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How LLM Reflection Enhances AI Agent Quality and Reliability

As AI agents move from simple chat interfaces to autonomous systems that plan, act, and decide, a critical limitation becomes clear: single-pass generation is not enough. Many failures in AI agents stem not from lack of capability, but from lack of self-evaluation. This is where LLM reflection plays a defining role. By enabling agents to critique, evaluate, and refine their own outputs, reflection transforms agents from fast responders into more reliable decision-makers.