Scoring agent context with Jev to cut tokens without rewriting
A demo uses Jev to assign each line a drop probability, keeping the original error text and removing irrelevant lines, cutting tokens from 156,000 to 62,000 without rewriting content.
YOUR AGENT FORGETS THE ERROR MESSAGE THE SECOND IT RUNS OUT OF CONTEXT. JEV KEEPS IT WORD FOR WORD the fix everyone ships is to ask a model to summarize the session. you wait, you pay for the tokens, and you get back a paraphrase of your own work. the exact failing line is gone. score every tool call → drop what's irrelevant → keep the rest verbatim jev scores each line with a drop probability instead. "read 212 lines" gets 0.97 and goes. the failing assertion, expected 1200 and received 120, gets 0.09 and stays exactly as it was written. 156,000 tokens down to 62,000. context from 78% to 31%. 10 lines kept verbatim, 6 removed, nothing rewritten in between. the takeaway is simple: summaries lose the detail you'll need in ten minutes. deletion doesn't. you keep the real line or you don't keep it at all.
This post quotes another post: “Ten-step guide to installing Jev from scratch”