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 useTraditional test-time scaling focuses on letting models “think” longer. However, for agentic tasks like web browsing, the number of tool calls directly determines the depth and breadth of exploration.This introduces significant operational overhead for businesses. “Tool calls such as webpage browsing results in more token consumption, increases the context length and introduces additional time latency,” Zifeng Wang and Tengxiao Liu, co-authors of the paper, told VentureBeat. “Tool calls themselves introduce additional API costs.”The researchers found that simply granting agents more test-time resources does not guarantee better performance. “In a deep research task, if the agent has no sense of budget, it often goes down blindly,” Wang and Liu explained. “It finds one somewhat related lead, then spends 10 or 20 tool calls digging into it, only to realize that the entire path was a dead end.”Optimizing resources with Budget TrackerTo evaluate how they can optimize tool-use budgets, the researchers first tried a lightweight approach called “Budget Tracker.” This module acts as a plug- …
