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?