AI Agent Outputs Garbage? The Problem Is You're Not Willing to Burn Enough Tokens
The core argument is that the quality of an AI Agent's output is directly proportional to the number of tokens invested in the process. More tokens lead to fewer errors, as they allow for deeper reasoning, multiple independent attempts, self-critique from fresh contexts, and verification through testing. This approach can solve problems of scale and complexity but fails when facing novel problems not present in the model's training data. For such novel challenges, human domain knowledge and guidance are essential. Two practical, immediate solutions are proposed: implementing an automatic review cycle (WAIT) for the Agent to repeatedly critique and fix its work, and establishing frequent verification checkpoints (VERIFY) where a separate Agent validates outputs to catch errors early. The key takeaway is that insufficient token investment is often the primary reason for poor Agent performance, not the underlying framework.
marsbit03/23 06:14
