The importance of reflection in agents
Reflection in LLM agents refers to their ability to examine their own thought processes, evaluate their actions, and adjust their approach. Like a person who might think, “that didn’t work well, let me try a different way”, an LLM agent can analyze its own outputs, recognize when its strategies aren’t effective, and modify its behavior accordingly. Here are some examples:
- An LLM agent might reflect on a failed attempt to solve a math problem and choose a different solution method
- It could recognize when its response wasn’t helpful to a user and adjust its communication style
- It might evaluate whether it has enough information to complete a task and request more details if needed
This self-monitoring and adaptation makes agents more effective than simple input-output systems, since they can learn from their successes and failures. This crucial capability has been recognized as vital for...
