ROUTEAgents & automation
JEV Tool Call Optimization
Nanda@thepeekpoker
JEV acts as a complementary layer to optimize tool calls by handling simple calls directly and pre-processing complex calls to reduce latency and cost.
TakeIf they require parameters, JEV has already selected the tool and the other model fills them in; because part of the decision is already solved, this also takes less time.
I implemented JEV @typesafeai yesterday and can say it is a new complementary layer. One example:
Before, an X%-scored small model handled the tool calls, taking 500–1700 ms on average.
Now the tool calls go through JEV first. If they have no parameters, it is a direct win in speed and cost (50–500 ms). If they require parameters, JEV has already selected the tool and the other model fills them in; because part of the decision is already solved, this also takes less time. So it is a win. You can also have JEV set tool-call parameters when they are booleans or enums.