Why you shouldn't use Jev for coding agents and routing

Jev offers a fast, low-cost way to return structured classification decisions with probabilities, making it potentially useful for routing coding-agent requests among models, but experience from Weave suggests that classifier architecture matters far less than the session state supplied to it. After testing prompt clustering, skill and turn taxonomies, escalation methods, and several classifier designs, the team found that features describing an agent’s prior actions dramatically improved routing accuracy, while prompt embeddings provided only marginal additional value. Their current router predicts the agent’s next action using tool-call history, errors, intent, harness type, and uncertainty estimates about the hidden phase of the session, reflecting the view that a call’s difficulty depends largely on its trajectory rather than its text alone. Jev could therefore reduce the cost and latency of serving an already state-aware classifier, but it cannot create the crucial state representation, so teams building routers should prioritize logging complete agent trajectories, tool activity and errors, session depth, available tools, harness information, and downstream outcomes.

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