The authors propose MedGuards, a medical safety guardrail framework designed to detect and correct errors in text generated by Large Language Models. This system treats error handling as a multi-agent in-context learning task where specialized agents separately perform detection, localization, and correction. A confidence-guided arbitration mechanism resolves disagreements among agents using reasoning traces and confidence scores without requiring additional model training. The study introduces the Keyword-Prioritized Correction Score (KPCS), a new metric that evaluates the accuracy of critical keywords within reference text. Experiments conducted across four multilingual medical datasets of clinical notes demonstrate significant improvements in performance metrics. These results highlight enhanced interpretability, robustness, and adaptability for safer LLM deployment in healthcare. The code for the MedErrBench benchmark is publicly available on GitHub.