What is Agent Reflection?
Agent reflection is the capability of an AI agent to evaluate its own outputs, reasoning, and past actions — identifying errors, adjusting strategies, and improving decision quality over time.
WHY IT MATTERS
Reflection turns a reactive agent into a self-improving one. Instead of blindly executing actions, a reflective agent pauses to evaluate: Was my last action successful? Did my reasoning contain errors? Should I try a different approach?
Common reflection patterns include self-critique (asking the LLM to evaluate its own output), verification loops (checking results against expectations), and experience replay (learning from past successes and failures).
For financial agents, reflection is valuable for strategy adjustment — recognizing when a trading approach isn't working, identifying patterns in failed transactions, and adapting to changing market conditions.