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Laminar’s RL-trained model finds agent-trace errors at 23 times lower cost

news.ycombinator.com 13 min read Editor score 8/10

Laminar says its flow-1 model matches GPT-6-sol on its trace-error benchmark while costing 23 times less, making full production-trace analysis more affordable. Teams evaluating agent observability should examine how the benchmark and cost comparison hold up.

What: • Laminar's flow-1 is an RL-trained model designed to find errors in AI-agent traces.
How: • It inspects trace spans through the Signals harness, returns findings as signal events, then groups similar events into clusters.
Why it matters: • The authors say its cost makes analyzing every production trace feasible, supporting failure discovery and eval datasets without sampling.

Signals analyzes agent traces, groups findings, and supports building evaluation datasets. Sketched from the article's own text.

Read on news.ycombinator.com → Hacker News · 27 comments

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