Profile-Then-Reason: Bounded Semantic Complexity for Tool-Augmented Language Agents

Researchers introduce Profile-Then-Reason (PTR), a new framework for AI language agents that use external tools, which reduces computational overhead by pre-planning workflows rather than recomputing after each step. The approach limits language model calls to 2-3 times maximum and shows superior performance in 16 of 24 test configurations compared to reactive execution methods.

• →PTR framework reduces latency and error propagation in tool-augmented AI agents by synthesizing explicit workflows upfront.
• →The system limits language model calls to just 2-3 times maximum, significantly reducing computational costs compared to reactive methods.
• →Testing across six benchmarks and four language models showed PTR outperformed ReAct baseline in 16 of 24 configurations.
• →PTR proves particularly effective for retrieval-centered and decomposition-heavy tasks.
• →Reactive execution remains preferable when tasks require substantial online adaptation and dynamic decision-making.