Launch HN: Laminar (YC S24) – RL-trained model that finds errors in agent traces

Hi HN, we’re Robert and Din from Laminar ( https://laminar.sh ). 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 ( https://laminar.sh/blog/flow-1 ), 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: https://youtu.be/KJa3h6ZJNLQ We also put together a post with example traces and analyses from flow-1 and other models: https://laminar.sh/blog/flow-1-in-action 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. To find them, you need to understand what the agent was trying to do and inspect its LLM calls and tool results. Simply looking at the final agent output is not enough. Collecting traces alone do