Robust detection of AI-generated source code across programming languages remains challenging due to language-specific cues and train–test distribution shifts. We present LAFED (Language-Agnostic Feature Engineering Detector), a feature-engineering approach trained on {Python, Java, C++} and evaluated on a multilingual test set that includes unseen languages {C, C#, Go, JavaScript, PHP}. LAFED combines (i) structural skeletal features (indentation, control-flow density, and approximations of McCabe/Halstead complexity), (ii) character and whitespace statistics inspired by stylometry, and (iii) micro-style patterns (operator spacing, blank lines, indentation consistency). Using XGBoost (Chen and Guestrin, 2016) with Optuna hyperparameter search (Akiba et al., 2019), our best model achieves macro-F1=0.7570 on a 1,000-sample test set; the official submission obtains macro-F1=0.75209 (5th place in Subtask A). Per-language analysis shows strong transfer to C# (0.7753) and JavaScript (0.7683), but weaker performance on Go (0.6400) and PHP (0.5238).