Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a reinforcement fine-tuning pipeline to fine tune an agent machine learning (ML) model utilizing user-defined grader endpoints and/or external tools with a multi-layer security architecture. For example, the disclosed systems can utilize a multi-layer security architecture to filter incoming training datasets, perform content refusal checks, chain-of-thought leak detections, and/or governance oversights prior to training the agent ML model, during active training of the agent ML model using external tools and/or grader models, and/or during post-training of the agent ML model (prior to releasing a fine-tuned snapshot of the model).