Ben Hylak is co-founder and CTO of Raindrop, which builds tools for finding and understanding failures in AI agents. His earlier work includes human-interface design at Apple and avionics internships at SpaceX. He writes about agent behavior, computer use, and product design.[1][2]
Start with Introducing Change Reports →
X Background
WPI profiled Hylak in 2016 as a robotics and computer-science student. His early work included a low-cost telepresence robot intended to help people visit remotely. The university account also describes an avionics placement at SpaceX. His personal site lists two SpaceX internships and later human-interface design work at Apple, connecting hardware interaction with his current interest in agent products.[1][4]
Contributions
Finding failures after deployment
Raindrop collects agent traces and identifies failures that ordinary error monitoring can miss, including incorrect tool use and incomplete responses. Its workflow connects issue detection, investigation, and simulations so a team can inspect a failure and test a proposed change. Hylak’s company work focuses on agent behavior across runs, where a successful API request alone may say little about whether the user’s task was completed.[5]
Following a change into production
In his announcement of Change Reports, Hylak describes comparing an agent’s behavior before and after a deployment. Connecting a repository lets Raindrop track changes such as new tools, model switches, and prompt updates. It then reports relevant successes and potential problems in Slack. His example is a fix intended to prevent empty responses that still left two production runs without text, showing why a merged fix needs behavioral follow-up.[2]
Ideas
Hylak argues that computer use matters because so much valuable work still happens through graphical interfaces. In his essay on OpenAI’s computer-use capabilities, he connects that access to software verification and personal assistance: an agent can test an application or operate a service even when a suitable API is unavailable. His argument emphasizes giving agents practical access to the environments where work happens.[3]
Tools, writing & talks
Writing, research & talks
Start here
Introducing Change Reportsraindrop.aiA product announcement explaining how Raindrop compares agent behavior around production changes.
Read the short deployment example to understand what behavior monitoring adds to a passing test. No setup is needed; using the feature requires a Raindrop project and repository connection.
Author and Raindrop CTO; team-built product.[2]A panel of heavy AI users reports what works after OpenAI DevDay: agent loops and forks, subagents, context management, model speed and quality, local models, prompt injection risk, payments, and delegation.
Catalog workOriginal sourceRaindrop's Ben Hylak argues eval advice is stuck in the chatbot era: an agent has effectively infinite issues, so what matters is the gap between its ceiling and its floor, and two numbers per issue: when it started and what share of users it hits.
Catalog workOriginal source- OpenAI did it again. But what did they do?benhylak.substack.com
An essay about computer use as a bridge between model capability and practical tasks.
Author.[3]