Recent large language models (LLMs) have shown that increasing the amount of inference‑time reasoning can markedly improve performance on complex tasks. Most existing approaches still rely on fixed reasoning controls—such as preset token budgets, pre‑estimated difficulty, or activation‑space interventions—and are typically evaluated on isolated reasoning benchmarks rather than on full agentic workflows. These assumptions often break down in agentic AI systems because reasoning demands evolve dynamically during planning, tool use, memory retrieval, and agent‑to‑agent interaction. Consequently, reasoning can become excessive or insufficient, leading to wasted computation, higher latency, planning drift, tool overuse, or incomplete answers.
We argue that the key challenge for next‑generation agentic AI is not how much reasoning a model should perform, but how to allocate reasoning resources adaptively as task demands change. We define over‑reasoning and under‑reasoning as recurring failure modes of misallocated reasoning and evaluate them on the MATH‑500 and GAIA public validation benchmarks. Using metrics such as tool‑decision latency, token consumption, token‑limit exhaustion, and answer correctness, our experiments show that over‑reasoning cases incur higher computational cost without proportional accuracy gains, while under‑reasoning cases consistently produce incorrect or incomplete solutions. These findings motivate future work on mechanisms that can dynamically adjust reasoning effort to improve overall agentic AI performance.
Blogger's Review: The paper clearly highlights the limitations of fixed reasoning budgets in realistic agentic settings and backs the need for adaptive reasoning with solid empirical evidence. Incorporating dynamic planning or meta‑learning to predict reasoning needs could further advance interpretable and efficient agentic AI.