From Agent Failures to Text Policies: What Works and What Breaks

Researchers have introduced TextGrad, a method that uses natural language feedback to optimize language model outputs without the need to adjust underlying model weights. While the approach is effective for refining static text, early testing indicates that implementing this feedback loop for autonomous agents remains a significant technical challenge.