Agentic AI from First Principles: Reflection

Agentic AI systems can be improved using the reflection design pattern, which creates feedback loops for LLMs to review and enhance their own outputs. This process mimics human workflows of drafting and refining work, and research shows it significantly boosts performance on tasks like coding and question-answering. While popular frameworks like ReAct and DSPy incorporate this concept, the content demonstrates how to build it from scratch to gain a deeper understanding. A practical example walks through creating a text-to-SQL agent in Python, showing how to implement a critique-and-refine cycle to generate more accurate SQL queries.