Beyond the LLM Judge: How Jev Decisions and Zero-Generation Evals Reclaim the Agent Loop

Openlayer-ai's jevals replaces autoregressive LLM judges with single-pass Jev decision models, compressing multi-metric agent evaluation into a single 300ms request that costs a fraction of a cent. The approach eliminates the seconds of latency and API credit spend required by generative judges that output binary evaluation labels.

Autoregressive LLM judges consume seconds of latency and dollars of API credits to output binary evaluation labels. By replacing generative text synthesis with single-pass Jev decision models, openlayer-ai/jevals compresses multi-metric agent evaluation into a single 300ms request costing a fraction of a cent.