As AI agents grow, vendors carve out decision-making as a separate model layer

As enterprises struggle to balance AI budgets with the demands of scaling agentic applications, they have been increasingly looking for ways to make those systems more efficient, from hard-coding deterministic decisions to using smaller models for specific tasks. TypeSafe’s Jev , released last month, introduced another option: using a specialized model to handle the bounded decisions that sit between an agent’s reasoning and its actions, potentially reducing token usage and inference costs.