# Laminar Launch: RL Model Finds Agent Errors

> RL-trained model detects agent trace errors 23x cheaper than GPT-6-sol.
- Title: Laminar Launch: RL Model Finds Agent Errors · Hacker News
- Summary: RL-trained model detects agent trace errors 23x cheaper than GPT-6-sol. Laminar's flow-1 model finds errors in AI agent traces 23x cheaper than GPT-6-sol.
- Keywords: laminar, flow-1, rl-trained, agent traces, error detection, ai observability, gpt-6-sol, 23x cheaper, signals harness, trace compression, agent observability, technology
- Source: Hacker News — https://news.ycombinator.com/item?id=50154029
- Read time: 1 min
- Topics: laminar, flow-1, rl-trained, agent traces, error detection, ai observability, gpt-6-sol, 23x cheaper, signals harness, trace compression, agent observability, technology
## Laminar Launch
Robert and Din from Laminar released flow-1, an rl-trained model that finds errors in agent traces.
## Benchmark Results
flow-1 matches gpt-6-sol in detection quality while being 23x cheaper.
## Agent Error Types
Agents click wrong buttons or cite wrong documents while every call completes successfully.
## Current Solutions
Most agent observability platforms are data collectors that store traces nobody reads.
## Why RL Works
Using rl on thousands of synthetic investigations of realistic agent workflows improves error detection.
## Signals Harness
flow-1 runs inside a harness called signals that treats agent traces like a repo.
## Signal Events
Signals returns findings as json in a schema users can define for their needs.
## Error Clustering
Similar signal events are grouped into clusters to find recurring problems.
## Fixing Errors
Users can build eval datasets from clusters and hillclimb fixes before shipping.
## Trace Compression
Laminar uses 20x trace compression to reduce storage costs.
## Open Source
Laminar is apache 2.0 and self-hostable with github repository at lmnr-ai/lmnr.
## Managed Cloud
Laminar makes money from managed cloud services for agent observability.
## User Questions
How much do users learn from user feedback versus their own monitoring?
## Real-World Use
Users can see how fixes work across clusters before shipping to production.
## Key Challenges
False positives occur when models misattribute causes of errors.
## Next Steps
Laminar aims to improve flow-1's efficiency by reducing thinking tokens.
## Key takeaway

Laminar's flow-1 model finds errors in AI agent traces 23x cheaper than GPT-6-sol.