Laminar releases flow-1, an RL-trained model for detecting silent agent failures

·2026.10.20 01:00

Key point

The model matches GPT-6-sol in detection quality while being 23x cheaper, enabling affordable analysis of every production agent trace.

Laminar released flow-1, a model trained with reinforcement learning to identify implicit errors in AI agent traces where individual calls succeed but the overall task fails.
On Laminar's benchmark, flow-1 matches GPT-6-sol in detection quality at 23x lower cost, making it feasible to analyze 100% of production traces without sampling.
The model operates within a harness called Signals, which treats agent traces like code repositories, allowing it to grep spans and read relevant LLM or tool calls in full.
Laminar is open-source under Apache 2.0 and self-hostable, with revenue generated from a managed cloud service.

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