Iterative Experience Refinement offers a promising framework for enhancing the capabilities of software-developing LLM agents. This research presents two patterns of experience refinement: successive and cumulative. The framework is augmented with a heuristic that eliminates less beneficial experiences, drastically increasing operational efficiency.

• Introduction of a dynamic framework for refining agent experiences during task execution.
• Demonstration of enhanced efficiency and error reduction in software development tasks.
• Comparison of successive vs. cumulative experience patterns.
• Significant reduction in necessary experience retention, maintaining performance with only 11.54% of data.

The paper provides valuable insights into the adaptability of LLM agents in practical applications. Further exploration is needed on optimizing experience refinement processes to maximize the potential of these agents.