What is Reflection?
Reflection is an agent capability that involves analyzing past experiences, actions, or observations to extract higher-level insights, patterns, or lessons that can inform future behavior. Rather than just storing raw experiences, reflective agents periodically review their memories to synthesize understanding, identify patterns, recognize mistakes, or formulate principles. This meta-cognitive process enables learning and improvement beyond simple experience accumulation.
The reflection process typically involves retrieving relevant past experiences, analyzing them to identify patterns or insights, generating abstract observations or rules based on the analysis, and storing these reflections as new memories that can guide future decisions. For example, an agent might reflect on multiple failed attempts at a task to identify common factors in the failures, or synthesize multiple interactions with a user to understand their preferences. These reflections become part of the agent's memory, often with higher importance scores than individual observations.
Reflection is a key component in sophisticated agent architectures like Generative Agents, where periodic reflection enables agents to develop coherent personalities and long-term behavioral patterns. It provides a mechanism for agents to learn from experience without additional training, distill actionable insights from accumulated observations, and develop increasingly sophisticated understanding over time. Implementing effective reflection requires carefully designed prompts that guide the LLM to generate useful insights and strategies for determining when and what to reflect upon.