Launch HN: Laminar (YC S24) – We trained a model to catch silent agent failures
Laminar has introduced 'flow-1', a specialized model trained via reinforcement learning to detect silent failures in AI agent traces. The tool aims to provide affordable, scalable observability by analyzing agent workflows rather than just final outputs.
Why it matters
As AI agents become more complex, identifying subtle logic errors that don't trigger traditional crashes is critical for reliable production deployment.
Hi HN, we’re Robert and Din from Laminar ( ). We’ve been building open-source observability for AI agents since 2024. Recently, we released flow-1, a model trained with RL to find errors in agent traces. On our benchmark ( ), it matches GPT-6-sol in detection quality while being 23x cheaper. This makes it affordable to analyze every production agent trace, without sampling. Here’s a one-minute walkthrough: We also put together a post with example traces and analyses from flow-1 and other models: Agent observability is crowded, so instead of a bullet list of how Laminar differs from X, we’ll explain how we’re solving agent observability from first principles. Agents now run for many steps and fail implicitly. An agent can click the wrong button or cite the wrong document while every call completes successfully. These errors are the most useful ones to catch and understand.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in