Budget-Aware Tool-Use Enables Effective Agent Scaling

Key points

• Standard tool-augmented LLM agents lack intrinsic budget awareness, so they use resources inefficiently and quickly hit performance limits even when the tool-use budget increases.
• There was no systematic study or formal cost metric for jointly evaluating LLM token consumption and the economic cost of external tool interactions during agent test-time scaling.
• Agents often explore too shallowly or stop early without strategically using the extra resources they have available, which leads to suboptimal results and wasted compute.
• The authors developed Budget Tracker, a lightweight plug-and-play module that provides agents with real-time, continuous budget awareness through prompt-level policy guidance and state updates.
• They introduced BATS, or Budget Aware Test-time Scaling, a training-free framework that dynamically adjusts planning, including constraint decomposition and structured dynamic planning, and self-verification strategies using continuous budget awareness.
• They formalized a unified cost metric that comprehensively covers both internal LLM token consumption and the economic cost of external tool interactions, making scaling efficiency easier to evaluate transparently.

Paper links

External research summaries. These are not HDATF publications or measured product results.