Five places Jev's decision layer fits into an engineering team's agent workflow
The author lists five uses for Jev inside the agent loop: tool routing, context pruning, model escalation, termination checks and test failure triage.
Several ways I see Jev improving parts of an engineering team's workflow: 1. decreased time + cost in tool routing letting an agent decide between some X amount of tools can overkill reasoning; Jev's parallel decisioning removes the majority of cost + time associated with this 2. context pruning coding agents accumulate a dumb amount of context from searches, file reads, diffs, etc. so deciding what context matters before the next model call is a perfect use for a cheap decision model 3. model escalation reducing the amount of deep reasoning in agent traces. Jev can become its own layer deciding when a state is ambiguous enough to justify escalating to a more expensive model 4. termination a surprising amount of agent cost comes from continuing where it optimally could have stopped. repeatedly evaluating a state like "does the current repo state satisfy X" is a natural bounded decision for Jev 5. test + failure triage this is an interesting one for agent observability. after running tests, deciding whether a failure is related to the current change, pre-exiting, worth-investing further, etc. may not need more full reasoning loops. a serious moat now is likely to come from how well you & your team integrates decisioning like Jev into the right parts of the agent loop.