A new framework called A-CEGIS has been developed to help artificial intelligence agents self-correct by using counterexamples as feedback, addressing limitations in current single-turn metrics which fail to assess the agents' ability to repair mistakes. This matters because it enhances the reliability and adaptability of AI systems in real-world applications where initial errors need correction.