Following the data-driven methods of evaluation and error analysis in meaning representation parsing presented in (Buljan et al., 2022), we performed an error exploration of an Abstract Meaning Representation (AMR) parser. Our aim is to perform a diagnosis of the types of errors found in the output of the tool in order to implement adaptation and correction strategies to accommodate these errors. This article presents the exploration, its results, the strategies we implemented, and the effect of these strategies on the performances of the tool. Though we did not observe a significative rise on average in the performances of the tool, we got much better results in some cases using our adaptation techniques.