This is super cool! From the article: Each LLM call incurs latency, cost, and token overhead. More subtly, it compounds context: every step includes not o

This is super cool! From the article: Each LLM call incurs latency, cost, and token overhead. More subtly, it compounds context: every step includes not only the original query, but intermediate outputs and scratchpad logic from earlier prompts. This creates a growing burden on both inference and model performance. I was working with agents over a year ago before the common workflows had really been set in stone. At that time we were heavily doctoring the context to give a very streamlined representation of what had occurred during a given run to the LLM. Is this not standard practice?