The quality of AI agent outputs is directly proportional to the number of tokens invested, according to a recent analysis. The report highlights that increasing token investment enhances the agent's ability to perform complex tasks, such as coding, by reducing errors and improving decision-making processes. This correlation is evident in experiments where agents tasked with complex programming challenges showed fewer bugs as token investment increased. The analysis also notes that while more tokens can resolve issues related to complexity and scale, they cannot address problems of novelty. Agents struggle with tasks that require knowledge beyond their training data, emphasizing the need for human guidance in novel scenarios. The report suggests practical methods to improve agent performance, such as iterative reviews and verification checkpoints, to maximize the effectiveness of token investment.