AI has dramatically reduced the cost of creating software. Today, agents can generate features, infrastructure changes, tests, and pull requests in minutes. What AI has not reduced is the cost of proving that software actually works. As software creation accelerates, validation becomes the new bottleneck. Every AI-generated change creates validation debt that must be paid before we can trust it in production. At Checkly, we began treating validation as a first-class artifact. Instead of generating only code, our agents generate executable specifications: unit tests, integration tests, synthetic monitoring, and production validation journeys. In this talk, we’ll explore how we use AI to generate testing and monitoring from the same understanding of a system, how this helps bridge the validation gap between development and production, and how we took our validation to the next level.
Daniel Paulus is SVP of Product and Engineering at Checkly, the synthetic monitoring platform built for any scale—trusted by teams from Indie Hackers to LinkedIn and Citi. He leads high-performing engineering teams, scales resilient systems, and stays hands-on with debugging and developer experience. Based near Berlin with his family, Daniel enjoys sharing practical lessons on shipping fast without breaking trust.
