// GLOSSARY -- AGENTIC AI

What is Agent Reflection?

1 min read Updated

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.

FREQUENTLY ASKED QUESTIONS

How does agent reflection work technically?
The agent generates an action, observes the result, then prompts itself (or a separate evaluator) to assess quality. The evaluation feeds back into the next action decision.
Does reflection slow agents down?
Yes — it adds inference calls. Use reflection selectively: after important decisions, on error recovery, or at defined checkpoints — not after every trivial action.
Can reflection catch financial mistakes?
Sometimes. An agent can catch logical errors in its reasoning. But reflection is still model-based — it can't reliably catch hallucinated data. External validation remains necessary.

FURTHER READING

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