Overview & result
Metaview's CTO says the company shipped Jev into its agents and decomposed their work into small semantic questions with team-defined thresholds. He reports that candidate searches in the sourcing product now take seconds instead of minutes, with similar accuracy and lower cost per search.
From the original post
at @MetaviewAI, over the weekend we shipped @typesafeai's jev into every agent on metaview.
candidate searches in our sourcing product that took minutes now take seconds. same accuracy, ~10x faster, meaningfully cheaper per search.
but the speedup isn't the interesting part. https://t.co/znTdUAkzO8
How Jev fits in the loop
- Break an agent workflow into smaller semantic questions, as described by Metaview's CTO.
- Ask Jev those bounded questions inside the sourcing workflow.
- Set application thresholds for the responses.
- Use the reported questions and thresholds as a way to revise agent behavior without relying only on a system prompt.
How to reproduce
- Read the Metaview CTO's original post and watch its before/after candidate-search video.
- Inspect the TypeSafe CEO's quote post for the original source attribution.
- To evaluate the reported change, compare the same candidate-search tasks before and after Jev using disclosed timing, accuracy criteria, and per-search cost.
Why this build matters
It is a first-hand report of Jev being used in a production recruiting product, with an original comparison video and a TypeSafe CEO quote post.
Reported performance
Reported by authorLatency: About 10x faster candidate searches, reported by Metaview's CTO
Limitations
- The production integration is not open source, so JevForAgents cannot inspect each agent's implementation or thresholds.
- The reported speed, accuracy, and cost comparison has no published task set or measurement method in the cited post.
- The before/after video and TypeSafe CEO's quote post support that this is a real first-hand account; they do not independently verify the performance figures.