A research project demonstrates a self-healing neural network architecture capable of correcting model drift in real time without full retraining or downtime. The system utilizes a frozen backbone network paired with a trainable adapter component called the ReflexiveLayer, which sits between the backbone and the output head. In a simulated fraud detection scenario, this approach recovered 27.8 percentage points of accuracy when the underlying data distribution shifted, moving performance from a collapsed 44.6 percent back to 72.4 percent. The core problem addressed is the lag between detecting model degradation and obtaining labeled data for retraining. Traditional solutions like rolling back to previous checkpoints are ineffective because they restore weights trained on a distribution that no longer exists. Similarly, waiting for new labels can leave systems vulnerable for hours. The proposed architecture solves this by isolating adaptation to a small, asynchronous component that updates via background threads, ensuring inference never pauses. The system relies on two primary signals to trigger healing. The first is FIDI, a feature-based distribution inspection that monitors statistical shifts in key input features, such as a z-score deviation in a specific transaction feature. The second is a symbolic rule engine that flags conflicts between the neural network's predictions and established domain logic. When these signals indicate drift, the ReflexiveLayer adjusts its parameters using a composite loss function that combines real data loss, consistency loss against symbolic rules, and entropy minimization. This neuro-symbolic approach prevents the adapter from overfitting to noisy data while anchoring it to known domain facts. While the method significantly improves accuracy and precision, it introduces a necessary tradeoff in recall. In the experiment, the healed model reduced false positives drastically but missed more fraud cases compared to a non-healing drifted model. The non-healing model flagged half the transactions as fraudulent, creating an unmanageable operational burden despite a high recall of 0.85. The healed model reduced false alarms to 177 out of 1,000 transactions, improving the overall reliability of the system. This tradeoff highlights that the correct decision depends on the specific cost structure of the deployment, particularly the relative expense of blocking legitimate transactions versus missing fraud. Safety mechanisms include a model registry that snapshots weights before and after every healing cycle. If the healing process degrades performance beyond a set threshold, the system can roll back to the best-performing post-heal snapshot, rather than reverting to outdated clean data. The implementation is fully reproducible using a synthetic dataset generated within the code, and all artifacts including monitoring exports and plots are available on GitHub. The project proves that targeted adaptation of small architectural components can bridge the gap between drift detection and full retraining, offering a practical path to resilient production AI systems.