Precision oncology increasingly depends on circulating tumor DNA (ctDNA), genomic biomarkers, molecular assay performance, and laboratory-quality indicators; however, these information streams are commonly interpreted through separate analytical pipelines, limiting diagnostic consistency and clinical decision reliability. This study develops an Integrated Diagnostic Intelligence Architecture for Precision Oncology and introduces a novel Clinical Genomic Assay Reliability and Diagnostic Intelligence Network (CGARDI-Net) for unified interpretation of ctDNA genomic signals, biomarker profiles, assay-quality characteristics, and clinical laboratory performance indicators. The proposed architecture integrates variant allele frequency, somatic mutation signatures, copy-number alterations, methylation-associated biomarkers, fragmentomic features, tumor-informed genomic indicators, limit of detection, analytical sensitivity, analytical specificity, sequencing depth, background error rate, sample-quality indices, turnaround time, quality-control compliance, and laboratory reproducibility within a reliability-aware multimodal learning framework. CGARDI-Net combines attention-based multimodal feature fusion, assay-reliability weighting, uncertainty-aware probabilistic calibration, and clinical decision scoring to reduce the influence of technically unreliable molecular measurements on diagnostic predictions. Its performance is comparatively evaluated against logistic regression, support vector machines, random forest, XGBoost, multilayer perceptron, convolutional neural networks, long short-term memory networks, standard Transformer architectures, and conventional threshold-based ctDNA interpretation. Model assessment is structured around sensitivity, specificity, precision, F1-score, area under the receiver operating characteristic curve, area under the precision-recall curve, Brier score, calibration error, false-negative rate, and decision-curve net benefit. Comparative graphs include ROC curves, precision-recall curves, calibration plots, confusion matrices, reliability-performance plots, biomarker importance charts, assay-error sensitivity curves, and three-dimensional relationships among ctDNA signal strength, assay reliability, and diagnostic confidence. Ablation analysis further quantifies the contribution of genomic biomarkers, assay reliability parameters, and laboratory-quality variables to overall diagnostic performance. The proposed architecture is designed to outperform isolated genomic and conventional machine-learning approaches by integrating biological evidence with analytical validity and laboratory operational reliability rather than treating every molecular measurement as equally trustworthy. The resulting framework establishes a technically interpretable pathway toward laboratory-aware precision oncology decision support capable of improving diagnostic robustness, reducing uncertainty associated with low-abundance ctDNA signals, and supporting more reliable translation of molecular testing into individualized cancer surveillance and treatment decisions.