🤖 GIVE AI AGENTS A FASTER BRANCHING LAYER
Autonomous agents continually face small but important choices:
Should this task be accepted?
Which tool should run next?
Does this action exceed the risk threshold?
Which workflow branch fits the current state?
Using a general-purpose generative model for every decision can introduce extra latency, cost, and output-parsing complexity.
Jev provides an alternative.
The first System One model from @typesafeai returns typed decisions with probability and confidence in approximately 70–500ms.
Its parallel support for Choice, Score, and Noul questions makes it a natural fit for agent branching and policy-based execution.
Jev does not generate explanatory text. It provides structured signals that another system or agent can act upon immediately.
This creates a useful division of labor: generative models handle creation and deep reasoning, while Jev manages fast operational decisions.
Now live on the B.AI API.
docs.b.ai/llmservice/mod…
@BAI_AGI
@justinsuntron
#TRONEcoStar
- 🤖 GIVE AI AGENTS A FASTER BRANCHING LAYER Autonomous agents continually face small but important choices: Should this task be accepted? Which tool should run next? Does this action exceed the risk threshold? Which workflow branch fits the current state? Using a general-purpose generative model for every decision can introduce extra latency, cost, and output-parsing complexity. Jev provides an alternative. The first System One model from @typesafeai returns typed decisions with probability and confidence in approximately 70–500ms. Its parallel support for Choice, Score, and Noul questions makes it a natural fit for agent branching and policy-based execution. Jev does not generate explanatory text. It provides structured signals that another system or agent can act upon immediately. This creates a useful division of labor: generative models handle creation and deep reasoning, while Jev manages fast operational decisions. Now live on the B.AI API. docs.b.ai/llmservice/mod… @BAI_AGI @justinsuntron #TRONEcoStar