Adaptive Reasoning Crucial for Next-Gen Agentic AI Systems

Recent advancements in Large Language Models (LLMs) have shown that increasing inference-time reasoning can enhance performance on complex tasks. However, many current approaches rely on fixed reasoning controls, such as predetermined token budgets or pre-execution difficulty estimates. These methods are often evaluated on isolated reasoning benchmarks rather than full agentic workflows, where reasoning requirements change dynamically through planning, tool use, and interactions. Such fixed reasoning allocations can lead to either excessive or insufficient reasoning, resulting in wasted computation, increased latency, planning deviations, overuse of tools, or incomplete solutions. The authors contend that a key challenge for future agentic AI is not just how much reasoning an LLM should perform, but how it should adaptively allocate that reasoning according to the task's evolving demands. The paper characterizes "over-reasoning" and "under-reasoning" as common failure modes stemming from misallocated reasoning. These were evaluated on MATH-500 and the GAIA public validation benchmark. Results indicate that over-reasoning incurs higher computational costs without proportional accuracy gains, while under-reasoning consistently leads to incorrect or incomplete solutions. These findings highlight the critical need for adaptive reasoning mechanisms in agentic AI.