Large language model agents often expend unnecessary effort on simple tasks due to their inability to gauge the required complexity. This leads to inefficient execution, as they re-examine already seen files and dependencies, transforming a straightforward edit into an exhaustive code review. Researchers argue that the key to improving this process lies in developing task-aware execution scope estimation, enabling AI agents to adjust their approach according to the task's complexity1. This capability would allow AI agents to optimize their workflow, streamlining simple tasks and allocating more resources to complex ones. The absence of this feature can have significant implications for various domains, including cybersecurity and informatics. As AI integration continues to grow, the development of complexity-aware reasoning and execution is crucial for maximizing efficiency and minimizing unnecessary resource expenditure, making it essential for practitioners to prioritize this aspect in their AI implementation strategies.