Using Jev as a fuzzy linter after agent edits
The author breaks coding guidelines into small rules Jev can judge in a single fast call, validates them with a synthetic eval, and uses Jev's confidence scores to send medium-confidence violations to the agent for review, roughly doubling recall with only a small rise in false positives.
I’ve been experimenting a bit with Jev as a fuzzy linter that runs after edits in your agent harness: looks very promising so far in my evals. First I converted all of our coding guidelines to tiny rules that are very easy to judge. Then, only kept the ones that don’t require extra context/files/reasoning. I want it to be a single call, super fast, and Jev isn’t smart enough to handle complex or ambiguous rules. I then created a synthetic eval that has different kinds of tool calls to exercise and test every one of those rules. Also a held out set that we don’t train on so we don’t accidentally overfit (ie benchmax). One cool thing that seems to work well: Jev returns confidence, so in addition to high confidence violations, we can also return medium confidence and get the agent to double check. Adding that lower confidence tier seems to double what it catches, and only increases false positives a little. I still have yet to test this inside an actual agent to see how the agent reacts to this feedback. Hopefully next week I can get a decent eval going and see how well this works E2E.