Reflection is a strategy used to enhance the performance of AI agents by prompting them to critique their previous actions, aiming to shift from instinctive to more methodical thinking patterns. This approach, though computationally intensive and not suitable for low-latency applications, is beneficial for knowledge-intensive tasks where quality is prioritized over speed. Basic reflection involves a loop where a generator produces responses, and a reflector critiques them to refine output quality, though it lacks grounding in external processes. Reflexion, developed by Shinn et al., incorporates verbal feedback and self-reflection, grounding criticism in external data to improve response quality, though it follows a fixed trajectory that may propagate errors. Language Agent Tree Search (LATS), by Zhou et al., enhances task performance by combining reflection, evaluation, and Monte-Carlo tree search to avoid repetitive loops and improve problem-solving. These techniques, while requiring additional inference time, aim at generating higher quality outputs and adapting models to avoid recurring mistakes, with potential applications in complex tasks such as code generation.