Jev cuts agent per-step decision cost from $0.48 to $0.00014
Opus 5.5 only writes code while Jev picks paths from a typed menu and deterministic code does final verification, cutting 1,000 decisions from $480 to $0.14 with 180ms median latency.
running raw claude opus 5.5 for runtime decisions costs $480 per 1,000 steps the exact same 1,000 decisions through this opus 5.5 + jev harness cost $0.14 the stack that completely changes ai agent economics in 2026: opus writes the code. jev picks the path. deterministic code keeps the final say. here is the exact 6-step loop running under the hood: → propose - opus 5.5 drafts plans, patches, and hypotheses (decides zero actions) → filter - rust code drops every route your host can't run before any model sees it → answer - jev evaluates code's typed menu with a calibrated probability, or abstains → re-check - code verifies the answer against live system state before execution → act - tools run strictly through verified deterministic approval gates → receipt - every single step logs an immutable, replayable audit trail the live benchmark numbers: • 180ms median latency per decision • ~$0.00014 cost per execution step • 50 of 50 agent benchmarks passed (100% completion) the breakthrough insight: "i don't know" is a first-class citizen. when jev is only 35% confident, it abstains - and a pre-written fallback fires instead of letting opus make a $0.48 hallucinated guess. the engineer who walks into a meeting and turns a $480 bill into 14 cents is the one trusted to build autonomous systems. save this architecture for your next production pipeline.
This post quotes another post: “Jev turns routing, scoring and verification into a decision layer”