building and operating AI agents, drawing parallels with microservices. They highlight the shift to stateful runtimes, innovative memory and context management using 'skills,' and crucial solutions for observability and testing non-deterministic systems. A central theme is the critical need for robust authorization and guardrails, given the blurring line of agentic action, and a preference for mastering single-agent loops before scaling to complex orchestrations. They conclude by outlining the solved and unsolved problems, with cost and debugging remaining significant hurdles.