Google’s new framework helps AI agents spend their compute and tool budget more wisely

In a new paper that studies tool-use in large language model (LLM) agents, researchers at Google and UC Santa Barbara have developed a framework that enables agents to make more efficient use of tool and compute budgets. The researchers introduce two new techniques: a simple "Budget Tracker" and a more comprehensive framework called "Budget Aware Test-time Scaling." These techniques make agents explicitly aware of their remaining reasoning and tool-use allowance.

As AI agents rely on tool calls to work in the real world, test-time scaling has become less about smarter models and more about controlling cost and latency.

For enterprise leaders and developers, budget-aware scaling techniques offer a practical path to deploying effective AI agents without facing unpredictable costs or diminishing returns on compute spend.

The challenge of scaling tool use

Traditional test-time scaling focuses on letting models "think" longer. However, for agentic tasks like web browsing, the number of ...